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Elektrikli Araçlarda Batarya Yönetim Sistemleri Üzerine Bir Derleme Çalışması

Year 2021, Volume: 8 Issue: 2, 234 - 275, 28.06.2021

Abstract

Küresel ısınma, fosil yakıtların çevreye verdiği zararlar ve sera gazı emisyonları ile ilgili endişeler nedeniyle elektrikli araçlar gün geçtikçe içten yanmalı motorlu araçların yerini almaktadır. Elektrikli araçlar için ana enerji kaynağı olan bataryaların, sürüş güvenliği için belirli bir çalışma sağlamak adına bazı sınırlamaları vardır. Batarya yönetim sistemleri (BYS’ler), bu sınırlamaların üstesinden gelmek, bataryayı korumak ve elektrikli araç için daha güvenilir sürüş sağlamak adına önemli bir rol oynamaktadır. Bu makalede BYS ve BYS’nin alt konuları olan bataryayı izleme, batarya güvenliği, araç iç-dış haberleşmesi, hücre dengelenmesi, durum kestirimleri, termal yönetimi ve topolojileri alanındaki çalışmalar derlenmiştir. Bu tür konularla ilgili yöntemlerin, avantaj-dezavantajları ve nitel faktörler açısından karşılaştırmaları yapılmıştır. Elektrikli araçlar geleceğin ulaşım aracı olacağı ve ülkemizde yerli üretime geçildiği için, elektrikli araçlar konusunda Türkçe literatürünün geliştirilmesi ve akademik çalışmaların yapılması gerektiği yazarlar tarafından düşünülmektedir. Yazarlar, bu çalışmanın Türkçe literatürüne katkı sağlayacağını ve batarya yönetim sistemi alanında çalışan tasarımcılara, araştırmacılara, üreticilere ve şirketlere bakış açısı kazandıracağını düşünmektedir.

References

  • Adany, R., Aurbach, D., & Kraus, S. (2013). Switching algorithms for extending battery life in Electric Vehicles. Journal of Power Sources, 231, 50-59.doi:10.1016/j.jpowsour.2012.12.075
  • Aktaş, M., Baygüneş, B., Kıvrak, S., Çavuş, B., & Sözen, F. (2020). Elektrikli Araç İçin Düşük Maliyetli Bir Batarya Yönetim Sistemi Tasarımı ve Gerçekleştirilmesi. Avrupa Bilim ve Teknoloji Dergisi, Ejosat Special Issue 2020 (HORA), 227-238. doi:10.31590/ejosat.779720
  • Al-Hallaj, S., & Selman, J. R. (2002). Thermal modeling of secondary lithium batteries for electric vehicle/hybrid electric vehicle applications. Journal of Power Sources, 110(2), 341-348. doi:10.1016/S0378-7753(02)00196-9
  • Alvarez Anton, J. C., Garcia Nieto, P. J., Blanco Viejo, C., & Vilan Vilan, J. A. (2013). Support Vector Machines Used to Estimate the Battery State of Charge. IEEE Transactions on Power Electronics, 28(12), 5919-5926. doi:10.1109/TPEL.2013.2243918
  • Álvarez Antón, J. C., García Nieto, P. J., de Cos Juez, F. J., Sánchez Lasheras, F., González Vega, M., & Roqueñí Gutiérrez, M. N. (2013). Battery state-of-charge estimator using the SVM technique. Applied Mathematical Modelling, 37(9), 6244-6253. doi:10.1016/j.apm.2013.01.024
  • Amir, U., Tao, L., Zhang, X., Saeed, M., & Hussain, M. (2018). A Novel SOC Estimation Method for Lithium Ion Battery Based On Improved Adaptive PI Observer. 2018 IEEE International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles & International Transportation Electrification Conference (ESARS-ITEC), 1-5. doi:10.1109/ESARS-ITEC.2018.8607386
  • Andrea, D. (2010). Battery Management Systems for Large Lithium-Ion Battery Packs. Artech hause.
  • Ariantara, B., Putra, N., & Supriadi, S. (2018). Battery thermal management system using loop heat pipe with LTP copper capillary wick. IOP Conference Series: Earth and Environmental Science, 105(1), 012045. doi:10.1088/1755-1315/105/1/012045
  • Arora, S. (2018). Selection of thermal management system for modular battery packs of electric vehicles: A review of existing and emerging technologies. Journal of Power Sources, 400, 621-640. doi:10.1016/j.jpowsour.2018.08.020
  • Aydin, S., Gümüş, R., & Akçay, İ. H. (2013). Yakıt Pili ile Çalışan Elektrikli Bir Aracın Güç , Sıcaklık , Bağıl Nem ve Hızının Anlık Olarak İzlenmesi ve Kontrolü. Teknik Bilimler Dergisi, 3(2), 7-12.
  • Bahiraei, F., Fartaj, A., & Nazri, G. A. (2016). Numerical Investigation of Active and Passive Cooling Systems of a Lithium-Ion Battery Module for Electric Vehicles. SAE Technical Papers, 2016-01-0655. doi:10.4271/2016-01-0655
  • Balagopal, B., & Chow, M. Y. (2015). The state of the art approaches to estimate the state of health (SOH) and state of function (SOF) of lithium Ion batteries. Proceeding - 2015 IEEE International Conference on Industrial Informatics, INDIN 2015, 2, 1302-1307. doi:10.1109/INDIN.2015.7281923
  • Bandhauer, T. M., & Garimella, S. (2013). Passive, internal thermal management system for batteries using microscale liquid–vapor phase change. Applied Thermal Engineering, 61(2), 756-769. doi:10.1016/j.applthermaleng.2013.08.004
  • Barai, A., Uddin, K., Widanalage, W. D., McGordon, A., & Jennings, P. (2016). The effect of average cycling current on total energy of lithium-ion batteries for electric vehicles. Journal of Power Sources, 303, 81-85. doi:10.1016/j.jpowsour.2015.10.095
  • Barré, A., Deguilhem, B., Grolleau, S., Gérard, M., Suard, F., & Riu, D. (2013). A review on lithium-ion battery ageing mechanisms and estimations for automotive applications. Journal of Power Sources, 241, 680-689. doi:10.1016/j.jpowsour.2013.05.040
  • Berecibar, M., Gandiaga, I., Villarreal, I., Omar, N., Van Mierlo, J., & Van Den Bossche, P. (2016). Critical review of state of health estimation methods of Li-ion batteries for real applications. Renewable and Sustainable Energy Reviews, 56, 572-587. doi:10.1016/j.rser.2015.11.042
  • Campestrini, C., Heil, T., Kosch, S., & Jossen, A. (2016). A comparative study and review of different Kalman filters by applying an enhanced validation method. Journal of Energy Storage, 8, 142-159. doi:10.1016/j.est.2016.10.004
  • Cao, J., Schofield, N., & Emadi, A. (2008). Battery balancing methods: A comprehensive review. 2008 IEEE Vehicle Power and Propulsion Conference, VPPC 2008, 3-8. doi:10.1109/VPPC.2008.4677669
  • Carter, J., Fan, Z., & Cao, J. (2020). Cell equalisation circuits: A review. Journal of Power Sources, 448, 227489. doi:10.1016/j.jpowsour.2019.227489
  • Chan, C. C. (2013). The Rise & Fall of Electric Vehicles in 1828–1930: Lessons Learned [Scanning Our Past]. Proceedings of the IEEE, 101(1), 206-212. doi:10.1109/JPROC.2012.2228370
  • Chau, K. T., Wu, K. C., & Chan, C. C. (2004). A new battery capacity indicator for lithium-ion battery powered electric vehicles using adaptive neuro-fuzzy inference system. Energy Conversion and Management, 45(11-12), 1681-1692. doi:10.1016/j.enconman.2003.09.031
  • Chemali, E., Kollmeyer, P. J., Preindl, M., & Emadi, A. (2018). State-of-charge estimation of Li-ion batteries using deep neural networks: A machine learning approach. Journal of Power Sources, 400, 242-255. doi:10.1016/j.jpowsour.2018.06.104
  • Chen, L., Wang, Z., Lu, Z., Li, J., Ji, B., Wei, H., & Pan, H. (2018). A Novel State-of-Charge Estimation Method of Lithium-Ion Batteries Combining the Grey Model and Genetic Algorithms. IEEE Transactions on Power Electronics, 33(10), 8797-8807. doi:10.1109/TPEL.2017.2782721
  • Chen, X., Shen, W. X., Cao, Z., & Kapoor, A. (2012). Sliding Mode Observer for State of Charge Estimation Based on Battery Equivalent Circuit in Electric Vehicles. Australian Journal of Electrical and Electronics Engineering, 9(3), 225-234. doi:10.1080/1448837X.2012.11464327
  • Chen, Xiaopeng, Shen, W., Cao, Z., & Kapoor, A. (2014a). A novel approach for state of charge estimation based on adaptive switching gain sliding mode observer in electric vehicles. Journal of Power Sources, 246, 667-678. doi:10.1016/j.jpowsour.2013.08.039
  • Chen, Xiaopeng, Shen, W., Cao, Z., & Kapoor, A. (2014b). Adaptive gain sliding mode observer for state of charge estimation based on combined battery equivalent circuit model. Computers & Chemical Engineering, 64, 114-123. doi:10.1016/j.compchemeng.2014.02.015
  • Chen, Zewang, Yang, L., Zhao, X., Wang, Y., & He, Z. (2019). Online state of charge estimation of Li-ion battery based on an improved unscented Kalman filter approach. Applied Mathematical Modelling, 70, 532-544. doi:10.1016/j.apm.2019.01.031
  • Chen, Zheng, Fu, Y., & Mi, C. C. (2013). State of Charge Estimation of Lithium-Ion Batteries in Electric Drive Vehicles Using Extended Kalman Filtering. IEEE Transactions on Vehicular Technology, 62(3), 1020-1030. doi:10.1109/TVT.2012.2235474
  • Chen, Zonghai, Sun, H., Dong, G., Wei, J., & Wu, J. (2019). Particle filter-based state-of-charge estimation and remaining-dischargeable-time prediction method for lithium-ion batteries. Journal of Power Sources, 414, 158-166. doi:10.1016/j.jpowsour.2019.01.012
  • Choudhari, V. G., Dhoble, D. A. S., & Sathe, T. M. (2020). A review on effect of heat generation and various thermal management systems for lithium ion battery used for electric vehicle. Journal of Energy Storage, 32, 101729. doi:10.1016/j.est.2020.101729
  • Choudhury, J. R., Banerjee, T. P., Gurung, H., Bhattacharjee, A. K., & Das, S. (2009). Real time state of charge prediction using Kalman Filter. 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 1190-1194. doi:10.1109/NABIC.2009.5393786
  • Daowd, M., Omar, N., van den Bossche, P., & van Mierlo, J. (2012). Capacitor based battery balancing system. World Electric Vehicle Journal, 5(2), 385-393. doi:10.3390/wevj5020385
  • Daowd, M., Omar, N., Van Den Bossche, P., & Van Mierlo, J. (2011). Passive and active battery balancing comparison based on MATLAB simulation. 2011 IEEE Vehicle Power and Propulsion Conference, 1-7. doi:10.1109/VPPC.2011.6043010
  • Dong, G., Wei, J., & Chen, Z. (2016). Kalman filter for onboard state of charge estimation and peak power capability analysis of lithium-ion batteries. Journal of Power Sources, 328, 615-626. doi:10.1016/j.jpowsour.2016.08.065
  • Dong, G., Zhang, X., Zhang, C., & Chen, Z. (2015). A method for state of energy estimation of lithium-ion batteries based on neural network model. Energy, 90, 879-888. doi:10.1016/j.energy.2015.07.120
  • Drori, Y., & Martinez, C. (2005). The benefits of cell balancing. Application Note, Xicor Incorporated, 1-9. http://www.neue-verpackung.de/ai/resources/a03b3cf05d5.pdf
  • Du, J., Liu, Z., Wang, Y., & Wen, C. (2016). An adaptive sliding mode observer for lithium-ion battery state of charge and state of health estimation in electric vehicles. Control Engineering Practice, 54, 81-90. doi:10.1016/j.conengprac.2016.05.014
  • Duong, V.-H., Bastawrous, H. A., Lim, K., See, K. W., Zhang, P., & Dou, S. X. (2015). Online state of charge and model parameters estimation of the LiFePO4 battery in electric vehicles using multiple adaptive forgetting factors recursive least-squares. Journal of Power Sources, 296, 215-224. doi:10.1016/j.jpowsour.2015.07.041
  • Duraisamy, T., & Kaliyaperumal, D. (2020). Active cell balancing for electric vehicle battery management system. International Journal of Power Electronics and Drive Systems, 11(2), 571-579. doi:10.11591/ijpeds.v11.i2.pp571-579
  • Eddahech, A., Briat, O., & Vinassa, J. M. (2012). Adaptive voltage estimation for EV Li-ion cell based on artificial neural networks state-of-charge meter. 2012 IEEE International Symposium on Industrial Electronics, 1318-1324. doi:10.1109/ISIE.2012.6237281
  • Ekici, Y. E. (2019). Batarya yönetim sistemleri. Yüksek Lisans Tezi, İnönü Üniversitesi, Fen bilimleri Enstitüsü, Malatya, Türkiye.
  • Ekici, Y. E., & Tan, N. (2019). Charge and discharge characteristics of different types of batteries on a hybrid electric vehicle model and selection of suitable battery type for electric vehicles. International Journal of Automotive Science and Technology, 3(4), 62-70. doi:10.30939/ijastech..527971
  • Fang, Y., Xiong, R., & Wang, J. (2018). Estimation of Lithium-Ion Battery State of Charge for Electric Vehicles Based on Dual Extended Kalman Filter. Energy Procedia, 152, 574-579. doi:10.1016/j.egypro.2018.09.213
  • Gallardo-Lozano, J., Romero-Cadaval, E., Milanes-Montero, M. I., & Guerrero-Martinez, M. A. (2014). Battery equalization active methods. Journal of Power Sources, 246, 934-949. doi:10.1016/j.jpowsour.2013.08.026
  • Gao, M., Liu, Y., & He, Z. (2011). Battery state of charge online estimation based on particle filter. 2011 4th International Congress on Image and Signal Processing, 2233-2236. doi:10.1109/CISP.2011.6100603
  • Guenther, C., Schott, B., Hennings, W., Waldowski, P., & Danzer, M. A. (2013). Model-based investigation of electric vehicle battery aging by means of vehicle-to-grid scenario simulations. Journal of Power Sources, 239, 604-610. doi:10.1016/j.jpowsour.2013.02.041
  • Hallaj, S. Al, & Selman, J. R. (2000). A Novel Thermal Management System for Electric Vehicle Batteries Using Phase-Change Material. Journal of The Electrochemical Society, 147(9), 3231. doi:10.1149/1.1393888
  • Hametner, C., & Jakubek, S. (2013). State of charge estimation for Lithium Ion cells: Design of experiments, nonlinear identification and fuzzy observer design. Journal of Power Sources, 238, 413-421. doi:10.1016/j.jpowsour.2013.04.040
  • Han, X., Lu, L., Zheng, Y., Feng, X., Li, Z., Li, J., & Ouyang, M. (2019). A review on the key issues of the lithium ion battery degradation among the whole life cycle. eTransportation, 1, 100005. doi:10.1016/j.etran.2019.100005
  • Hannan, M. A., Lipu, M. S. H., Hussain, A., & Mohamed, A. (2017). A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations. Renewable and Sustainable Energy Reviews, 78, 834-854. doi:10.1016/j.rser.2017.05.001
  • Hansen, T., & Wang, C.-J. (2005). Support vector based battery state of charge estimator. Journal of Power Sources, 141(2), 351-358. doi:10.1016/j.jpowsour.2004.09.020
  • Hauser, A., & Kuhn, R. (2015). Cell balancing, battery state estimation, and safety aspects of battery management systems for electric vehicles. İçinde: B. Scrosati, J. Garche & W. Tillmetz (Eds.) Advances in Battery Technologies for Electric Vehicles (ss. 283-326). Elsevier. doi:10.1016/B978-1-78242-377-5.00012-1
  • He, H. W., Zhang, Y. Z., Xiong, R., & Wang, C. (2015). A novel Gaussian model based battery state estimation approach: State-of-Energy. Applied Energy, 151, 41-48. doi:10.1016/j.apenergy.2015.04.062
  • He, H., Xiong, R., & Peng, J. (2016). Real-time estimation of battery state-of-charge with unscented Kalman filter and RTOS μCOS-II platform. Applied Energy, 162, 1410-1418. doi:10.1016/j.apenergy.2015.01.120
  • He, H., Zhang, X., Xiong, R., Xu, Y., & Guo, H. (2012). Online model-based estimation of state-of-charge and open-circuit voltage of lithium-ion batteries in electric vehicles. Energy, 39(1), 310-318. doi:10.1016/j.energy.2012.01.009
  • He, W., Williard, N., Chen, C., & Pecht, M. (2013). State of charge estimation for electric vehicle batteries using unscented kalman filtering. Microelectronics Reliability, 53(6), 840-847. doi:10.1016/j.microrel.2012.11.010
  • He, W., Williard, N., Chen, C., & Pecht, M. (2014). State of charge estimation for Li-ion batteries using neural network modeling and unscented Kalman filter-based error cancellation. International Journal of Electrical Power & Energy Systems, 62, 783-791. doi:10.1016/j.ijepes.2014.04.059
  • Hoque, M. M., Hannan, M. A., Mohamed, A., & Ayob, A. (2017). Battery charge equalization controller in electric vehicle applications: A review. Renewable and Sustainable Energy Reviews, 75, 1363-1385. doi:10.1016/j.rser.2016.11.126
  • Hou, Z. Y., Lou, P. Y., & Wang, C. C. (2017). State of charge, state of health, and state of function monitoring for EV BMS. 2017 IEEE International Conference on Consumer Electronics, ICCE 2017, 1, 310-311. doi:10.1109/ICCE.2017.7889332
  • Hu, J. N., Hu, J. J., Lin, H. B., Li, X. P., Jiang, C. L., Qiu, X. H., & Li, W. S. (2014). State-of-charge estimation for battery management system using optimized support vector machine for regression. Journal of Power Sources, 269, 682-693. doi:10.1016/j.jpowsour.2014.07.016
  • Hu, L., Hu, X., Che, Y., Feng, F., Lin, X., & Zhang, Z. (2020). Reliable state of charge estimation of battery packs using fuzzy adaptive federated filtering. Applied Energy, 262, 114569. doi:10.1016/j.apenergy.2020.114569
  • Hu, X., Feng, F., Liu, K., Zhang, L., Xie, J., & Liu, B. (2019). State estimation for advanced battery management: Key challenges and future trends. Renewable and Sustainable Energy Reviews, 114, 109334. doi:10.1016/j.rser.2019.109334
  • Hu, X., Zheng, Y., Howey, D. A., Perez, H., Foley, A., & Pecht, M. (2020). Battery warm-up methodologies at subzero temperatures for automotive applications: Recent advances and perspectives. Progress in Energy and Combustion Science, 77, 100806. doi:10.1016/j.pecs.2019.100806
  • Hu Xiaosong, Sun Fengchun, Zou Yuan, & Peng Huei. (2011). Online estimation of an electric vehicle Lithium-Ion battery using recursive least squares with forgetting. Proceedings of the 2011 American Control Conference, 935-940. doi:10.1109/ACC.2011.5991260
  • Ianniciello, L., Biwolé, P. H., & Achard, P. (2018). Electric vehicles batteries thermal management systems employing phase change materials. Journal of Power Sources, 378, 383-403. doi:10.1016/j.jpowsour.2017.12.071
  • Jaguemont, J., Boulon, L., & Dubé, Y. (2016). A comprehensive review of lithium-ion batteries used in hybrid and electric vehicles at cold temperatures. Applied Energy, 164, 99-114. doi:10.1016/j.apenergy.2015.11.034
  • Jaguemont, Joris, & Van Mierlo, J. (2020). A comprehensive review of future thermal management systems for battery-electrified vehicles. Journal of Energy Storage, 31, 101551. doi:10.1016/j.est.2020.101551
  • Ji, Y., & Wang, C. Y. (2013). Heating strategies for Li-ion batteries operated from subzero temperatures. Electrochimica Acta, 107, 664-674. doi:10.1016/j.electacta.2013.03.147
  • Jiang, C., Taylor, A., Duan, C., & Bai, K. (2013). Extended Kalman Filter based battery state of charge (SOC) estimation for electric vehicles. 2013 IEEE Transportation Electrification Conference and Expo (ITEC), 1-5. doi:10.1109/ITEC.2013.6573477
  • Jiménez-Bermejo, D., Fraile-Ardanuy, J., Castaño-Solis, S., Merino, J., & Álvaro-Hermana, R. (2018). Using Dynamic Neural Networks for Battery State of Charge Estimation in Electric Vehicles. Procedia Computer Science, 130, 533-540. doi:10.1016/j.procs.2018.04.077
  • Juang, L. W., Kollmeyer, P. J., Jahns, T. M., & Lorenz, R. D. (2012). Implementation of online battery state-of-power and state-of-function estimation in electric vehicle applications. 2012 IEEE Energy Conversion Congress and Exposition, ECCE 2012, 1819-1826. doi:10.1109/ECCE.2012.6342591
  • Jun Xu, Mi, C. C., Binggang Cao, Junjun Deng, Zheng Chen, & Siqi Li. (2014). The State of Charge Estimation of Lithium-Ion Batteries Based on a Proportional-Integral Observer. IEEE Transactions on Vehicular Technology, 63(4), 1614-1621. doi:10.1109/TVT.2013.2287375
  • Jung, D. Y., Lee, B. H., & Kim, S. W. (2002). Development of battery management system for nickel-metal hydride batteries in electric vehicle applications. Journal of Power Sources, 109(1), 1-10. doi:10.1016/S0378-7753(02)00020-4
  • Karabeyoğlu, E. D., Güzel, E., Kılıç, H., Sarıoğlu, B., Gökdel, Y. D., & Serincan, M. F. (2019). Investigation and Design of an Active Cell Balancing System for Li-İon Batteries. Mühendislik Bilimleri ve Tasarım Dergisi, 7(4), 869-877. doi:10.21923/jesd.553295
  • Kim, I.-S. (2006). The novel state of charge estimation method for lithium battery using sliding mode observer. Journal of Power Sources, 163(1), 584-590. doi:10.1016/j.jpowsour.2006.09.006
  • Kıvrak, S., Özer, T., & Oğuz, Y. (2020). Can Bus Based BMS Control Card Design And Implementation By Using STM32f103 Series Microcontroller. Engineering Sciences, 15(1), 27-33. doi:10.12739/NWSA.2020.15.1.1A0448
  • Kong, X., Zheng, Y., Ouyang, M., Li, X., Lu, L., & Li, J. (2017). Signal synchronization for massive data storage in modular battery management system with controller area network. Applied Energy, 197, 52-62. doi:10.1016/j.apenergy.2017.04.002
  • Li, Yanwen, Wang, C., & Gong, J. (2016). A combination Kalman filter approach for State of Charge estimation of lithium-ion battery considering model uncertainty. Energy, 109, 933-946. doi:10.1016/j.energy.2016.05.047
  • Li, Yi, Liu, K., Foley, A. M., Zülke, A., Berecibar, M., Nanini-Maury, E., Van Mierlo, J., & Hoster, H. E. (2019). Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review. Renewable and Sustainable Energy Reviews, 113, 109254. doi:10.1016/j.rser.2019.109254
  • Li, Yigang, Chen, J., & Lan, F. (2020). Enhanced online model identification and state of charge estimation for lithium-ion battery under noise corrupted measurements by bias compensation recursive least squares. Journal of Power Sources, 456, 227984. doi:10.1016/j.jpowsour.2020.227984
  • Li, Yue, Chattopadhyay, P., Xiong, S., Ray, A., & Rahn, C. D. (2016). Dynamic data-driven and model-based recursive analysis for estimation of battery state-of-charge. Applied Energy, 184, 266-275. doi:10.1016/j.apenergy.2016.10.025
  • Li, Zhe, Huang, J., Liaw, B. Y., & Zhang, J. (2017). On state-of-charge determination for lithium-ion batteries. Journal of Power Sources, 348, 281-301. doi:10.1016/j.jpowsour.2017.03.001
  • Li, Zhenhe, Khajepour, A., & Song, J. (2019). A comprehensive review of the key technologies for pure electric vehicles. Energy, 182, 824-839. doi:10.1016/j.energy.2019.06.077
  • Lin, C., Mu, H., Xiong, R., & Cao, J. (2017). Multi-model probabilities based state fusion estimation method of lithium-ion battery for electric vehicles: State-of-energy. Applied Energy, 194, 560-568. doi:10.1016/j.apenergy.2016.05.065
  • Lin, C., Mu, H., Xiong, R., & Shen, W. (2016). A novel multi-model probability battery state of charge estimation approach for electric vehicles using H-infinity algorithm. Applied Energy, 166, 76-83. doi:10.1016/j.apenergy.2016.01.010
  • Lipu, M. S. H., Hannan, M. A., Hussain, A., Hoque, M. M., Ker, P. J., Saad, M. H. M., & Ayob, A. (2018). A review of state of health and remaining useful life estimation methods for lithium-ion battery in electric vehicles: Challenges and recommendations. Journal of Cleaner Production, 205, 115-133. doi:10.1016/j.jclepro.2018.09.065
  • Liu, H., Wei, Z., He, W., & Zhao, J. (2017). Thermal issues about Li-ion batteries and recent progress in battery thermal management systems: A review. Energy Conversion and Management, 150, 304-330. doi:10.1016/j.enconman.2017.08.016
  • Liu, K., Li, K., Peng, Q., & Zhang, C. (2019). A brief review on key technologies in the battery management system of electric vehicles. Frontiers of Mechanical Engineering, 14(1), 47-64. doi:10.1007/s11465-018-0516-8
  • Liu, X., Wu, J., Zhang, C., & Chen, Z. (2014). A method for state of energy estimation of lithium-ion batteries at dynamic currents and temperatures. Journal of Power Sources, 270, 151-157. doi:10.1016/j.jpowsour.2014.07.107
  • Lopez, C. F., Jeevarajan, J. A., & Mukherjee, P. P. (2016). Evaluation of Combined Active and Passive Thermal Management Strategies for Lithium-Ion Batteries. Journal of Electrochemical Energy Conversion and Storage, 13(3), 1-10. doi:10.1115/1.4035245
  • Lu, J., Chen, Z., Yang, Y., & L.V., M. (2018). Online Estimation of State of Power for Lithium-Ion Batteries in Electric Vehicles Using Genetic Algorithm. IEEE Access, 6, 20868-20880. doi:10.1109/ACCESS.2018.2824559
  • Lu, L., Han, X., Li, J., Hua, J., & Ouyang, M. (2013). A review on the key issues for lithium-ion battery management in electric vehicles. Journal of Power Sources, 226, 272-288. doi:10.1016/j.jpowsour.2012.10.060
  • Lu, M., Zhang, X., Ji, J., Xu, X., & Zhang, Y. (2020). Research progress on power battery cooling technology for electric vehicles. Journal of Energy Storage, 27, 101155. doi:10.1016/j.est.2019.101155
  • Lukic, S., & Emadi, A. (2008). Charging ahead. IEEE Industrial Electronics Magazine, 2(4), 22-31. doi:10.1109/MIE.2008.930361 Ma, Y., Li, B., Xie, Y., & Chen, H. (2016). Estimating the State of Charge of Lithium-ion Battery based on Sliding Mode Observer. IFAC-PapersOnLine, 49(11), 54-61. doi:10.1016/j.ifacol.2016.08.009
  • Mamadou, K., Lemaire, E., Delaille, A., Riu, D., Hing, S. E., & Bultel, Y. (2012). Definition of a State-of-Energy Indicator (SoE) for Electrochemical Storage Devices: Application for Energetic Availability Forecasting. Journal of The Electrochemical Society, 159(8), A1298-A1307. doi:10.1149/2.075208jes
  • Martinez, A. C., Sorlien, D., Goodrich, R., Chandler, L., & Magnuson, D. (2005). Multi-cell Li-Ion Battery Packs. An167. www.xicor.com
  • Maskey, M., Patten, M., Vines, D., & Maxwell, T. (1999). Intelligent battery management system for electric and hybrid electric vehicles. IEEE VTS 50th Vehicular Technology Conference, VTC 1999-Fall, 2, 1389-1391. doi:10.1109/vetec.1999.780575
  • Mastali, M., Vazquez-Arenas, J., Fraser, R., Fowler, M., Afshar, S., & Stevens, M. (2013). Battery state of the charge estimation using Kalman filtering. Journal of Power Sources, 239, 294-307. doi:10.1016/j.jpowsour.2013.03.131
  • Meissner, E., & Richter, G. (2003). Battery Monitoring and Electrical Energy Management precondition for future vehicle electric power systems. Journal of Power Sources, 116(1-2), 79-98. doi:10.1016/S0378-7753(02)00713-9
  • Meng, J., Ricco, M., Acharya, A. B., Luo, G., Swierczynski, M., Stroe, D.-I., & Teodorescu, R. (2018). Low-complexity online estimation for LiFePO4 battery state of charge in electric vehicles. Journal of Power Sources, 395, 280-288. doi:10.1016/j.jpowsour.2018.05.082
  • Messier, P., Lebel, F. A., Rouleau, J., & Trovão, J. P. F. (2019). Multi-cell emulation for battery management system validation. 2018 IEEE Vehicle Power and Propulsion Conference, VPPC 2018 - Proceedings. doi:10.1109/VPPC.2018.8604959
  • Messier, P., Nguyễn, B. H., LeBel, F. A., & Trovão, J. P. F. (2020). Disturbance observer-based state-of-charge estimation for Li-ion battery used in light electric vehicles. Journal of Energy Storage, 27, 101144. doi:10.1016/j.est.2019.101144
  • Moore, S. W., & Schneider, P. J. (2001). A review of cell equalization methods for lithium ion and lithium polymer battery systems. SAE Technical Papers, 724. doi:10.4271/2001-01-0959
  • Navet, N., Song, Y., Simonot-Lion, F., & Wilwert, C. (2005). Trends in automotive communication systems. Proceedings of the IEEE, 93(6), 1204-1222. doi:10.1109/JPROC.2005.849725
  • Ng, K. S., Moo, C.-S., Chen, Y.-P., & Hsieh, Y.-C. (2009). Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries. Applied Energy, 86(9), 1506-1511. doi:10.1016/j.apenergy.2008.11.021
  • Omariba, Z. B., Zhang, L., & Sun, D. (2019). Review of Battery Cell Balancing Methodologies for Optimizing Battery Pack Performance in Electric Vehicles. IEEE Access, 7, 129335-129352. doi:10.1109/ACCESS.2019.2940090
  • Pan, H., Lü, Z., Lin, W., Li, J., & Chen, L. (2017). State of charge estimation of lithium-ion batteries using a grey extended Kalman filter and a novel open-circuit voltage model. Energy, 138, 764-775. doi:10.1016/j.energy.2017.07.099
  • Park, S., Ahn, J., Kang, T., Park, S., Kim, Y., Cho, I., & Kim, J. (2020). Review of state-of-the-art battery state estimation technologies for battery management systems of stationary energy storage systems. Journal of Power Electronics, 20(6), 1526-1540. doi:10.1007/s43236-020-00122-7
  • Pesaran, A. (2001). Battery Thermal Management in EVs and HEVs: Issues and Solutions. Advanced Automotive Battery Conference, January 2001, 10.
  • Pesaran, A. A., Burch, S., & Keyser, M. (1999). An Approach for Designing Thermal Management Systems for Electric and Hybrid Vehicle Battery Packs Preprint. The Fourth Vehicle Thermal Management Systems Conference and Exhibition 24-27, January, 1-18.
  • Plett, G. L. (2004). Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs. Journal of Power Sources, 134(2), 252-261. doi:10.1016/j.jpowsour.2004.02.031
  • Qaisar, S. M. (2020). Event-Driven Approach for an Efficient Coulomb Counting Based Li-Ion Battery State of Charge Estimation. Procedia Computer Science, 168, 202-209. doi:10.1016/j.procs.2020.02.268
  • Qi, J., & Dah-Chuan Lu, D. (2014). Review of battery cell balancing techniques. 2014 Australasian Universities Power Engineering Conference, AUPEC 2014 - Proceedings, October, 2-7. doi:10.1109/AUPEC.2014.6966514
  • Qian, K., Huang, B., Ran, A., He, Y. B., Li, B., & Kang, F. (2019). State-of-health (SOH) evaluation on lithium-ion battery by simulating the voltage relaxation curves. Electrochimica Acta, 303, 183-191. doi:10.1016/j.electacta.2019.02.055
  • Qiu, X., Wu, W., & Wang, S. (2020). Remaining useful life prediction of lithium-ion battery based on improved cuckoo search particle filter and a novel state of charge estimation method. Journal of Power Sources, 450, 227700. doi:10.1016/j.jpowsour.2020.227700
  • Rahimi-Eichi, H., Ojha, U., Baronti, F., & Chow, M. Y. (2013). Battery management system: An overview of its application in the smart grid and electric vehicles. IEEE Industrial Electronics Magazine, 7(2), 4-16. doi:10.1109/MIE.2013.2250351
  • Rajalakshmi, M., & Razia Sultana, W. (2020). Intelligent Hybrid Battery Management System for Electric Vehicle. Içinde A. Chitra, P. Sanjeevikumar, J. B. Holm‐Nielsen & S. Himavathi (Eds.) Artificial Intelligent Techniques for Electric and Hybrid Electric Vehicles (ss. 179-206). Wiley. doi:10.1002/9781119682035.ch10
  • Ramadan, H. S., Becherif, M., & Claude, F. (2017). Extended kalman filter for accurate state of charge estimation of lithium-based batteries: a comparative analysis. International Journal of Hydrogen Energy, 42(48), 29033-29046. doi:10.1016/j.ijhydene.2017.07.219
  • Rao, Z., & Wang, S. (2011). A review of power battery thermal energy management. Renewable and Sustainable Energy Reviews, 15(9), 4554-4571. doi:10.1016/j.rser.2011.07.096
  • Rezvanizaniani, S. M., Liu, Z., Chen, Y., & Lee, J. (2014). Review and recent advances in battery health monitoring and prognostics technologies for electric vehicle (EV) safety and mobility. Journal of Power Sources, 256, 110-124. doi:10.1016/j.jpowsour.2014.01.085
  • Rivera-Barrera, J., Muñoz-Galeano, N., & Sarmiento-Maldonado, H. (2017). SoC Estimation for Lithium-ion Batteries: Review and Future Challenges. Electronics, 6(4), 102. doi:10.3390/electronics6040102
  • Sabbah, R., Kizilel, R., Selman, J. R., & Al-Hallaj, S. (2008). Active (air-cooled) vs. passive (phase change material) thermal management of high power lithium-ion packs: Limitation of temperature rise and uniformity of temperature distribution. Journal of Power Sources, 182(2), 630-638. doi:10.1016/j.jpowsour.2008.03.082
  • Salkind, A. J., Fennie, C., Singh, P., Atwater, T., & Reisner, D. E. (1999). Determination of state-of-charge and state-of-health of batteries by fuzzy logic methodology. Journal of Power Sources, 80(1-2), 293-300. doi:10.1016/S0378-7753(99)00079-8
  • Saraf, P. (2012). The Traditional and New Generation in-vehicle Networks in Automotive Field. International Conference on Advances in Computer, Electronics and Electrical Engineering, March, 978-981. doi:10.3850/978-981-07-1847-3
  • Sarikurt, T., & Balikçi, A. (2017). Tam elektrikli araçlar için özgün bir enerji yönetim sistemi uygulaması. Journal of the Faculty of Engineering and Architecture of Gazi University, 32(2), 323--333. doi:10.17341/gazimmfd.322153
  • Saw, L. H., Ye, Y., & Tay, A. A. O. (2016). Integration issues of lithium-ion battery into electric vehicles battery pack. Journal of Cleaner Production, 113, 1032-1045. doi:10.1016/j.jclepro.2015.11.011
  • Shen, M., & Gao, Q. (2019). A review on battery management system from the modeling efforts to its multiapplication and integration. International Journal of Energy Research, 43(10), 5042-5075. doi:10.1002/er.4433
  • Shen, P., Ouyang, M., Lu, L., Li, J., & Feng, X. (2018). The co-estimation of state of charge, state of health, and state of function for lithium-ion batteries in electric vehicles. IEEE Transactions on Vehicular Technology, 67(1), 92-103. doi:10.1109/TVT.2017.2751613
  • Shen, Y. (2010). Adaptive online state-of-charge determination based on neuro-controller and neural network. Energy Conversion and Management, 51(5), 1093-1098. doi:10.1016/j.enconman.2009.12.015
  • Shen, Y. (2018a). A chaos genetic algorithm based extended Kalman filter for the available capacity evaluation of lithium-ion batteries. Electrochimica Acta, 264, 400-409. doi:10.1016/j.electacta.2018.01.123
  • Shen, Y. (2018b). Improved chaos genetic algorithm based state of charge determination for lithium batteries in electric vehicles. Energy, 152, 576-585. doi:10.1016/j.energy.2018.03.174
  • Shen, Y. (2018c). Adaptive extended Kalman filter based state of charge determination for lithium-ion batteries. Electrochimica Acta, 283, 1432-1440. doi:10.1016/j.electacta.2018.07.078
  • Sheng, H., & Xiao, J. (2015). Electric vehicle state of charge estimation: Nonlinear correlation and fuzzy support vector machine. Journal of Power Sources, 281, 131-137. doi:10.1016/j.jpowsour.2015.01.145
  • Shrivastava, P., Soon, T. K., Idris, M. Y. I. Bin, & Mekhilef, S. (2019). Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries. Renewable and Sustainable Energy Reviews, 113, 109233. doi:10.1016/j.rser.2019.06.040
  • Siddique, A. R. M., Mahmud, S., & Heyst, B. Van. (2018). A comprehensive review on a passive (phase change materials) and an active (thermoelectric cooler) battery thermal management system and their limitations. Journal of Power Sources, 401, 224-237. doi:10.1016/j.jpowsour.2018.08.094
  • Singh, P., Fennie, C., & Reisner, D. (2004). Fuzzy logic modelling of state-of-charge and available capacity of nickel/metal hydride batteries. Journal of Power Sources, 136(2), 322-333. doi:10.1016/j.jpowsour.2004.03.035
  • Singh, P., Vinjamuri, R., Wang, X., & Reisner, D. (2006). Design and implementation of a fuzzy logic-based state-of-charge meter for Li-ion batteries used in portable defibrillators. Journal of Power Sources, 162(2), 829-836. doi:10.1016/j.jpowsour.2005.04.039
  • Steinhorst, S., Lukasiewycz, M., Narayanaswamy, S., Kauer, M., & Chakraborty, S. (2014). Smart cells for embedded battery management. Proceedings - 2nd IEEE International Conference on Cyber-Physical Systems, Networks, and Applications, CPSNA 2014, 59-64. doi:10.1109/CPSNA.2014.22
  • Steinhorst, S., Shao, Z., Chakraborty, S., Kauer, M., Li, S., Lukasiewycz, M., Narayanaswamy, S., Rafique, M. U., & Wang, Q. (2016). Distributed reconfigurable Battery System Management Architectures. Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC, 25-28 Jan, 429-434. doi:10.1109/ASPDAC.2016.7428049
  • Stuart, T. A., & Zhu, W. (2011). Modularized battery management for large lithium ion cells. Journal of Power Sources, 196(1), 458-464. doi:10.1016/j.jpowsour.2010.04.055
  • Stuart, T., Fang, F., Wang, X., Ashtiani, C., & Pesaran, A. (2002). A modular battery management system for HEVs. SAE Technical Papers, 111, 777-785. doi:10.4271/2002-01-1918
  • Sun, F., Hu, X., Zou, Y., & Li, S. (2011). Adaptive unscented Kalman filtering for state of charge estimation of a lithium-ion battery for electric vehicles. Energy, 36(5), 3531-3540. doi:10.1016/j.energy.2011.03.059
  • Teng, H., & Yeow, K. (2012). Design of direct and indirect liquid cooling systems for high-capacity, high-power lithium-ion battery packs. SAE International Journal of Alternative Powertrains, 1(2), 525-536. doi:10.4271/2012-01-2017
  • Thakur, A. K., Prabakaran, R., Elkadeem, M. R., Sharshir, S. W., Arıcı, M., Wang, C., Zhao, W., Hwang, J.-Y., & Saidur, R. (2020). A state of art review and future viewpoint on advance cooling techniques for Lithium–ion battery system of electric vehicles. Journal of Energy Storage, 32, 101771. doi:10.1016/j.est.2020.101771
  • Tian, H., Qin, P., Li, K., & Zhao, Z. (2020). A review of the state of health for lithium-ion batteries: Research status and suggestions. Journal of Cleaner Production, 261, 120813. doi:10.1016/j.jclepro.2020.120813
  • Tian, Y., Xia, B., Sun, W., Xu, Z., & Zheng, W. (2014). A modified model based state of charge estimation of power lithium-ion batteries using unscented Kalman filter. Journal of Power Sources, 270, 619-626. doi:10.1016/j.jpowsour.2014.07.143
  • Ting, T. O., Man, K. L., Lim, E. G., & Leach, M. (2014). Tuning of Kalman Filter Parameters via Genetic Algorithm for State-of-Charge Estimation in Battery Management System. The Scientific World Journal, 2014, 176052. doi:10.1155/2014/176052
  • TOGG Press Release. (2020). ‘More Than a Factory’ Construction starts at TOGG Gemlik. (Accessed:18/09/2020) https://www.togg.com.tr/Dosyalar/Press/togg-construction-pr.pdf
  • Ungurean, L., Cârstoiu, G., Micea, M. V., & Groza, V. (2017). Battery state of health estimation: a structured review of models, methods and commercial devices. International Journal of Energy Research, 41(2), 151-181. doi:10.1002/er.3598
  • Urbain, M., Rael, S., Davat, B., & Desprez, P. (2007). State Estimation of a Lithium-Ion Battery Through Kalman Filter. 2007 IEEE Power Electronics Specialists Conference, 2804-2810. doi:10.1109/PESC.2007.4342463
  • Väyrynen, A., & Salminen, J. (2012). Lithium ion battery production. Journal of Chemical Thermodynamics, 46, 80-85. doi:10.1016/j.jct.2011.09.005
  • Waag, W., Fleischer, C., & Sauer, D. U. (2014). Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles. Journal of Power Sources, 258, 321-339. doi:10.1016/j.jpowsour.2014.02.064
  • Wang, Q., Jiang, B., Li, B., & Yan, Y. (2016). A critical review of thermal management models and solutions of lithium-ion batteries for the development of pure electric vehicles. Renewable and Sustainable Energy Reviews, 64, 106-128. doi:10.1016/j.rser.2016.05.033
  • Wang, S.-L., Fernandez, C., Zou, C.-Y., Yu, C.-M., Li, X.-X., Pei, S.-J., & Xie, W. (2018). Open circuit voltage and state of charge relationship functional optimization for the working state monitoring of the aerial lithium-ion battery pack. Journal of Cleaner Production, 198, 1090-1104. doi:10.1016/j.jclepro.2018.07.030
  • Wang, Y., Tian, J., Sun, Z., Wang, L., Xu, R., Li, M., & Chen, Z. (2020). A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems. Renewable and Sustainable Energy Reviews, 131, 110015. doi:10.1016/j.rser.2020.110015
  • Wang, Y., Zhang, C., & Chen, Z. (2016). An adaptive remaining energy prediction approach for lithium-ion batteries in electric vehicles. Journal of Power Sources, 305, 80-88. doi:10.1016/j.jpowsour.2015.11.087
  • Wei, J., Dong, G., & Chen, Z. (2017). On-board adaptive model for state of charge estimation of lithium-ion batteries based on Kalman filter with proportional integral-based error adjustment. Journal of Power Sources, 365, 308-319. doi:10.1016/j.jpowsour.2017.08.101
  • Wu, S., Xiong, R., Li, H., Nian, V., & Ma, S. (2020). The state of the art on preheating lithium-ion batteries in cold weather. Journal of Energy Storage, 27, 101059. doi:10.1016/j.est.2019.101059
  • Wu, W., Wang, S., Wu, W., Chen, K., Hong, S., & Lai, Y. (2019). A critical review of battery thermal performance and liquid based battery thermal management. Energy Conversion and Management, 182, 262-281. doi:10.1016/j.enconman.2018.12.051
  • Wu, W., Yang, X., Zhang, G., Ke, X., Wang, Z., Situ, W., Li, X., & Zhang, J. (2016). An experimental study of thermal management system using copper mesh-enhanced composite phase change materials for power battery pack. Energy, 113, 909-916. doi:10.1016/j.energy.2016.07.119
  • Xia, B., Chen, C., Tian, Y., Sun, W., Xu, Z., & Zheng, W. (2014). A novel method for state of charge estimation of lithium-ion batteries using a nonlinear observer. Journal of Power Sources, 270, 359-366. doi:10.1016/j.jpowsour.2014.07.103
  • Xia, B., Lao, Z., Zhang, R., Tian, Y., Chen, G., Sun, Z., Wang, W., Sun, W., Lai, Y., Wang, M., & Wang, H. (2017). Online Parameter Identification and State of Charge Estimation of Lithium-Ion Batteries Based on Forgetting Factor Recursive Least Squares and Nonlinear Kalman Filter. Energies, 11(1), 3. doi:10.3390/en11010003
  • Xia, B., Zhang, Z., Lao, Z., Wang, W., Sun, W., Lai, Y., & Wang, M. (2018). Strong Tracking of a H-Infinity Filter in Lithium-Ion Battery State of Charge Estimation. Energies, 11(6), 1481. doi:10.3390/en11061481
  • Xia, G., Cao, L., & Bi, G. (2017). A review on battery thermal management in electric vehicle application. Journal of Power Sources, 367, 90-105. doi:10.1016/j.jpowsour.2017.09.046
  • Xing, Y., He, W., Pecht, M., & Tsui, K. L. (2014). State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatu res. Applied Energy, 113, 106-115. doi:10.1016/j.apenergy.2013.07.008
  • Xing, Y., Ma, E. W. M., Tsui, K. L., & Pecht, M. (2011). Battery management systems in electric and hybrid vehicles. Energies, 4(11), 1840-1857. doi:10.3390/en4111840
  • Xiong, R. (2020). Battery Management Algorithm for Electric Vehicles. Içinde Battery Management Algorithm for Electric Vehicles. Springer Singapore. doi:10.1007/978-981-15-0248-4
  • Xiong, R., Gong, X., Mi, C. C., & Sun, F. (2013). A robust state-of-charge estimator for multiple types of lithium-ion batteries using adaptive extended Kalman filter. Journal of Power Sources, 243, 805-816. doi:10.1016/j.jpowsour.2013.06.076
  • Xiong, R., Li, L., & Tian, J. (2018). Towards a smarter battery management system: A critical review on battery state of health monitoring methods. Journal of Power Sources, 405(5), 18-29. doi:10.1016/j.jpowsour.2018.10.019
  • Xiong, R., Yu, Q., & Wang, L. Y. (2017). Open circuit voltage and state of charge online estimation for lithium ion batteries. Energy Procedia, 142, 1902-1907. doi:10.1016/j.egypro.2017.12.388
  • Xiong, R., Yu, Q., Wang, L. Y., & Lin, C. (2017). A novel method to obtain the open circuit voltage for the state of charge of lithium ion batteries in electric vehicles by using H infinity filter. Applied Energy, 207, 346-353. doi:10.1016/j.apenergy.2017.05.136
  • Yang, D., Wang, Y., Pan, R., Chen, R., & Chen, Z. (2017). A Neural Network Based State-of-Health Estimation of Lithium-ion Battery in Electric Vehicles. Energy Procedia, 105, 2059-2064. doi:10.1016/j.egypro.2017.03.583
  • Yang, D., Wang, Y., Pan, R., Chen, R., & Chen, Z. (2018). State-of-health estimation for the lithium-ion battery based on support vector regression. Applied Energy, 227, 273-283. doi:10.1016/j.apenergy.2017.08.096
  • Yang, F., Zhang, S., Li, W., & Miao, Q. (2020). State-of-charge estimation of lithium-ion batteries using LSTM and UKF. Energy, 201, 117664. doi:10.1016/j.energy.2020.117664
  • Yang, S., Ling, C., Fan, Y., Yang, Y., Tan, X., & Dong, H. (2019). A review of lithium-ion battery thermal management system strategies and the evaluate criteria. International Journal of Electrochemical Science, 14(7), 6077-6107. doi:10.20964/2019.07.06
  • Yang, X., Chen, Y., Li, B., & Luo, D. (2020). Battery states online estimation based on exponential decay particle swarm optimization and proportional-integral observer with a hybrid battery model. Energy, 191, 116509. doi:10.1016/j.energy.2019.116509
  • Yatsui, M. W., & Bai, H. (2011). Kalman filter based state-of-charge estimation for lithium-ion batteries in hybrid electric vehicles using pulse charging. 2011 IEEE Vehicle Power and Propulsion Conference, 1-5. doi:10.1109/VPPC.2011.6042988
  • Ye, M., Guo, H., Xiong, R., & Yu, Q. (2018). A double-scale and adaptive particle filter-based online parameter and state of charge estimation method for lithium-ion batteries. Energy, 144, 789-799. doi:10.1016/j.energy.2017.12.061
  • Yenigün, M., & Utlu, Z. (2018). Elektrikli Araçlarda Kullanılan Batarya Soğutma Sistemlerinin İncelenmesi ve Değerlendirilmesi. Mühendis ve Makina Dergisi, 59(692), 35-47.
  • Yong, J. Y., Ramachandaramurthy, V. K., Tan, K. M., & Mithulananthan, N. (2015). A review on the state-of-the-art technologies of electric vehicle, its impacts and prospects. Renewable and Sustainable Energy Reviews, 49, 365-385. doi:10.1016/j.rser.2015.04.130
  • Yu, Z., Huai, R., & Xiao, L. (2015). State-of-charge estimation for lithium-ion batteries using a Kalman filter based on local linearization. Energies, 8(8), 7854-7873. doi:10.3390/en8087854
  • Zenati, A., Desprez, P., & Razik, H. (2010). Estimation of the SOC and the SOH of li-ion batteries, by combining impedance measurements with the fuzzy logic inference. IECON 2010 - 36th Annual Conference on IEEE Industrial Electronics Society, 1773-1778. doi:10.1109/IECON.2010.5675408
  • Zhang, Q., Cui, N., Li, Y., Duan, B., & Zhang, C. (2020). Fractional calculus based modeling of open circuit voltage of lithium-ion batteries for electric vehicles. Journal of Energy Storage, 27, 100945. doi:10.1016/j.est.2019.100945
  • Zhang, S., Guo, X., Dou, X., & Zhang, X. (2020a). A data-driven coulomb counting method for state of charge calibration and estimation of lithium-ion battery. Sustainable Energy Technologies and Assessments, 40, 100752. doi:10.1016/j.seta.2020.100752
  • Zhang, S., Guo, X., Dou, X., & Zhang, X. (2020b). A rapid online calculation method for state of health of lithium-ion battery based on coulomb counting method and differential voltage analysis. Journal of Power Sources, 479, 228740. doi:10.1016/j.jpowsour.2020.228740
  • Zhang, Xiujuan, Liu, P., & Wang, D. (2011). The design and implementation of smart battery management system balance technology. Journal of Convergence Information Technology, 6(5), 108-116. doi:10.4156/jcit.vol6.issue5.12
  • Zhang, Xu, Wang, Y., Liu, C., & Chen, Z. (2018). A novel approach of battery pack state of health estimation using artificial intelligence optimization algorithm. Journal of Power Sources, 376, 191-199. doi:10.1016/j.jpowsour.2017.11.068
  • Zhao, L., Liu, Z., & Ji, G. (2018). Lithium-ion battery state of charge estimation with model parameters adaptation using H∞ extended Kalman Filter. Control Engineering Practice, 81, 114-128. doi:10.1016/j.conengprac.2018.09.010
  • Zheng, F., Xing, Y., Jiang, J., Sun, B., Kim, J., & Pecht, M. (2016). Influence of different open circuit voltage tests on state of charge online estimation for lithium-ion batteries. Applied Energy, 183, 513-525. doi:10.1016/j.apenergy.2016.09.010
  • Zhi, L., Peng, Z., Zhifu, W., Qiang, S., & Yinan, R. (2017). State of Charge Estimation for Li-ion Battery Based on Extended Kalman Filter. Energy Procedia, 105, 3515-3520. doi:10.1016/j.egypro.2017.03.806
  • Zhou, F., Wang, L., Lin, H., & Lv, Z. (2013). High accuracy state-of-charge online estimation of EV/HEV lithium batteries based on Adaptive Wavelet Neural Network. 2013 IEEE ECCE Asia Downunder, 513-517. doi:10.1109/ECCE-Asia.2013.6579145
  • Zhu, H., Wu, Z., Wang, D., & Sun, J. (2013). Design and implementation of distributed battery management system. Advanced Materials Research, 608-609, 1039-1042. doi:10.4028/www.scientific.net/AMR.608-609.1039
  • Zhu, Q., Xiong, N., Yang, M.-L., Huang, R.-S., & Hu, G.-D. (2017). State of Charge Estimation for Lithium-Ion Battery Based on Nonlinear Observer: An H∞ Method. Energies, 10(5), 679. doi:10.3390/en10050679
  • Zhu, R., Duan, B., Zhang, J., Zhang, Q., & Zhang, C. (2020). Co-estimation of model parameters and state-of-charge for lithium-ion batteries with recursive restricted total least squares and unscented Kalman filter. Applied Energy, 277, 115494. doi:10.1016/j.apenergy.2020.115494
  • Zou, Y., Hu, X., Ma, H., & Li, S. E. (2015). Combined State of Charge and State of Health estimation over lithium-ion battery cell cycle lifespan for electric vehicles. Journal of Power Sources, 273, 793-803. doi:10.1016/j.jpowsour.2014.09.146

A Review Study on Battery Management Systems in Electric Vehicles

Year 2021, Volume: 8 Issue: 2, 234 - 275, 28.06.2021

Abstract

Due to concerns about global warming, environmental damages from fossil fuels, and greenhouse gas emissions, electric vehicles have been taken place of internal combustion motor vehicles day by day. Being the main energy source for electric vehicles, the batteries have some limitations to give certain operations for safe driving. The battery management systems (BMSs) play a vital role in order to overcome these limitations, protect the battery and ensure more reliable driving for electric vehicles. This paper reviews the papers about the battery management system and its sub-issues including battery monitoring, battery safety, vehicle internal-external communication, cell balancing, state estimations, thermal management, and topologies. The methods about such issues have been compared in terms of merits-demerits and qualitative factors. Since electric vehicles will be the transportation vehicles for the future and domestic production has been started in our country, the authors consider that it is necessary to improve the Turkish literature on electric vehicles and conduct academic studies. The authors consider that this study will contribute to the Turkish literature and gaining some perspective to the designers, researchers, producers, and companies that work in the field of the battery management systems.

References

  • Adany, R., Aurbach, D., & Kraus, S. (2013). Switching algorithms for extending battery life in Electric Vehicles. Journal of Power Sources, 231, 50-59.doi:10.1016/j.jpowsour.2012.12.075
  • Aktaş, M., Baygüneş, B., Kıvrak, S., Çavuş, B., & Sözen, F. (2020). Elektrikli Araç İçin Düşük Maliyetli Bir Batarya Yönetim Sistemi Tasarımı ve Gerçekleştirilmesi. Avrupa Bilim ve Teknoloji Dergisi, Ejosat Special Issue 2020 (HORA), 227-238. doi:10.31590/ejosat.779720
  • Al-Hallaj, S., & Selman, J. R. (2002). Thermal modeling of secondary lithium batteries for electric vehicle/hybrid electric vehicle applications. Journal of Power Sources, 110(2), 341-348. doi:10.1016/S0378-7753(02)00196-9
  • Alvarez Anton, J. C., Garcia Nieto, P. J., Blanco Viejo, C., & Vilan Vilan, J. A. (2013). Support Vector Machines Used to Estimate the Battery State of Charge. IEEE Transactions on Power Electronics, 28(12), 5919-5926. doi:10.1109/TPEL.2013.2243918
  • Álvarez Antón, J. C., García Nieto, P. J., de Cos Juez, F. J., Sánchez Lasheras, F., González Vega, M., & Roqueñí Gutiérrez, M. N. (2013). Battery state-of-charge estimator using the SVM technique. Applied Mathematical Modelling, 37(9), 6244-6253. doi:10.1016/j.apm.2013.01.024
  • Amir, U., Tao, L., Zhang, X., Saeed, M., & Hussain, M. (2018). A Novel SOC Estimation Method for Lithium Ion Battery Based On Improved Adaptive PI Observer. 2018 IEEE International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles & International Transportation Electrification Conference (ESARS-ITEC), 1-5. doi:10.1109/ESARS-ITEC.2018.8607386
  • Andrea, D. (2010). Battery Management Systems for Large Lithium-Ion Battery Packs. Artech hause.
  • Ariantara, B., Putra, N., & Supriadi, S. (2018). Battery thermal management system using loop heat pipe with LTP copper capillary wick. IOP Conference Series: Earth and Environmental Science, 105(1), 012045. doi:10.1088/1755-1315/105/1/012045
  • Arora, S. (2018). Selection of thermal management system for modular battery packs of electric vehicles: A review of existing and emerging technologies. Journal of Power Sources, 400, 621-640. doi:10.1016/j.jpowsour.2018.08.020
  • Aydin, S., Gümüş, R., & Akçay, İ. H. (2013). Yakıt Pili ile Çalışan Elektrikli Bir Aracın Güç , Sıcaklık , Bağıl Nem ve Hızının Anlık Olarak İzlenmesi ve Kontrolü. Teknik Bilimler Dergisi, 3(2), 7-12.
  • Bahiraei, F., Fartaj, A., & Nazri, G. A. (2016). Numerical Investigation of Active and Passive Cooling Systems of a Lithium-Ion Battery Module for Electric Vehicles. SAE Technical Papers, 2016-01-0655. doi:10.4271/2016-01-0655
  • Balagopal, B., & Chow, M. Y. (2015). The state of the art approaches to estimate the state of health (SOH) and state of function (SOF) of lithium Ion batteries. Proceeding - 2015 IEEE International Conference on Industrial Informatics, INDIN 2015, 2, 1302-1307. doi:10.1109/INDIN.2015.7281923
  • Bandhauer, T. M., & Garimella, S. (2013). Passive, internal thermal management system for batteries using microscale liquid–vapor phase change. Applied Thermal Engineering, 61(2), 756-769. doi:10.1016/j.applthermaleng.2013.08.004
  • Barai, A., Uddin, K., Widanalage, W. D., McGordon, A., & Jennings, P. (2016). The effect of average cycling current on total energy of lithium-ion batteries for electric vehicles. Journal of Power Sources, 303, 81-85. doi:10.1016/j.jpowsour.2015.10.095
  • Barré, A., Deguilhem, B., Grolleau, S., Gérard, M., Suard, F., & Riu, D. (2013). A review on lithium-ion battery ageing mechanisms and estimations for automotive applications. Journal of Power Sources, 241, 680-689. doi:10.1016/j.jpowsour.2013.05.040
  • Berecibar, M., Gandiaga, I., Villarreal, I., Omar, N., Van Mierlo, J., & Van Den Bossche, P. (2016). Critical review of state of health estimation methods of Li-ion batteries for real applications. Renewable and Sustainable Energy Reviews, 56, 572-587. doi:10.1016/j.rser.2015.11.042
  • Campestrini, C., Heil, T., Kosch, S., & Jossen, A. (2016). A comparative study and review of different Kalman filters by applying an enhanced validation method. Journal of Energy Storage, 8, 142-159. doi:10.1016/j.est.2016.10.004
  • Cao, J., Schofield, N., & Emadi, A. (2008). Battery balancing methods: A comprehensive review. 2008 IEEE Vehicle Power and Propulsion Conference, VPPC 2008, 3-8. doi:10.1109/VPPC.2008.4677669
  • Carter, J., Fan, Z., & Cao, J. (2020). Cell equalisation circuits: A review. Journal of Power Sources, 448, 227489. doi:10.1016/j.jpowsour.2019.227489
  • Chan, C. C. (2013). The Rise & Fall of Electric Vehicles in 1828–1930: Lessons Learned [Scanning Our Past]. Proceedings of the IEEE, 101(1), 206-212. doi:10.1109/JPROC.2012.2228370
  • Chau, K. T., Wu, K. C., & Chan, C. C. (2004). A new battery capacity indicator for lithium-ion battery powered electric vehicles using adaptive neuro-fuzzy inference system. Energy Conversion and Management, 45(11-12), 1681-1692. doi:10.1016/j.enconman.2003.09.031
  • Chemali, E., Kollmeyer, P. J., Preindl, M., & Emadi, A. (2018). State-of-charge estimation of Li-ion batteries using deep neural networks: A machine learning approach. Journal of Power Sources, 400, 242-255. doi:10.1016/j.jpowsour.2018.06.104
  • Chen, L., Wang, Z., Lu, Z., Li, J., Ji, B., Wei, H., & Pan, H. (2018). A Novel State-of-Charge Estimation Method of Lithium-Ion Batteries Combining the Grey Model and Genetic Algorithms. IEEE Transactions on Power Electronics, 33(10), 8797-8807. doi:10.1109/TPEL.2017.2782721
  • Chen, X., Shen, W. X., Cao, Z., & Kapoor, A. (2012). Sliding Mode Observer for State of Charge Estimation Based on Battery Equivalent Circuit in Electric Vehicles. Australian Journal of Electrical and Electronics Engineering, 9(3), 225-234. doi:10.1080/1448837X.2012.11464327
  • Chen, Xiaopeng, Shen, W., Cao, Z., & Kapoor, A. (2014a). A novel approach for state of charge estimation based on adaptive switching gain sliding mode observer in electric vehicles. Journal of Power Sources, 246, 667-678. doi:10.1016/j.jpowsour.2013.08.039
  • Chen, Xiaopeng, Shen, W., Cao, Z., & Kapoor, A. (2014b). Adaptive gain sliding mode observer for state of charge estimation based on combined battery equivalent circuit model. Computers & Chemical Engineering, 64, 114-123. doi:10.1016/j.compchemeng.2014.02.015
  • Chen, Zewang, Yang, L., Zhao, X., Wang, Y., & He, Z. (2019). Online state of charge estimation of Li-ion battery based on an improved unscented Kalman filter approach. Applied Mathematical Modelling, 70, 532-544. doi:10.1016/j.apm.2019.01.031
  • Chen, Zheng, Fu, Y., & Mi, C. C. (2013). State of Charge Estimation of Lithium-Ion Batteries in Electric Drive Vehicles Using Extended Kalman Filtering. IEEE Transactions on Vehicular Technology, 62(3), 1020-1030. doi:10.1109/TVT.2012.2235474
  • Chen, Zonghai, Sun, H., Dong, G., Wei, J., & Wu, J. (2019). Particle filter-based state-of-charge estimation and remaining-dischargeable-time prediction method for lithium-ion batteries. Journal of Power Sources, 414, 158-166. doi:10.1016/j.jpowsour.2019.01.012
  • Choudhari, V. G., Dhoble, D. A. S., & Sathe, T. M. (2020). A review on effect of heat generation and various thermal management systems for lithium ion battery used for electric vehicle. Journal of Energy Storage, 32, 101729. doi:10.1016/j.est.2020.101729
  • Choudhury, J. R., Banerjee, T. P., Gurung, H., Bhattacharjee, A. K., & Das, S. (2009). Real time state of charge prediction using Kalman Filter. 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 1190-1194. doi:10.1109/NABIC.2009.5393786
  • Daowd, M., Omar, N., van den Bossche, P., & van Mierlo, J. (2012). Capacitor based battery balancing system. World Electric Vehicle Journal, 5(2), 385-393. doi:10.3390/wevj5020385
  • Daowd, M., Omar, N., Van Den Bossche, P., & Van Mierlo, J. (2011). Passive and active battery balancing comparison based on MATLAB simulation. 2011 IEEE Vehicle Power and Propulsion Conference, 1-7. doi:10.1109/VPPC.2011.6043010
  • Dong, G., Wei, J., & Chen, Z. (2016). Kalman filter for onboard state of charge estimation and peak power capability analysis of lithium-ion batteries. Journal of Power Sources, 328, 615-626. doi:10.1016/j.jpowsour.2016.08.065
  • Dong, G., Zhang, X., Zhang, C., & Chen, Z. (2015). A method for state of energy estimation of lithium-ion batteries based on neural network model. Energy, 90, 879-888. doi:10.1016/j.energy.2015.07.120
  • Drori, Y., & Martinez, C. (2005). The benefits of cell balancing. Application Note, Xicor Incorporated, 1-9. http://www.neue-verpackung.de/ai/resources/a03b3cf05d5.pdf
  • Du, J., Liu, Z., Wang, Y., & Wen, C. (2016). An adaptive sliding mode observer for lithium-ion battery state of charge and state of health estimation in electric vehicles. Control Engineering Practice, 54, 81-90. doi:10.1016/j.conengprac.2016.05.014
  • Duong, V.-H., Bastawrous, H. A., Lim, K., See, K. W., Zhang, P., & Dou, S. X. (2015). Online state of charge and model parameters estimation of the LiFePO4 battery in electric vehicles using multiple adaptive forgetting factors recursive least-squares. Journal of Power Sources, 296, 215-224. doi:10.1016/j.jpowsour.2015.07.041
  • Duraisamy, T., & Kaliyaperumal, D. (2020). Active cell balancing for electric vehicle battery management system. International Journal of Power Electronics and Drive Systems, 11(2), 571-579. doi:10.11591/ijpeds.v11.i2.pp571-579
  • Eddahech, A., Briat, O., & Vinassa, J. M. (2012). Adaptive voltage estimation for EV Li-ion cell based on artificial neural networks state-of-charge meter. 2012 IEEE International Symposium on Industrial Electronics, 1318-1324. doi:10.1109/ISIE.2012.6237281
  • Ekici, Y. E. (2019). Batarya yönetim sistemleri. Yüksek Lisans Tezi, İnönü Üniversitesi, Fen bilimleri Enstitüsü, Malatya, Türkiye.
  • Ekici, Y. E., & Tan, N. (2019). Charge and discharge characteristics of different types of batteries on a hybrid electric vehicle model and selection of suitable battery type for electric vehicles. International Journal of Automotive Science and Technology, 3(4), 62-70. doi:10.30939/ijastech..527971
  • Fang, Y., Xiong, R., & Wang, J. (2018). Estimation of Lithium-Ion Battery State of Charge for Electric Vehicles Based on Dual Extended Kalman Filter. Energy Procedia, 152, 574-579. doi:10.1016/j.egypro.2018.09.213
  • Gallardo-Lozano, J., Romero-Cadaval, E., Milanes-Montero, M. I., & Guerrero-Martinez, M. A. (2014). Battery equalization active methods. Journal of Power Sources, 246, 934-949. doi:10.1016/j.jpowsour.2013.08.026
  • Gao, M., Liu, Y., & He, Z. (2011). Battery state of charge online estimation based on particle filter. 2011 4th International Congress on Image and Signal Processing, 2233-2236. doi:10.1109/CISP.2011.6100603
  • Guenther, C., Schott, B., Hennings, W., Waldowski, P., & Danzer, M. A. (2013). Model-based investigation of electric vehicle battery aging by means of vehicle-to-grid scenario simulations. Journal of Power Sources, 239, 604-610. doi:10.1016/j.jpowsour.2013.02.041
  • Hallaj, S. Al, & Selman, J. R. (2000). A Novel Thermal Management System for Electric Vehicle Batteries Using Phase-Change Material. Journal of The Electrochemical Society, 147(9), 3231. doi:10.1149/1.1393888
  • Hametner, C., & Jakubek, S. (2013). State of charge estimation for Lithium Ion cells: Design of experiments, nonlinear identification and fuzzy observer design. Journal of Power Sources, 238, 413-421. doi:10.1016/j.jpowsour.2013.04.040
  • Han, X., Lu, L., Zheng, Y., Feng, X., Li, Z., Li, J., & Ouyang, M. (2019). A review on the key issues of the lithium ion battery degradation among the whole life cycle. eTransportation, 1, 100005. doi:10.1016/j.etran.2019.100005
  • Hannan, M. A., Lipu, M. S. H., Hussain, A., & Mohamed, A. (2017). A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations. Renewable and Sustainable Energy Reviews, 78, 834-854. doi:10.1016/j.rser.2017.05.001
  • Hansen, T., & Wang, C.-J. (2005). Support vector based battery state of charge estimator. Journal of Power Sources, 141(2), 351-358. doi:10.1016/j.jpowsour.2004.09.020
  • Hauser, A., & Kuhn, R. (2015). Cell balancing, battery state estimation, and safety aspects of battery management systems for electric vehicles. İçinde: B. Scrosati, J. Garche & W. Tillmetz (Eds.) Advances in Battery Technologies for Electric Vehicles (ss. 283-326). Elsevier. doi:10.1016/B978-1-78242-377-5.00012-1
  • He, H. W., Zhang, Y. Z., Xiong, R., & Wang, C. (2015). A novel Gaussian model based battery state estimation approach: State-of-Energy. Applied Energy, 151, 41-48. doi:10.1016/j.apenergy.2015.04.062
  • He, H., Xiong, R., & Peng, J. (2016). Real-time estimation of battery state-of-charge with unscented Kalman filter and RTOS μCOS-II platform. Applied Energy, 162, 1410-1418. doi:10.1016/j.apenergy.2015.01.120
  • He, H., Zhang, X., Xiong, R., Xu, Y., & Guo, H. (2012). Online model-based estimation of state-of-charge and open-circuit voltage of lithium-ion batteries in electric vehicles. Energy, 39(1), 310-318. doi:10.1016/j.energy.2012.01.009
  • He, W., Williard, N., Chen, C., & Pecht, M. (2013). State of charge estimation for electric vehicle batteries using unscented kalman filtering. Microelectronics Reliability, 53(6), 840-847. doi:10.1016/j.microrel.2012.11.010
  • He, W., Williard, N., Chen, C., & Pecht, M. (2014). State of charge estimation for Li-ion batteries using neural network modeling and unscented Kalman filter-based error cancellation. International Journal of Electrical Power & Energy Systems, 62, 783-791. doi:10.1016/j.ijepes.2014.04.059
  • Hoque, M. M., Hannan, M. A., Mohamed, A., & Ayob, A. (2017). Battery charge equalization controller in electric vehicle applications: A review. Renewable and Sustainable Energy Reviews, 75, 1363-1385. doi:10.1016/j.rser.2016.11.126
  • Hou, Z. Y., Lou, P. Y., & Wang, C. C. (2017). State of charge, state of health, and state of function monitoring for EV BMS. 2017 IEEE International Conference on Consumer Electronics, ICCE 2017, 1, 310-311. doi:10.1109/ICCE.2017.7889332
  • Hu, J. N., Hu, J. J., Lin, H. B., Li, X. P., Jiang, C. L., Qiu, X. H., & Li, W. S. (2014). State-of-charge estimation for battery management system using optimized support vector machine for regression. Journal of Power Sources, 269, 682-693. doi:10.1016/j.jpowsour.2014.07.016
  • Hu, L., Hu, X., Che, Y., Feng, F., Lin, X., & Zhang, Z. (2020). Reliable state of charge estimation of battery packs using fuzzy adaptive federated filtering. Applied Energy, 262, 114569. doi:10.1016/j.apenergy.2020.114569
  • Hu, X., Feng, F., Liu, K., Zhang, L., Xie, J., & Liu, B. (2019). State estimation for advanced battery management: Key challenges and future trends. Renewable and Sustainable Energy Reviews, 114, 109334. doi:10.1016/j.rser.2019.109334
  • Hu, X., Zheng, Y., Howey, D. A., Perez, H., Foley, A., & Pecht, M. (2020). Battery warm-up methodologies at subzero temperatures for automotive applications: Recent advances and perspectives. Progress in Energy and Combustion Science, 77, 100806. doi:10.1016/j.pecs.2019.100806
  • Hu Xiaosong, Sun Fengchun, Zou Yuan, & Peng Huei. (2011). Online estimation of an electric vehicle Lithium-Ion battery using recursive least squares with forgetting. Proceedings of the 2011 American Control Conference, 935-940. doi:10.1109/ACC.2011.5991260
  • Ianniciello, L., Biwolé, P. H., & Achard, P. (2018). Electric vehicles batteries thermal management systems employing phase change materials. Journal of Power Sources, 378, 383-403. doi:10.1016/j.jpowsour.2017.12.071
  • Jaguemont, J., Boulon, L., & Dubé, Y. (2016). A comprehensive review of lithium-ion batteries used in hybrid and electric vehicles at cold temperatures. Applied Energy, 164, 99-114. doi:10.1016/j.apenergy.2015.11.034
  • Jaguemont, Joris, & Van Mierlo, J. (2020). A comprehensive review of future thermal management systems for battery-electrified vehicles. Journal of Energy Storage, 31, 101551. doi:10.1016/j.est.2020.101551
  • Ji, Y., & Wang, C. Y. (2013). Heating strategies for Li-ion batteries operated from subzero temperatures. Electrochimica Acta, 107, 664-674. doi:10.1016/j.electacta.2013.03.147
  • Jiang, C., Taylor, A., Duan, C., & Bai, K. (2013). Extended Kalman Filter based battery state of charge (SOC) estimation for electric vehicles. 2013 IEEE Transportation Electrification Conference and Expo (ITEC), 1-5. doi:10.1109/ITEC.2013.6573477
  • Jiménez-Bermejo, D., Fraile-Ardanuy, J., Castaño-Solis, S., Merino, J., & Álvaro-Hermana, R. (2018). Using Dynamic Neural Networks for Battery State of Charge Estimation in Electric Vehicles. Procedia Computer Science, 130, 533-540. doi:10.1016/j.procs.2018.04.077
  • Juang, L. W., Kollmeyer, P. J., Jahns, T. M., & Lorenz, R. D. (2012). Implementation of online battery state-of-power and state-of-function estimation in electric vehicle applications. 2012 IEEE Energy Conversion Congress and Exposition, ECCE 2012, 1819-1826. doi:10.1109/ECCE.2012.6342591
  • Jun Xu, Mi, C. C., Binggang Cao, Junjun Deng, Zheng Chen, & Siqi Li. (2014). The State of Charge Estimation of Lithium-Ion Batteries Based on a Proportional-Integral Observer. IEEE Transactions on Vehicular Technology, 63(4), 1614-1621. doi:10.1109/TVT.2013.2287375
  • Jung, D. Y., Lee, B. H., & Kim, S. W. (2002). Development of battery management system for nickel-metal hydride batteries in electric vehicle applications. Journal of Power Sources, 109(1), 1-10. doi:10.1016/S0378-7753(02)00020-4
  • Karabeyoğlu, E. D., Güzel, E., Kılıç, H., Sarıoğlu, B., Gökdel, Y. D., & Serincan, M. F. (2019). Investigation and Design of an Active Cell Balancing System for Li-İon Batteries. Mühendislik Bilimleri ve Tasarım Dergisi, 7(4), 869-877. doi:10.21923/jesd.553295
  • Kim, I.-S. (2006). The novel state of charge estimation method for lithium battery using sliding mode observer. Journal of Power Sources, 163(1), 584-590. doi:10.1016/j.jpowsour.2006.09.006
  • Kıvrak, S., Özer, T., & Oğuz, Y. (2020). Can Bus Based BMS Control Card Design And Implementation By Using STM32f103 Series Microcontroller. Engineering Sciences, 15(1), 27-33. doi:10.12739/NWSA.2020.15.1.1A0448
  • Kong, X., Zheng, Y., Ouyang, M., Li, X., Lu, L., & Li, J. (2017). Signal synchronization for massive data storage in modular battery management system with controller area network. Applied Energy, 197, 52-62. doi:10.1016/j.apenergy.2017.04.002
  • Li, Yanwen, Wang, C., & Gong, J. (2016). A combination Kalman filter approach for State of Charge estimation of lithium-ion battery considering model uncertainty. Energy, 109, 933-946. doi:10.1016/j.energy.2016.05.047
  • Li, Yi, Liu, K., Foley, A. M., Zülke, A., Berecibar, M., Nanini-Maury, E., Van Mierlo, J., & Hoster, H. E. (2019). Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review. Renewable and Sustainable Energy Reviews, 113, 109254. doi:10.1016/j.rser.2019.109254
  • Li, Yigang, Chen, J., & Lan, F. (2020). Enhanced online model identification and state of charge estimation for lithium-ion battery under noise corrupted measurements by bias compensation recursive least squares. Journal of Power Sources, 456, 227984. doi:10.1016/j.jpowsour.2020.227984
  • Li, Yue, Chattopadhyay, P., Xiong, S., Ray, A., & Rahn, C. D. (2016). Dynamic data-driven and model-based recursive analysis for estimation of battery state-of-charge. Applied Energy, 184, 266-275. doi:10.1016/j.apenergy.2016.10.025
  • Li, Zhe, Huang, J., Liaw, B. Y., & Zhang, J. (2017). On state-of-charge determination for lithium-ion batteries. Journal of Power Sources, 348, 281-301. doi:10.1016/j.jpowsour.2017.03.001
  • Li, Zhenhe, Khajepour, A., & Song, J. (2019). A comprehensive review of the key technologies for pure electric vehicles. Energy, 182, 824-839. doi:10.1016/j.energy.2019.06.077
  • Lin, C., Mu, H., Xiong, R., & Cao, J. (2017). Multi-model probabilities based state fusion estimation method of lithium-ion battery for electric vehicles: State-of-energy. Applied Energy, 194, 560-568. doi:10.1016/j.apenergy.2016.05.065
  • Lin, C., Mu, H., Xiong, R., & Shen, W. (2016). A novel multi-model probability battery state of charge estimation approach for electric vehicles using H-infinity algorithm. Applied Energy, 166, 76-83. doi:10.1016/j.apenergy.2016.01.010
  • Lipu, M. S. H., Hannan, M. A., Hussain, A., Hoque, M. M., Ker, P. J., Saad, M. H. M., & Ayob, A. (2018). A review of state of health and remaining useful life estimation methods for lithium-ion battery in electric vehicles: Challenges and recommendations. Journal of Cleaner Production, 205, 115-133. doi:10.1016/j.jclepro.2018.09.065
  • Liu, H., Wei, Z., He, W., & Zhao, J. (2017). Thermal issues about Li-ion batteries and recent progress in battery thermal management systems: A review. Energy Conversion and Management, 150, 304-330. doi:10.1016/j.enconman.2017.08.016
  • Liu, K., Li, K., Peng, Q., & Zhang, C. (2019). A brief review on key technologies in the battery management system of electric vehicles. Frontiers of Mechanical Engineering, 14(1), 47-64. doi:10.1007/s11465-018-0516-8
  • Liu, X., Wu, J., Zhang, C., & Chen, Z. (2014). A method for state of energy estimation of lithium-ion batteries at dynamic currents and temperatures. Journal of Power Sources, 270, 151-157. doi:10.1016/j.jpowsour.2014.07.107
  • Lopez, C. F., Jeevarajan, J. A., & Mukherjee, P. P. (2016). Evaluation of Combined Active and Passive Thermal Management Strategies for Lithium-Ion Batteries. Journal of Electrochemical Energy Conversion and Storage, 13(3), 1-10. doi:10.1115/1.4035245
  • Lu, J., Chen, Z., Yang, Y., & L.V., M. (2018). Online Estimation of State of Power for Lithium-Ion Batteries in Electric Vehicles Using Genetic Algorithm. IEEE Access, 6, 20868-20880. doi:10.1109/ACCESS.2018.2824559
  • Lu, L., Han, X., Li, J., Hua, J., & Ouyang, M. (2013). A review on the key issues for lithium-ion battery management in electric vehicles. Journal of Power Sources, 226, 272-288. doi:10.1016/j.jpowsour.2012.10.060
  • Lu, M., Zhang, X., Ji, J., Xu, X., & Zhang, Y. (2020). Research progress on power battery cooling technology for electric vehicles. Journal of Energy Storage, 27, 101155. doi:10.1016/j.est.2019.101155
  • Lukic, S., & Emadi, A. (2008). Charging ahead. IEEE Industrial Electronics Magazine, 2(4), 22-31. doi:10.1109/MIE.2008.930361 Ma, Y., Li, B., Xie, Y., & Chen, H. (2016). Estimating the State of Charge of Lithium-ion Battery based on Sliding Mode Observer. IFAC-PapersOnLine, 49(11), 54-61. doi:10.1016/j.ifacol.2016.08.009
  • Mamadou, K., Lemaire, E., Delaille, A., Riu, D., Hing, S. E., & Bultel, Y. (2012). Definition of a State-of-Energy Indicator (SoE) for Electrochemical Storage Devices: Application for Energetic Availability Forecasting. Journal of The Electrochemical Society, 159(8), A1298-A1307. doi:10.1149/2.075208jes
  • Martinez, A. C., Sorlien, D., Goodrich, R., Chandler, L., & Magnuson, D. (2005). Multi-cell Li-Ion Battery Packs. An167. www.xicor.com
  • Maskey, M., Patten, M., Vines, D., & Maxwell, T. (1999). Intelligent battery management system for electric and hybrid electric vehicles. IEEE VTS 50th Vehicular Technology Conference, VTC 1999-Fall, 2, 1389-1391. doi:10.1109/vetec.1999.780575
  • Mastali, M., Vazquez-Arenas, J., Fraser, R., Fowler, M., Afshar, S., & Stevens, M. (2013). Battery state of the charge estimation using Kalman filtering. Journal of Power Sources, 239, 294-307. doi:10.1016/j.jpowsour.2013.03.131
  • Meissner, E., & Richter, G. (2003). Battery Monitoring and Electrical Energy Management precondition for future vehicle electric power systems. Journal of Power Sources, 116(1-2), 79-98. doi:10.1016/S0378-7753(02)00713-9
  • Meng, J., Ricco, M., Acharya, A. B., Luo, G., Swierczynski, M., Stroe, D.-I., & Teodorescu, R. (2018). Low-complexity online estimation for LiFePO4 battery state of charge in electric vehicles. Journal of Power Sources, 395, 280-288. doi:10.1016/j.jpowsour.2018.05.082
  • Messier, P., Lebel, F. A., Rouleau, J., & Trovão, J. P. F. (2019). Multi-cell emulation for battery management system validation. 2018 IEEE Vehicle Power and Propulsion Conference, VPPC 2018 - Proceedings. doi:10.1109/VPPC.2018.8604959
  • Messier, P., Nguyễn, B. H., LeBel, F. A., & Trovão, J. P. F. (2020). Disturbance observer-based state-of-charge estimation for Li-ion battery used in light electric vehicles. Journal of Energy Storage, 27, 101144. doi:10.1016/j.est.2019.101144
  • Moore, S. W., & Schneider, P. J. (2001). A review of cell equalization methods for lithium ion and lithium polymer battery systems. SAE Technical Papers, 724. doi:10.4271/2001-01-0959
  • Navet, N., Song, Y., Simonot-Lion, F., & Wilwert, C. (2005). Trends in automotive communication systems. Proceedings of the IEEE, 93(6), 1204-1222. doi:10.1109/JPROC.2005.849725
  • Ng, K. S., Moo, C.-S., Chen, Y.-P., & Hsieh, Y.-C. (2009). Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries. Applied Energy, 86(9), 1506-1511. doi:10.1016/j.apenergy.2008.11.021
  • Omariba, Z. B., Zhang, L., & Sun, D. (2019). Review of Battery Cell Balancing Methodologies for Optimizing Battery Pack Performance in Electric Vehicles. IEEE Access, 7, 129335-129352. doi:10.1109/ACCESS.2019.2940090
  • Pan, H., Lü, Z., Lin, W., Li, J., & Chen, L. (2017). State of charge estimation of lithium-ion batteries using a grey extended Kalman filter and a novel open-circuit voltage model. Energy, 138, 764-775. doi:10.1016/j.energy.2017.07.099
  • Park, S., Ahn, J., Kang, T., Park, S., Kim, Y., Cho, I., & Kim, J. (2020). Review of state-of-the-art battery state estimation technologies for battery management systems of stationary energy storage systems. Journal of Power Electronics, 20(6), 1526-1540. doi:10.1007/s43236-020-00122-7
  • Pesaran, A. (2001). Battery Thermal Management in EVs and HEVs: Issues and Solutions. Advanced Automotive Battery Conference, January 2001, 10.
  • Pesaran, A. A., Burch, S., & Keyser, M. (1999). An Approach for Designing Thermal Management Systems for Electric and Hybrid Vehicle Battery Packs Preprint. The Fourth Vehicle Thermal Management Systems Conference and Exhibition 24-27, January, 1-18.
  • Plett, G. L. (2004). Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs. Journal of Power Sources, 134(2), 252-261. doi:10.1016/j.jpowsour.2004.02.031
  • Qaisar, S. M. (2020). Event-Driven Approach for an Efficient Coulomb Counting Based Li-Ion Battery State of Charge Estimation. Procedia Computer Science, 168, 202-209. doi:10.1016/j.procs.2020.02.268
  • Qi, J., & Dah-Chuan Lu, D. (2014). Review of battery cell balancing techniques. 2014 Australasian Universities Power Engineering Conference, AUPEC 2014 - Proceedings, October, 2-7. doi:10.1109/AUPEC.2014.6966514
  • Qian, K., Huang, B., Ran, A., He, Y. B., Li, B., & Kang, F. (2019). State-of-health (SOH) evaluation on lithium-ion battery by simulating the voltage relaxation curves. Electrochimica Acta, 303, 183-191. doi:10.1016/j.electacta.2019.02.055
  • Qiu, X., Wu, W., & Wang, S. (2020). Remaining useful life prediction of lithium-ion battery based on improved cuckoo search particle filter and a novel state of charge estimation method. Journal of Power Sources, 450, 227700. doi:10.1016/j.jpowsour.2020.227700
  • Rahimi-Eichi, H., Ojha, U., Baronti, F., & Chow, M. Y. (2013). Battery management system: An overview of its application in the smart grid and electric vehicles. IEEE Industrial Electronics Magazine, 7(2), 4-16. doi:10.1109/MIE.2013.2250351
  • Rajalakshmi, M., & Razia Sultana, W. (2020). Intelligent Hybrid Battery Management System for Electric Vehicle. Içinde A. Chitra, P. Sanjeevikumar, J. B. Holm‐Nielsen & S. Himavathi (Eds.) Artificial Intelligent Techniques for Electric and Hybrid Electric Vehicles (ss. 179-206). Wiley. doi:10.1002/9781119682035.ch10
  • Ramadan, H. S., Becherif, M., & Claude, F. (2017). Extended kalman filter for accurate state of charge estimation of lithium-based batteries: a comparative analysis. International Journal of Hydrogen Energy, 42(48), 29033-29046. doi:10.1016/j.ijhydene.2017.07.219
  • Rao, Z., & Wang, S. (2011). A review of power battery thermal energy management. Renewable and Sustainable Energy Reviews, 15(9), 4554-4571. doi:10.1016/j.rser.2011.07.096
  • Rezvanizaniani, S. M., Liu, Z., Chen, Y., & Lee, J. (2014). Review and recent advances in battery health monitoring and prognostics technologies for electric vehicle (EV) safety and mobility. Journal of Power Sources, 256, 110-124. doi:10.1016/j.jpowsour.2014.01.085
  • Rivera-Barrera, J., Muñoz-Galeano, N., & Sarmiento-Maldonado, H. (2017). SoC Estimation for Lithium-ion Batteries: Review and Future Challenges. Electronics, 6(4), 102. doi:10.3390/electronics6040102
  • Sabbah, R., Kizilel, R., Selman, J. R., & Al-Hallaj, S. (2008). Active (air-cooled) vs. passive (phase change material) thermal management of high power lithium-ion packs: Limitation of temperature rise and uniformity of temperature distribution. Journal of Power Sources, 182(2), 630-638. doi:10.1016/j.jpowsour.2008.03.082
  • Salkind, A. J., Fennie, C., Singh, P., Atwater, T., & Reisner, D. E. (1999). Determination of state-of-charge and state-of-health of batteries by fuzzy logic methodology. Journal of Power Sources, 80(1-2), 293-300. doi:10.1016/S0378-7753(99)00079-8
  • Saraf, P. (2012). The Traditional and New Generation in-vehicle Networks in Automotive Field. International Conference on Advances in Computer, Electronics and Electrical Engineering, March, 978-981. doi:10.3850/978-981-07-1847-3
  • Sarikurt, T., & Balikçi, A. (2017). Tam elektrikli araçlar için özgün bir enerji yönetim sistemi uygulaması. Journal of the Faculty of Engineering and Architecture of Gazi University, 32(2), 323--333. doi:10.17341/gazimmfd.322153
  • Saw, L. H., Ye, Y., & Tay, A. A. O. (2016). Integration issues of lithium-ion battery into electric vehicles battery pack. Journal of Cleaner Production, 113, 1032-1045. doi:10.1016/j.jclepro.2015.11.011
  • Shen, M., & Gao, Q. (2019). A review on battery management system from the modeling efforts to its multiapplication and integration. International Journal of Energy Research, 43(10), 5042-5075. doi:10.1002/er.4433
  • Shen, P., Ouyang, M., Lu, L., Li, J., & Feng, X. (2018). The co-estimation of state of charge, state of health, and state of function for lithium-ion batteries in electric vehicles. IEEE Transactions on Vehicular Technology, 67(1), 92-103. doi:10.1109/TVT.2017.2751613
  • Shen, Y. (2010). Adaptive online state-of-charge determination based on neuro-controller and neural network. Energy Conversion and Management, 51(5), 1093-1098. doi:10.1016/j.enconman.2009.12.015
  • Shen, Y. (2018a). A chaos genetic algorithm based extended Kalman filter for the available capacity evaluation of lithium-ion batteries. Electrochimica Acta, 264, 400-409. doi:10.1016/j.electacta.2018.01.123
  • Shen, Y. (2018b). Improved chaos genetic algorithm based state of charge determination for lithium batteries in electric vehicles. Energy, 152, 576-585. doi:10.1016/j.energy.2018.03.174
  • Shen, Y. (2018c). Adaptive extended Kalman filter based state of charge determination for lithium-ion batteries. Electrochimica Acta, 283, 1432-1440. doi:10.1016/j.electacta.2018.07.078
  • Sheng, H., & Xiao, J. (2015). Electric vehicle state of charge estimation: Nonlinear correlation and fuzzy support vector machine. Journal of Power Sources, 281, 131-137. doi:10.1016/j.jpowsour.2015.01.145
  • Shrivastava, P., Soon, T. K., Idris, M. Y. I. Bin, & Mekhilef, S. (2019). Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries. Renewable and Sustainable Energy Reviews, 113, 109233. doi:10.1016/j.rser.2019.06.040
  • Siddique, A. R. M., Mahmud, S., & Heyst, B. Van. (2018). A comprehensive review on a passive (phase change materials) and an active (thermoelectric cooler) battery thermal management system and their limitations. Journal of Power Sources, 401, 224-237. doi:10.1016/j.jpowsour.2018.08.094
  • Singh, P., Fennie, C., & Reisner, D. (2004). Fuzzy logic modelling of state-of-charge and available capacity of nickel/metal hydride batteries. Journal of Power Sources, 136(2), 322-333. doi:10.1016/j.jpowsour.2004.03.035
  • Singh, P., Vinjamuri, R., Wang, X., & Reisner, D. (2006). Design and implementation of a fuzzy logic-based state-of-charge meter for Li-ion batteries used in portable defibrillators. Journal of Power Sources, 162(2), 829-836. doi:10.1016/j.jpowsour.2005.04.039
  • Steinhorst, S., Lukasiewycz, M., Narayanaswamy, S., Kauer, M., & Chakraborty, S. (2014). Smart cells for embedded battery management. Proceedings - 2nd IEEE International Conference on Cyber-Physical Systems, Networks, and Applications, CPSNA 2014, 59-64. doi:10.1109/CPSNA.2014.22
  • Steinhorst, S., Shao, Z., Chakraborty, S., Kauer, M., Li, S., Lukasiewycz, M., Narayanaswamy, S., Rafique, M. U., & Wang, Q. (2016). Distributed reconfigurable Battery System Management Architectures. Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC, 25-28 Jan, 429-434. doi:10.1109/ASPDAC.2016.7428049
  • Stuart, T. A., & Zhu, W. (2011). Modularized battery management for large lithium ion cells. Journal of Power Sources, 196(1), 458-464. doi:10.1016/j.jpowsour.2010.04.055
  • Stuart, T., Fang, F., Wang, X., Ashtiani, C., & Pesaran, A. (2002). A modular battery management system for HEVs. SAE Technical Papers, 111, 777-785. doi:10.4271/2002-01-1918
  • Sun, F., Hu, X., Zou, Y., & Li, S. (2011). Adaptive unscented Kalman filtering for state of charge estimation of a lithium-ion battery for electric vehicles. Energy, 36(5), 3531-3540. doi:10.1016/j.energy.2011.03.059
  • Teng, H., & Yeow, K. (2012). Design of direct and indirect liquid cooling systems for high-capacity, high-power lithium-ion battery packs. SAE International Journal of Alternative Powertrains, 1(2), 525-536. doi:10.4271/2012-01-2017
  • Thakur, A. K., Prabakaran, R., Elkadeem, M. R., Sharshir, S. W., Arıcı, M., Wang, C., Zhao, W., Hwang, J.-Y., & Saidur, R. (2020). A state of art review and future viewpoint on advance cooling techniques for Lithium–ion battery system of electric vehicles. Journal of Energy Storage, 32, 101771. doi:10.1016/j.est.2020.101771
  • Tian, H., Qin, P., Li, K., & Zhao, Z. (2020). A review of the state of health for lithium-ion batteries: Research status and suggestions. Journal of Cleaner Production, 261, 120813. doi:10.1016/j.jclepro.2020.120813
  • Tian, Y., Xia, B., Sun, W., Xu, Z., & Zheng, W. (2014). A modified model based state of charge estimation of power lithium-ion batteries using unscented Kalman filter. Journal of Power Sources, 270, 619-626. doi:10.1016/j.jpowsour.2014.07.143
  • Ting, T. O., Man, K. L., Lim, E. G., & Leach, M. (2014). Tuning of Kalman Filter Parameters via Genetic Algorithm for State-of-Charge Estimation in Battery Management System. The Scientific World Journal, 2014, 176052. doi:10.1155/2014/176052
  • TOGG Press Release. (2020). ‘More Than a Factory’ Construction starts at TOGG Gemlik. (Accessed:18/09/2020) https://www.togg.com.tr/Dosyalar/Press/togg-construction-pr.pdf
  • Ungurean, L., Cârstoiu, G., Micea, M. V., & Groza, V. (2017). Battery state of health estimation: a structured review of models, methods and commercial devices. International Journal of Energy Research, 41(2), 151-181. doi:10.1002/er.3598
  • Urbain, M., Rael, S., Davat, B., & Desprez, P. (2007). State Estimation of a Lithium-Ion Battery Through Kalman Filter. 2007 IEEE Power Electronics Specialists Conference, 2804-2810. doi:10.1109/PESC.2007.4342463
  • Väyrynen, A., & Salminen, J. (2012). Lithium ion battery production. Journal of Chemical Thermodynamics, 46, 80-85. doi:10.1016/j.jct.2011.09.005
  • Waag, W., Fleischer, C., & Sauer, D. U. (2014). Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles. Journal of Power Sources, 258, 321-339. doi:10.1016/j.jpowsour.2014.02.064
  • Wang, Q., Jiang, B., Li, B., & Yan, Y. (2016). A critical review of thermal management models and solutions of lithium-ion batteries for the development of pure electric vehicles. Renewable and Sustainable Energy Reviews, 64, 106-128. doi:10.1016/j.rser.2016.05.033
  • Wang, S.-L., Fernandez, C., Zou, C.-Y., Yu, C.-M., Li, X.-X., Pei, S.-J., & Xie, W. (2018). Open circuit voltage and state of charge relationship functional optimization for the working state monitoring of the aerial lithium-ion battery pack. Journal of Cleaner Production, 198, 1090-1104. doi:10.1016/j.jclepro.2018.07.030
  • Wang, Y., Tian, J., Sun, Z., Wang, L., Xu, R., Li, M., & Chen, Z. (2020). A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems. Renewable and Sustainable Energy Reviews, 131, 110015. doi:10.1016/j.rser.2020.110015
  • Wang, Y., Zhang, C., & Chen, Z. (2016). An adaptive remaining energy prediction approach for lithium-ion batteries in electric vehicles. Journal of Power Sources, 305, 80-88. doi:10.1016/j.jpowsour.2015.11.087
  • Wei, J., Dong, G., & Chen, Z. (2017). On-board adaptive model for state of charge estimation of lithium-ion batteries based on Kalman filter with proportional integral-based error adjustment. Journal of Power Sources, 365, 308-319. doi:10.1016/j.jpowsour.2017.08.101
  • Wu, S., Xiong, R., Li, H., Nian, V., & Ma, S. (2020). The state of the art on preheating lithium-ion batteries in cold weather. Journal of Energy Storage, 27, 101059. doi:10.1016/j.est.2019.101059
  • Wu, W., Wang, S., Wu, W., Chen, K., Hong, S., & Lai, Y. (2019). A critical review of battery thermal performance and liquid based battery thermal management. Energy Conversion and Management, 182, 262-281. doi:10.1016/j.enconman.2018.12.051
  • Wu, W., Yang, X., Zhang, G., Ke, X., Wang, Z., Situ, W., Li, X., & Zhang, J. (2016). An experimental study of thermal management system using copper mesh-enhanced composite phase change materials for power battery pack. Energy, 113, 909-916. doi:10.1016/j.energy.2016.07.119
  • Xia, B., Chen, C., Tian, Y., Sun, W., Xu, Z., & Zheng, W. (2014). A novel method for state of charge estimation of lithium-ion batteries using a nonlinear observer. Journal of Power Sources, 270, 359-366. doi:10.1016/j.jpowsour.2014.07.103
  • Xia, B., Lao, Z., Zhang, R., Tian, Y., Chen, G., Sun, Z., Wang, W., Sun, W., Lai, Y., Wang, M., & Wang, H. (2017). Online Parameter Identification and State of Charge Estimation of Lithium-Ion Batteries Based on Forgetting Factor Recursive Least Squares and Nonlinear Kalman Filter. Energies, 11(1), 3. doi:10.3390/en11010003
  • Xia, B., Zhang, Z., Lao, Z., Wang, W., Sun, W., Lai, Y., & Wang, M. (2018). Strong Tracking of a H-Infinity Filter in Lithium-Ion Battery State of Charge Estimation. Energies, 11(6), 1481. doi:10.3390/en11061481
  • Xia, G., Cao, L., & Bi, G. (2017). A review on battery thermal management in electric vehicle application. Journal of Power Sources, 367, 90-105. doi:10.1016/j.jpowsour.2017.09.046
  • Xing, Y., He, W., Pecht, M., & Tsui, K. L. (2014). State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatu res. Applied Energy, 113, 106-115. doi:10.1016/j.apenergy.2013.07.008
  • Xing, Y., Ma, E. W. M., Tsui, K. L., & Pecht, M. (2011). Battery management systems in electric and hybrid vehicles. Energies, 4(11), 1840-1857. doi:10.3390/en4111840
  • Xiong, R. (2020). Battery Management Algorithm for Electric Vehicles. Içinde Battery Management Algorithm for Electric Vehicles. Springer Singapore. doi:10.1007/978-981-15-0248-4
  • Xiong, R., Gong, X., Mi, C. C., & Sun, F. (2013). A robust state-of-charge estimator for multiple types of lithium-ion batteries using adaptive extended Kalman filter. Journal of Power Sources, 243, 805-816. doi:10.1016/j.jpowsour.2013.06.076
  • Xiong, R., Li, L., & Tian, J. (2018). Towards a smarter battery management system: A critical review on battery state of health monitoring methods. Journal of Power Sources, 405(5), 18-29. doi:10.1016/j.jpowsour.2018.10.019
  • Xiong, R., Yu, Q., & Wang, L. Y. (2017). Open circuit voltage and state of charge online estimation for lithium ion batteries. Energy Procedia, 142, 1902-1907. doi:10.1016/j.egypro.2017.12.388
  • Xiong, R., Yu, Q., Wang, L. Y., & Lin, C. (2017). A novel method to obtain the open circuit voltage for the state of charge of lithium ion batteries in electric vehicles by using H infinity filter. Applied Energy, 207, 346-353. doi:10.1016/j.apenergy.2017.05.136
  • Yang, D., Wang, Y., Pan, R., Chen, R., & Chen, Z. (2017). A Neural Network Based State-of-Health Estimation of Lithium-ion Battery in Electric Vehicles. Energy Procedia, 105, 2059-2064. doi:10.1016/j.egypro.2017.03.583
  • Yang, D., Wang, Y., Pan, R., Chen, R., & Chen, Z. (2018). State-of-health estimation for the lithium-ion battery based on support vector regression. Applied Energy, 227, 273-283. doi:10.1016/j.apenergy.2017.08.096
  • Yang, F., Zhang, S., Li, W., & Miao, Q. (2020). State-of-charge estimation of lithium-ion batteries using LSTM and UKF. Energy, 201, 117664. doi:10.1016/j.energy.2020.117664
  • Yang, S., Ling, C., Fan, Y., Yang, Y., Tan, X., & Dong, H. (2019). A review of lithium-ion battery thermal management system strategies and the evaluate criteria. International Journal of Electrochemical Science, 14(7), 6077-6107. doi:10.20964/2019.07.06
  • Yang, X., Chen, Y., Li, B., & Luo, D. (2020). Battery states online estimation based on exponential decay particle swarm optimization and proportional-integral observer with a hybrid battery model. Energy, 191, 116509. doi:10.1016/j.energy.2019.116509
  • Yatsui, M. W., & Bai, H. (2011). Kalman filter based state-of-charge estimation for lithium-ion batteries in hybrid electric vehicles using pulse charging. 2011 IEEE Vehicle Power and Propulsion Conference, 1-5. doi:10.1109/VPPC.2011.6042988
  • Ye, M., Guo, H., Xiong, R., & Yu, Q. (2018). A double-scale and adaptive particle filter-based online parameter and state of charge estimation method for lithium-ion batteries. Energy, 144, 789-799. doi:10.1016/j.energy.2017.12.061
  • Yenigün, M., & Utlu, Z. (2018). Elektrikli Araçlarda Kullanılan Batarya Soğutma Sistemlerinin İncelenmesi ve Değerlendirilmesi. Mühendis ve Makina Dergisi, 59(692), 35-47.
  • Yong, J. Y., Ramachandaramurthy, V. K., Tan, K. M., & Mithulananthan, N. (2015). A review on the state-of-the-art technologies of electric vehicle, its impacts and prospects. Renewable and Sustainable Energy Reviews, 49, 365-385. doi:10.1016/j.rser.2015.04.130
  • Yu, Z., Huai, R., & Xiao, L. (2015). State-of-charge estimation for lithium-ion batteries using a Kalman filter based on local linearization. Energies, 8(8), 7854-7873. doi:10.3390/en8087854
  • Zenati, A., Desprez, P., & Razik, H. (2010). Estimation of the SOC and the SOH of li-ion batteries, by combining impedance measurements with the fuzzy logic inference. IECON 2010 - 36th Annual Conference on IEEE Industrial Electronics Society, 1773-1778. doi:10.1109/IECON.2010.5675408
  • Zhang, Q., Cui, N., Li, Y., Duan, B., & Zhang, C. (2020). Fractional calculus based modeling of open circuit voltage of lithium-ion batteries for electric vehicles. Journal of Energy Storage, 27, 100945. doi:10.1016/j.est.2019.100945
  • Zhang, S., Guo, X., Dou, X., & Zhang, X. (2020a). A data-driven coulomb counting method for state of charge calibration and estimation of lithium-ion battery. Sustainable Energy Technologies and Assessments, 40, 100752. doi:10.1016/j.seta.2020.100752
  • Zhang, S., Guo, X., Dou, X., & Zhang, X. (2020b). A rapid online calculation method for state of health of lithium-ion battery based on coulomb counting method and differential voltage analysis. Journal of Power Sources, 479, 228740. doi:10.1016/j.jpowsour.2020.228740
  • Zhang, Xiujuan, Liu, P., & Wang, D. (2011). The design and implementation of smart battery management system balance technology. Journal of Convergence Information Technology, 6(5), 108-116. doi:10.4156/jcit.vol6.issue5.12
  • Zhang, Xu, Wang, Y., Liu, C., & Chen, Z. (2018). A novel approach of battery pack state of health estimation using artificial intelligence optimization algorithm. Journal of Power Sources, 376, 191-199. doi:10.1016/j.jpowsour.2017.11.068
  • Zhao, L., Liu, Z., & Ji, G. (2018). Lithium-ion battery state of charge estimation with model parameters adaptation using H∞ extended Kalman Filter. Control Engineering Practice, 81, 114-128. doi:10.1016/j.conengprac.2018.09.010
  • Zheng, F., Xing, Y., Jiang, J., Sun, B., Kim, J., & Pecht, M. (2016). Influence of different open circuit voltage tests on state of charge online estimation for lithium-ion batteries. Applied Energy, 183, 513-525. doi:10.1016/j.apenergy.2016.09.010
  • Zhi, L., Peng, Z., Zhifu, W., Qiang, S., & Yinan, R. (2017). State of Charge Estimation for Li-ion Battery Based on Extended Kalman Filter. Energy Procedia, 105, 3515-3520. doi:10.1016/j.egypro.2017.03.806
  • Zhou, F., Wang, L., Lin, H., & Lv, Z. (2013). High accuracy state-of-charge online estimation of EV/HEV lithium batteries based on Adaptive Wavelet Neural Network. 2013 IEEE ECCE Asia Downunder, 513-517. doi:10.1109/ECCE-Asia.2013.6579145
  • Zhu, H., Wu, Z., Wang, D., & Sun, J. (2013). Design and implementation of distributed battery management system. Advanced Materials Research, 608-609, 1039-1042. doi:10.4028/www.scientific.net/AMR.608-609.1039
  • Zhu, Q., Xiong, N., Yang, M.-L., Huang, R.-S., & Hu, G.-D. (2017). State of Charge Estimation for Lithium-Ion Battery Based on Nonlinear Observer: An H∞ Method. Energies, 10(5), 679. doi:10.3390/en10050679
  • Zhu, R., Duan, B., Zhang, J., Zhang, Q., & Zhang, C. (2020). Co-estimation of model parameters and state-of-charge for lithium-ion batteries with recursive restricted total least squares and unscented Kalman filter. Applied Energy, 277, 115494. doi:10.1016/j.apenergy.2020.115494
  • Zou, Y., Hu, X., Ma, H., & Li, S. E. (2015). Combined State of Charge and State of Health estimation over lithium-ion battery cell cycle lifespan for electric vehicles. Journal of Power Sources, 273, 793-803. doi:10.1016/j.jpowsour.2014.09.146
There are 195 citations in total.

Details

Primary Language Turkish
Journal Section Electrical & Electronics Engineering
Authors

Ramazan Menak 0000-0003-3223-4808

Teoman Karadağ 0000-0002-7682-7771

Mehmet Altuğ 0000-0002-4745-9164

Nusret Tan 0000-0002-1285-1991

Publication Date June 28, 2021
Submission Date February 25, 2021
Published in Issue Year 2021 Volume: 8 Issue: 2

Cite

APA Menak, R., Karadağ, T., Altuğ, M., Tan, N. (2021). Elektrikli Araçlarda Batarya Yönetim Sistemleri Üzerine Bir Derleme Çalışması. Gazi University Journal of Science Part A: Engineering and Innovation, 8(2), 234-275.