Elektrikli Araçlarda Batarya Yönetim Sistemleri Üzerine Bir Derleme Çalışması

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.

Keywords

• Elektrikli Araçlar
• Batarya Yönetim Sistemi
• Batarya Termal Yönetimi
• Batarya Durum Kestirimi
• Batarya Yönetim Sistemleri Türleri

A Review Study on Battery Management Systems in Electric Vehicles

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.

Keywords

• Electric Vehicles
• Battery Management System
• Battery Thermal Management
• Battery State Estimation
• Topology of the Battery Management Systems

References

1. 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
2. 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
3. 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
4. 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
5. 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
6. 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
7. Andrea, D. (2010). Battery Management Systems for Large Lithium-Ion Battery Packs. Artech hause.
8. 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

9. 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
10. 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.
11. 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
12. 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
13. 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
14. 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
15. 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
16. 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
17. 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
18. 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
19. 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
20. 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
21. 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
22. 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
23. 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
24. 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
25. 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
26. 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
27. 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
28. 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
29. 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
30. 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
31. 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
32. 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
33. 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
34. 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
35. 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
36. 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
37. 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
38. 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
39. 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
40. 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
41. Ekici, Y. E. (2019). Batarya yönetim sistemleri. Yüksek Lisans Tezi, İnönü Üniversitesi, Fen bilimleri Enstitüsü, Malatya, Türkiye.
42. 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
43. 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
44. 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
45. 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
46. 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
47. 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
48. 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
49. 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
50. 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
51. 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
52. 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
53. 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
54. 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
55. 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
56. 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
57. 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
58. 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
59. 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
60. 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
61. 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
62. 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
63. 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
64. 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
65. 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
66. 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
67. 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
68. 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
69. 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
70. 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
71. 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
72. 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
73. 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
74. 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
75. 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
76. 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
77. 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
78. 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
79. 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
80. 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
81. 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
82. 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
83. 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
84. 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
85. 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
86. 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
87. 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
88. 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
89. 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
90. 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
91. 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
92. 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
93. 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
94. 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
95. 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
96. Martinez, A. C., Sorlien, D., Goodrich, R., Chandler, L., & Magnuson, D. (2005). Multi-cell Li-Ion Battery Packs. An167. www.xicor.com
97. 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
98. 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
99. 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
100. 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
101. 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
102. 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
103. 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
104. 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
105. 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
106. 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
107. 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
108. 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
109. Pesaran, A. (2001). Battery Thermal Management in EVs and HEVs: Issues and Solutions. Advanced Automotive Battery Conference, January 2001, 10.
110. 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.
111. 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
112. 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
113. 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
114. 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
115. 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
116. 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
117. 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
118. 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
119. 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
120. 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
121. 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
122. 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
123. 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
124. 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
125. 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
126. 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
127. 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
128. 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
129. 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
130. 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
131. 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
132. 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
133. 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
134. 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
135. 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
136. 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
137. 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
138. 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
139. 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
140. 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
141. 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
142. 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
143. 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
144. 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
145. 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
146. 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
147. 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
148. 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
149. 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
150. 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
151. Väyrynen, A., & Salminen, J. (2012). Lithium ion battery production. Journal of Chemical Thermodynamics, 46, 80-85. doi:10.1016/j.jct.2011.09.005
152. 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
153. 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
154. 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
155. 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
156. 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
157. 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
158. 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
159. 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
160. 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
161. 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
162. 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
163. 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
164. 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
165. 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
166. 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
167. 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
168. 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
169. 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
170. 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
171. 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
172. 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
173. 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
174. 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
175. 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
176. 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
177. 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
178. 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
179. Yenigün, M., & Utlu, Z. (2018). Elektrikli Araçlarda Kullanılan Batarya Soğutma Sistemlerinin İncelenmesi ve Değerlendirilmesi. Mühendis ve Makina Dergisi, 59(692), 35-47.
180. 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
181. 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
182. 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
183. 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
184. 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
185. 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
186. 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
187. 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
188. 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
189. 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
190. 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
191. 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
192. 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
193. 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
194. 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
195. 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