A comparative review of neural network approaches for advancements in state of charge estimation for electric vehicles
Main Article Content
Abstract
An electric vehicle (EV) operates using batteries and electric motors, serving as an eco-friendly alternative to traditional vehicles by cutting emissions and decreasing reliance on fossil fuels. EVs are pivotal in advancing environmental sustainability and aiding global efforts toward carbon neutrality. As the push for carbon neutrality and emission reduction intensifies, advancements in the electric vehicle sector become imperative, with lithium-ion batteries serving as key power sources. Accurate estimation of the state of charge (SOC) of these batteries is crucial for optimizing the performance and management of electric vehicles. This article delves into SOC estimation using neural network approaches, which utilize their advanced feature extraction and modelling capabilities to achieve high precision without requiring detailed knowledge of the battery's internal electrochemical processes. The concept of SOC is defined, and its relationship with battery aging is explored to establish a basis for further analysis. Recent studies are reviewed, categorizing neural network-based SOC estimation methods into three main types: recurrent neural networks, convolutional neural networks, and hybrid models. Additionally, it offers recommendations for the future development of intelligent battery management systems and SOC estimation methods. The insights from this review are intended to inspire researchers in the battery technology field and support the evolution of next-generation electric vehicles.
Article Details
Section
How to Cite
References
1. Asaad, M., Shrivastava, P., Alam, M.S., Rafat, Y. and Pillai, R.K., 2018. Viability of xEVs in India: A public opinion survey. In: ISGW 2017: Compendium of Technical Papers: 3rd International Conference and Exhibition on Smart Grids and Smart Cities, pp.165–178. https://doi.org/10.1007/978-981-10-8249-8_15
2. Bunsen, T., Cazzola, P., Gorner, M., Paoli, L., Scheffer, S., Schuitmaker, R., Tattini, J. and Teter, J., 2018. Global EV Outlook 2018: Towards Cross-Modal Electrification. Paris: International Energy Agency.
3. Monteiro, V., Gonçalves, H. and Afonso, J.L., 2011. Impact of electric vehicles on power quality in a smart grid context. In: 11th International Conference on Electrical Power Quality and Utilisation, pp.1–6. https://doi.org/10.1109/EPQU.2011.6128861
4. Jordehi, A.R., 2019. Optimisation of demand response in electric power systems: A review. Renewable and Sustainable Energy Reviews, 103, pp.308–319. https://doi.org/10.1016/j.rser.2018.12.054
5. Strbac, G., 2008. Demand side management: Benefits and challenges. Energy Policy, 36(12), pp.4419–4426. https://doi.org/10.1016/j.enpol.2008.09.030
6. Kempton, W. and Letendre, S.E., 1997. Electric vehicles as a new power source for electric utilities. Transportation Research Part D: Transport and Environment, 2(3), pp.157–175. https://doi.org/10.1016/S1361-9209(97)00001-1
7. Pasaoglu, G., Fiorello, D., Martino, A., Zani, L., Zubaryeva, A. and Thiel, C., 2014. Travel patterns and the potential use of electric cars: Results from a direct survey in six European countries. Technological Forecasting and Social Change, 87, pp.51–59. https://doi.org/10.1016/j.techfore.2013.10.022
8. Tushar, W., Yuen, C., Mohsenian-Rad, H., Saha, T., Poor, H.V. and Wood, K.L., 2018. Transforming energy networks via peer-to-peer energy trading: The potential of game-theoretic approaches. IEEE Signal Processing Magazine, 35(4), pp.90–111. https://doi.org/10.1109/MSP.2018.2818327
9. Woo, J., Choi, H. and Ahn, J., 2017. Well-to-wheel analysis of greenhouse gas emissions for electric vehicles based on electricity generation mix: A global perspective. Transportation Research Part D: Transport and Environment, 51, pp.340–350. https://doi.org/10.1016/j.trd.2017.01.005
10. Saber, A.Y. and Venayagamoorthy, G.K., 2011. Plug-in vehicles and renewable energy sources for cost and emission reductions. IEEE Transactions on Industrial Electronics, 58(4), pp.1229–1238. https://doi.org/10.1109/TIE.2010.2047828
11. Plett, G.L., 2004. Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 1. Background. Journal of Power Sources, 134(2), pp.252–261. https://doi.org/10.1016/j.jpowsour.2004.02.003
12. Hu, X., Li, S. and Peng, H., 2012. A comparative study of equivalent circuit models for Li-ion batteries. Journal of Power Sources, 198, pp.359–367. https://doi.org/10.1016/j.jpowsour.2011.10.013
13. He, H., Xiong, R. and Fan, J., 2011. Evaluation of lithium-ion battery equivalent circuit models for state of charge estimation by an experimental approach. Energies, 4(4), pp.582–598. https://doi.org/10.3390/en4040582
14. Ramadesigan, V., Northrop, P.W.C., De, S., Santhanagopalan, S., Braatz, R.D. and Subramanian, V.R., 2012. Modeling and simulation of lithium-ion batteries from a systems engineering perspective. Journal of The Electrochemical Society, 159(3), pp.R31–R45. https://doi.org/10.1149/2.018203jes
15. Zhao, Z., Gong, F., Zhang, S. and Li, S., 2012. Poly(arylene ether sulfone)s ionomers containing quaternized triptycene groups for alkaline fuel cells. Journal of Power Sources, 218, pp.368–374. https://doi.org/10.1016/j.jpowsour.2012.07.011
16. Santhanagopalan, S., Guo, Q., Ramadass, P. and White, R.E., 2006. Review of models for predicting the cycling performance of lithium-ion batteries. Journal of Power Sources, 156(2), pp.620–628. https://doi.org/10.1016/j.jpowsour.2005.05.070
17. Kim, I. and Moon, S., 2018. Machine learning approaches to battery state of charge (SOC) and state of health (SOH) estimation: A review. Applied Energy, 228, pp.2144–2156. https://doi.org/10.1016/j.apenergy.2018.07.037
18. Xiong, R., Sun, F., Gong, X. and He, H., 2014. Adaptive state of charge estimator for lithium-ion cells series battery pack in electric vehicles. Journal of Power Sources, 242, pp.699–713. https://doi.org/10.1016/j.jpowsour.2013.12.049
19. Zhang, C., Ai, X. and Li, W., 2020. State-of-charge estimation for lithium-ion battery using data-driven approaches. Journal of Energy Storage, 32, Article 101977. https://doi.org/10.1016/j.est.2020.101977
20. Moura, S.J., Forman, J.C. and Krstic, M., 2014. Optimal charging of batteries with electrochemical aging. IEEE Transactions on Vehicular Technology, 63(7), pp.3058–3064. https://doi.org/10.1109/TVT.2014.2317152
21. Wang, Q., Sun, J. and Yang, K., 2019. A hybrid method combining Coulomb counting and artificial neural network for state of charge estimation of lithium-ion batteries. Journal of Energy Storage, 25, Article 100819. https://doi.org/10.1016/j.est.2019.100819
22. Singh, P. and Rathore, P.K., 2020. Internet of Things (IoT) in electric vehicles: A study. Journal of Electronic Science and Technology, 18(2), Article 100016. https://doi.org/10.1016/j.jnlest.2020.100016
23. Hu, X., Jiang, J. and Cao, D., 2017. Battery management systems in electric and hybrid vehicles. Energies, 10(9), Article 1207. https://doi.org/10.3390/en10091207
24. Chacko, S. and Chung, Y.G., 2012. Thermal modelling of Li-ion polymer battery for electric vehicle drive cycles. Journal of Power Sources, 213, pp.296–303. https://doi.org/10.1016/j.jpowsour.2012.04.016
25. Jiang, Z., Zhao, Y. and Chen, M., 2019. Battery state estimation using Kalman filter and machine learning. Journal of Power Sources, 429, pp.43–52. https://doi.org/10.1016/j.jpowsour.2019.04.098
26. Singh, M. and Verma, A., 2021. Data-driven modeling and machine learning for battery state of charge estimation: A review. Journal of Energy Storage, 42, Article 102967. https://doi.org/10.1016/j.est.2021.102967
27. Zhang, J., Zhang, C. and He, H., 2021. A big data-driven approach to enhance battery state of charge estimation accuracy for electric vehicles. Applied Energy, 304, Article 117829. https://doi.org/10.1016/j.apenergy.2021.117829
28. Lin, C., Zhao, Y. and Huang, J., 2019. Data-driven prediction and estimation for battery state of charge and state of health: A review. Renewable and Sustainable Energy Reviews, 113, Article 109254. https://doi.org/10.1016/j.rser.2019.109254
29. Chen, L. and Li, Y., 2017. Road sign detection and recognition using deep learning methods. IEEE Transactions on Intelligent Transportation Systems, 18(4), pp.1018–1028. https://doi.org/10.1109/TITS.2016.2590942
30. He, K., Zhang, X., Ren, S. and Sun, J., 2016. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.770–778. https://doi.org/10.1109/CVPR.2016.90
31. Redmon, J. and Farhadi, A., 2018. YOLOv3: An incremental improvement. arXiv Preprint, arXiv:1804.02767. https://doi.org/10.48550/arXiv.1804.02767
32. Zhang, L., Xu, L. and Cheng, L., 2019. Battery state of charge estimation using convolutional neural networks. Journal of Power Sources, 436, Article 226878. https://doi.org/10.1016/j.jpowsour.2019.226878
33. Yang, X. and Wang, Z., 2020. Convolutional neural networks-based state of health estimation for lithium-ion batteries. IEEE Access, 8, pp.78160–78167. https://doi.org/10.1109/ACCESS.2020.2990203
34. Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H. and Bengio, Y., 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv Preprint, arXiv:1406.1078. https://doi.org/10.48550/arXiv.1406.1078
35. Wu, X., Wang, K., Zhang, Y. and He, X., 2018. Energy consumption prediction for electric vehicles based on deep learning. IEEE Transactions on Industrial Informatics, 14(9), pp.4089–4098. https://doi.org/10.1109/TII.2018.2794996
36. Zhang, Y. and Zhao, J., 2021. Real-time prediction of energy consumption for electric vehicles based on LSTM recurrent neural networks. IEEE Transactions on Vehicular Technology, 70(4), pp.3022–3031. https://doi.org/10.1109/TVT.2021.3059916
37. Li, Y., Zhang, Y. and Luo, Y., 2019. Battery life prediction using recurrent neural networks. IEEE Access, 7, pp.24271–24282. https://doi.org/10.1109/ACCESS.2019.2894093
38. Liu, D. and Hu, X., 2019. Remaining useful life prediction of lithium-ion batteries based on health indicator and GRU. IEEE Transactions on Industrial Electronics, 67(11), pp.10164–10173. https://doi.org/10.1109/TIE.2019.2953791
39. Doshi, A. and Trivedi, M.M., 2010. A study of driver behaviors through the analysis of driver interactions with the steering wheel. IEEE Transactions on Intelligent Transportation Systems, 12(2), pp.417–430. https://doi.org/10.1109/TITS.2010.2096818
40. Yoon, J., Choi, S. and Kim, H.J., 2019. Driver behavior prediction and risk assessment based on multi-sensor data fusion. IEEE Transactions on Intelligent Transportation Systems, 21(5), pp.2144–2153. https://doi.org/10.1109/TITS.2019.2935739
41. Chen, C. and Zhou, Z., 2017. Large-scale real-time data acquisition for autonomous driving. IEEE Internet of Things Journal, 4(4), pp.766–778. https://doi.org/10.1109/JIOT.2017.2691605
42. Simonyan, K. and Zisserman, A., 2015. Very deep convolutional networks for large-scale image recognition. In: Proceedings of the International Conference on Learning Representations (ICLR). https://doi.org/10.48550/arXiv.1409.1556
43. Goodfellow, I., Bengio, Y. and Courville, A., 2016. Deep Learning. Cambridge, MA: MIT Press. https://doi.org/10.1038/nmeth.3707
44. Pan, S.J. and Yang, Q., 2010. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), pp.1345–1359. https://doi.org/10.1109/TKDE.2009.191
45. Chung, Y.-W., Khaki, B., Li, T., Chu, C. and Gadh, R., 2019. Ensemble machine learning-based algorithm for electric vehicle user behaviour prediction. Applied Energy, 254, Article 113732. https://doi.org/10.1016/j.apenergy.2019.113732
46. Lee, Z.J., Li, T. and Low, S.H., 2019. ACN-data: Analysis and applications of an open EV charging dataset. In: Proceedings of the 10th ACM International Conference on Future Energy Systems, pp.139–149. https://doi.org/10.1145/3307772.3331011
47. Frendo, O., Gaertner, N. and Stuckenschmidt, H., 2021. Improving smart charging prioritization by predicting electric vehicle departure time. IEEE Transactions on Intelligent Transportation Systems, 22(10), pp.6646–6653. https://doi.org/10.1109/TITS.2020.2992258
48. Xiong, Y., Chu, C.C., Gadh, R. and Wang, B., 2017. Distributed optimal vehicle grid integration strategy with user behavior prediction. In: 2017 IEEE Power and Energy Society General Meeting, pp.1–5. https://doi.org/10.1109/PESGM.2017
49. Xu, Z., 2017. Forecasting Electric Vehicle Arrival and Departure Time on UCSD Campus Using Support Vector Machines. PhD Dissertation. University of California, San Diego, USA.
50. Ai, S., Chakravorty, A. and Rong, C., 2018. Household EV charging demand prediction using machine and ensemble learning. In: 2018 IEEE International Conference on Energy Internet (ICEI), pp.163–168.
51. Majidpour, M., Qiu, C., Chu, P., Gadh, R. and Pota, H.R., 2015. Fast prediction for sparse time series: Demand forecast of EV charging stations for cell phone applications. IEEE Transactions on Industrial Informatics, 11(1), pp.242–250. https://doi.org/10.1109/TII.2014.2357157
52. Majidpour, M., Qiu, C., Chu, P., Gadh, R. and Pota, H.R., 2014. A novel forecasting algorithm for electric vehicle charging stations. In: 2014 International Conference on Connected Vehicles and Expo (ICCVE), pp.1035–1040. https://doi.org/10.1109/ICCVE.2014.7297504
53. Bokde, N., Beck, M.W., Álvarez, F.M. and Kulat, K., 2018. A novel imputation methodology for time series based on pattern sequence forecasting. Pattern Recognition Letters, 116, pp.88–96. https://doi.org/10.1016/j.patrec.2018.04.027
54. Majidpour, M., 2016. Time Series Prediction for Electric Vehicle Charging Load and Solar Power Generation in the Context of Smart Grid. PhD Dissertation. University of California, Los Angeles, USA.
55. Yang, Y., Tan, Z. and Ren, Y., 2020. Research on factors that influence the fast charging behavior of private battery electric vehicles. Sustainability, 12(8), Article 3439. https://doi.org/10.3390/su12083439
56. Venticinque, S. and Nacchia, S., 2019. Learning and prediction of E-car charging requirements for flexible loads shifting. In: Internet and Distributed Computing Systems: 12th International Conference, IDCS 2019, pp.284–293. Springer International Publishing. https://doi.org/10.1007/978-3-030-34914-1_27
57. Frendo, O., Graf, J., Gaertner, N. and Stuckenschmidt, H., 2020. Data-driven smart charging for heterogeneous electric vehicle fleets. Energy and AI, 1, Article 100007. https://doi.org/10.1016/j.egyai.2020.100007
58. Mies, J.J., Helmus, J.R. and Van den Hoed, R., 2018. Estimating the charging profile of individual charge sessions of electric vehicles in The Netherlands. World Electric Vehicle Journal, 9(2), Article 17. https://doi.org/10.3390/wevj9020017
59. Lu, Y., Li, Y., Xie, D., Wei, E., Bao, X., Chen, H. and Zhong, X., 2018. The application of improved random forest algorithm on the prediction of electric vehicle charging load. Energies, 11(11), Article 3207. https://doi.org/10.3390/en11113207
60. Helmus, J.R., Lees, M.H. and Van den Hoed, R., 2020. A data driven typology of electric vehicle user types and charging sessions. Transportation Research Part C: Emerging Technologies, 115, Article 102637. https://doi.org/10.1016/j.trc.2020.102637
61. Quirós-Tortós, J., Navarro-Espinosa, A., Ochoa, L.F. and Butler, T., 2018. Statistical representation of EV charging: Real data analysis and applications. In: 2018 Power Systems Computation Conference (PSCC), pp.1–7. https://doi.org/10.23919/PSCC.2018.8442988
62. Flammini, M.G., Prettico, G., Julea, A., Fulli, G., Mazza, A. and Chicco, G., 2019. Statistical characterisation of the real transaction data gathered from electric vehicle charging stations. Electric Power Systems Research, 166, pp.136–150. https://doi.org/10.1016/j.epsr.2018.11.027
63. Sadeghianpourhamami, N., Refa, N., Strobbe, M. and Develder, C., 2018. Quantitative analysis of electric vehicle flexibility: A data-driven approach. International Journal of Electrical Power & Energy Systems, 95, pp.451–462. https://doi.org/10.1016/j.ijepes.2017.08.026
64. Xiong, Y., Wang, B., Chu, C.C. and Gadh, R., 2018. Electric vehicle driver clustering using statistical model and machine learning. In: 2018 IEEE Power and Energy Society General Meeting (PESGM), pp.1–5. https://doi.org/10.1109/PESGM.2018.8586132
65. Shen, Y., Fang, W., Ye, F. and Kadoch, M., 2020. EV charging behavior analysis using hybrid intelligence for 5G smart grid. Electronics, 9(1), Article 80. https://doi.org/10.3390/electronics9010080
66. Gerossier, A., Girard, R. and Kariniotakis, G., 2019. Modeling and forecasting electric vehicle consumption profiles. Energies, 12(7), Article 1341. https://doi.org/10.3390/en12071341
67. Xiong, Y., Wang, B., Chu, C.C. and Gadh, R., 2018. Vehicle grid integration for demand response with mixture user model and decentralized optimization. Applied Energy, 231, pp.481–493. https://doi.org/10.1016/j.apenergy.2018.09.053
68. Xydas, E., Marmaras, C., Cipcigan, L.M., Jenkins, N., Carroll, S. and Barker, M., 2016. A data-driven approach for characterising the charging demand of electric vehicles: A UK case study. Applied Energy, 162, pp.763–771. https://doi.org/10.1016/j.apenergy.2015.10.116
69. Khaki, B., Chung, Y.W., Chu, C.C. and Gadh, R., 2018. Nonparametric user behavior prediction for distributed EV charging scheduling. In: 2018 IEEE Power and Energy Society General Meeting (PESGM), pp.1–5. IEEE. https://doi.org/10.1109/ACCESS.2018.2816382
70. Chen, Z., Zhang, Z., Zhao, J., Wu, B. and Huang, X., 2018. An analysis of the charging characteristics of electric vehicles based on measured data and its application. IEEE Access, 6, pp.24475–24487. https://doi.org/10.1109/ACCESS.2018.2816382
71. Chung, Y.W., Khaki, B., Chu, C.C. and Gadh, R., 2018. Electric vehicle user behavior prediction using hybrid kernel density estimator. In: 2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), pp.1–6. https://doi.org/10.1109/PMAPS.2018.8440454
72. Amini, M.H., Kargarian, A. and Karabasoglu, O., 2016. ARIMA-based decoupled time series forecasting of electric vehicle charging demand for stochastic power system operation. Electric Power Systems Research, 140, pp.378–390. https://doi.org/10.1016/j.epsr.2016.06.007
73. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S. and Bengio, Y., 2014. Generative adversarial nets. In: Advances in Neural Information Processing Systems, 27, pp.2672–2680. https://doi.org/10.5555/2969033.2969120
74. LeCun, Y., 2016. Unsupervised learning: Generative adversarial networks. MIT Deep Learning for Self-Driving Cars Lecture, Massachusetts Institute of Technology, USA.
75. Goodfellow, I., 2016. NIPS 2016 tutorial: Generative adversarial networks. arXiv Preprint, arXiv:1701.00160. https://doi.org/10.48550/arXiv.1701.00160