REFERENCES
1. Xing, Y.; He, W.; Pecht, M.; Tsui, K. L. State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures. Appl. Energy. 2014, 113, 106-15.
2. Ng, K. S.; Moo, C.; Chen, Y.; Hsieh, Y. Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries. Appl. Energy. 2009, 86, 1506-11.
3. He, H.; Xiong, R.; Fan, J. Evaluation of lithium-ion battery equivalent circuit models for state of charge estimation by an experimental approach. Energies 2011, 4, 582-98.
4. Zheng, F.; Xing, Y.; Jiang, J.; Sun, B.; Kim, J.; Pecht, M. Influence of different open circuit voltage tests on state of charge online estimation for lithium-ion batteries. Appl. Energy. 2016, 183, 513-25.
5. He, L.; Guo, D. An improved coulomb counting approach based on numerical iteration for SOC estimation with real-time error correction ability. IEEE. Access. 2019, 7, 74274-82.
6. Liu, P.; Xu, R.; Liu, Y.; Lin, F.; Zhao, K. Computational modeling of heterogeneity of stress, charge, and cyclic damage in composite electrodes of Li-ion batteries. J. Electrochem. Soc. 2020, 167, 040527.
7. Xu, R.; Yang, Y.; Yin, F.; et al. Heterogeneous damage in Li-ion batteries: experimental analysis and theoretical modeling. J. Mech. Phys. Solids. 2019, 129, 160-83.
8. Ng, M.; Zhao, J.; Yan, Q.; Conduit, G. J.; Seh, Z. W. Predicting the state of charge and health of batteries using data-driven machine learning. Nat. Mach. Intell. 2020, 2, 161-70.
9. Hossain, L. M.; Hannan, M.; Hussain, A.; et al. Data-driven state of charge estimation of lithium-ion batteries: algorithms, implementation factors, limitations and future trends. J. Clean. Prod. 2020, 277, 124110.
10. Cover, T.; Hart, P. Nearest neighbor pattern classification. IEEE. Trans. Inf. Theory. 1967, 13, 21-7.
11. Salzberg, S. L. C4.5: programs for machine learning by j. ross QuinLan. Morgan Kaufmann Publishers, Inc., 1993. Mach. Learn. 1994, 16, 235-40.
12. Hearst, M.; Dumais, S.; Osuna, E.; Platt, J.; Scholkopf, B. Support vector machines. IEEE. Intell. Syst. Appl. 1998, 13, 18-28.
13. Huang, G.; Zhu, Q.; Siew, C. Extreme learning machine: theory and applications. Neurocomputing 2006, 70, 489-501.
14. Talluri, T.; Chung, H. T.; Shin, K. Study of battery state-of-charge estimation with kNN machine learning method. IEIESPC. 2021, 10, 496-504.
15. Song, S.; Zhang, X.; Gao, D.; et al. A hierarchical state of charge estimation method for lithium-ion batteries via xgboost and kalman filter. In 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Toronto, Canada, October 11 - 14, 2020; IEEE Press; pp 2317-22.
16. Antón JC, García Nieto PJ, Blanco Viejo C, Vilán Vilán JA. Support vector machines used to estimate the battery state of charge. IEEE. Trans. Power. Electron. 2013, 28, 5919-26.
17. Dou, J.; Ma, H.; Zhang, Y.; et al. Extreme learning machine model for state-of-charge estimation of lithium-ion battery using salp swarm algorithm. J. Energy. Storage. 2022, 52, 104996.
18. Rumelhart, D. E.; Hinton, G. E.; Williams, R. J. Learning representations by back-propagating errors. Nature 1986, 323, 533-6.
19. Lecun, Y.; Boser, B.; Denker, J. S.; et al. Backpropagation applied to handwritten zip code recognition. Neural. Comput. 1989, 1, 541-51.
20. Hopfield, J. J. Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. U. S. A. 1982, 79, 2554-8.
21. Hannan, M. A.; Lipu, M. S. H.; Hussain, A.; Saad, M. H.; Ayob, A. Neural network approach for estimating state of charge of lithium-ion battery using backtracking search algorithm. IEEE. Access. 2018, 6, 10069-79.
22. Bhattacharyya, H.S.; Yadav, A.; Choudhury, A.B.; Chanda, C.K. Convolution neural network-based SOC estimation of Li-ion battery in EV applications. In 2021 5th International Conference on Electrical, Electronics, Communication, Computer Technologies and Optimization Techniques (ICEECCOT), Dublin, Ireland, August 30-September 01, 2023; IEEE, 2023; pp 587-92.
24. Chung, J.; Gulcehre, C.; Cho, K.; Bengio, Y. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv 2014, arXiv:1412.3555.
25. Liu, Z.; Jia, W.; Liu, T.; Chang, Y.; Li, J. State of charge estimation of lithium-ion battery based on recurrent neural network. In 2020 Asia Energy and Electrical Engineering Symposium (AEEES), IEEE, 2020; pp 742-6.
26. Cheng, M.; Lee, Y.; Liu, M.; Sun, C. State-of-charge estimation with aging effect and correction for lithium-ion battery. IET. Electr. Syst. Transp. 2015, 5, 70-6.
27. Tian, J.; Xiong, R.; Shen, W. A review on state of health estimation for lithium ion batteries in photovoltaic systems. eTransportation 2019, 2, 100028.
28. Tian, J.; Chen, C.; Shen, W.; Sun, F.; Xiong, R. Deep learning framework for lithium-ion battery state of charge estimation: recent advances and future perspectives. Energy. Storage. Mater. 2023, 61, 102883.
29. Ghassani, F.; Abdurohman, M.; Putrada, A.G. Prediction of smarthphone charging using K-nearest neighbor machine learning. In 2018 Third Int Conf Inform Comput ICIC, Palembang, Indonesia, October 17-18, 2018; IEEE, 2018; pp. 1-4.
30. Hu, C.; Jain, G.; Zhang, P.; Schmidt, C.; Gomadam, P.; Gorka, T. Data-driven method based on particle swarm optimization and k-nearest neighbor regression for estimating capacity of lithium-ion battery. Appl. Energy. 2014, 129, 49-55.
31. Jiang, F.; Yang, J.; Cheng, Y.; et al. An aging-aware soc estimation method for lithium-ion batteries using xgboost algorithm. In 2019 IEEE Int Conf Progn Health Manag ICPHM, San Francisco, USA, June 17-20, 2019; IEEE, 2019; pp. 1-8.
32. Liu, X.; Li, K.; Wu, J.; He, Y.; Liu, X. An extended Kalman filter based data-driven method for state of charge estimation of Li-ion batteries. J. Energy. Storage. 2021, 40, 102655.
33. Chen, W.; Cai, M.; Tan, X.; Wei, B. Parameter identification and state-of-charge estimation for Li-ion batteries using an improved tree seed algorithm. IEICE. Trans. Inf. &. Syst. 2019, E102.D, 1489-97.
34. Wang, Q.; Luo, Y.; Han, X. Research on estimation model of the battery state of charge in a hybrid electric vehicle based on the classification and regression tree. Math. Comput. Modell. Dyn. Syst. 2019, 25, 376-96.
35. Antón J, García Nieto P, de Cos Juez F, Sánchez Lasheras F, González Vega M, Roqueñí Gutiérrez M. Battery state-of-charge estimator using the SVM technique. Appl. Math. Modell. 2013, 37, 6244-53.
36. Wei, K.; Wu, J.; Ma, W.; Li, H. State of charge prediction for UAVs based on support vector machine. J. Eng. 2019, 2019, 9133-6.
37. Li, J.; Ye, M.; Meng, W.; Xu, X.; Jiao, S. A novel state of charge approach of lithium ion battery using least squares support vector machine. IEEE. Access. 2020, 8, 195398-410.
38. Sheng, H.; Xiao, J. Electric vehicle state of charge estimation: nonlinear correlation and fuzzy support vector machine. J. Power. Sources. 2015, 281, 131-7.
39. Li, R.; Xu, S.; Li, S.; et al. State of charge prediction algorithm of lithium-ion battery based on PSO-SVR cross validation. IEEE. Access. 2020, 8, 10234-42.
40. Stighezza, M.; Bianchi, V.; De, M. I. FPGA implementation of an ant colony optimization based SVM algorithm for state of charge estimation in Li-ion batteries. Energies 2021, 14, 7064.
41. Ipek, E.; Eren, M.K.; Yilmaz, M. State-of-charge estimation of li-ion battery cell using support vector regression and gradient boosting techniques. In 2019 Int Aegean Conf Electr Mach Power Electron ACEMP 2019 Int Conf Optim Electr Electron Equip OPTIM, Istanbul, Turkey, August 27-29, 2019; IEEE, 2019; pp 604-9.
42. Song, Q.; Wang, S.; Xu, W.; Shao, Y.; Fernandez, C. A novel joint support vector machine - cubature kalman filtering method for adaptive state of charge prediction of lithium-ion batteries. Int. J. Electrochem. Sci. 2021, 16, 210823.
43. Xie, F.; Wang, S.; Xie, Y.; Fernandezb, C.; Li, X.; Zou, C. A novel battery state of charge estimation based on the joint unscented kalman filter and support vector machine algorithms. Int. J. Electrochem. Sci. 2020, 15, 7935-53.
44. Hu, J.; Hu, J.; Lin, H.; et al. State-of-charge estimation for battery management system using optimized support vector machine for regression. J. Power. Sources. 2014, 269, 682-93.
45. Hansen, T.; Wang, C. Support vector based battery state of charge estimator. J. Power. Sources. 2005, 141, 351-8.
46. Densmore A, Hanif M. Modeling the condition of lithium ion batteries using the extreme learning machine. In 2016 IEEE PES PowerAfrica, Livingstone, Zambia, June 28-July 03, 2016; IEEE, 2016; pp. 184-8.
47. Jiao, M.; Wang, D.; Yang, Y.; Liu, F. More intelligent and robust estimation of battery state-of-charge with an improved regularized extreme learning machine. Eng. Appl. Artif. Intell. 2021, 104, 104407.
48. Wang, Z.; Yang, D. State-of-charge estimation of lithium iron phosphate battery using extreme learning machine. In 2015 6th Int Conf Power Electron Syst Appl PESA, Hong Kong, December 15-17 2015; IEEE 2015; pp 1-5.
49. Chin, C.; Gao, Z. State-of-charge estimation of battery pack under varying ambient temperature using an adaptive sequential extreme learning machine. Energies 2018, 11, 711.
50. Zhao, X.; Qian, X.; Xuan, D.; Jung, S. State of charge estimation of lithium-ion battery based on multi-input extreme learning machine using online model parameter identification. J. Energy. Storage. 2022, 56, 105796.
51. Zhang, B.; Ren, G. Li-ion battery state of charge prediction for electric vehicles based on improved regularized extreme learning machine. WEVJ. 2023, 14, 202.
52. Zhang, C.; Wang, S.; Yu, C.; Xie, Y.; Fernandez, C. Novel improved particle swarm optimization-extreme learning machine algorithm for state of charge estimation of lithium-ion batteries. Ind. Eng. Chem. Res. 2022, 61, 17209-17.
53. Du, J.; Liu, Z.; Wang, Y. State of charge estimation for Li-ion battery based on model from extreme learning machine. Control. Eng. Pract. 2014, 26, 11-9.
54. Ozcan, G.; Pajovic, M.; Sahinoglu, Z.; Wang, Y.; Orlik, P.V.; Wada, T. Online state of charge estimation for lithium-ion batteries using Gaussian process regression. In IECON 2016-42nd Annual Conference of the IEEE Industrial Electronics Society, Florence, Italy, October 23-26, 2016; IEEE, 2016; pp 998-1003.
55. Ozcan, G.; Pajovic, M.; Sahinoglu, Z.; Wang, Y.; Orlik, P.V.; Wada, T. Online battery state-of-charge estimation based on sparse gaussian process regression. In 2016 IEEE Power and Energy Society General Meeting (PESGM), Boston, USA, July 17-21 2016; IEEE, 2016; pp 1-5.
56. Sahinoglu, G. O.; Pajovic, M.; Sahinoglu, Z.; Wang, Y.; Orlik, P. V.; Wada, T. Battery state-of-charge estimation based on regular/recurrent gaussian process regression. IEEE. Trans. Ind. Electron. 2018, 65, 4311-21.
57. Chen, X.; Chen, X.; Chen, X. A novel framework for lithium-ion battery state of charge estimation based on Kalman filter Gaussian process regression. Int. J. Energy. Res. 2021, 45, 13238-49.
58. Lee, K.; Lee, W.; Kim, K. K. Battery state-of-charge estimation using data-driven Gaussian process Kalman filters. J. Energy. Storage. 2023, 72, 108392.
59. Yi, Y.; Xia, C.; Shi, L.; et al. Lithium-ion battery expansion mechanism and Gaussian process regression based state of charge estimation with expansion characteristics. Energy 2024, 292, 130541.
60. Deng, Z.; Hu, X.; Lin, X.; Che, Y.; Xu, L.; Guo, W. Data-driven state of charge estimation for lithium-ion battery packs based on Gaussian process regression. Energy 2020, 205, 118000.
61. Plett, G. L. Battery management systems: battery modeling. Artech, 2015. https://ieeexplore.ieee.org/document/9100168 (accessed 2025-02-22).
62. Chemali, E.; Kollmeyer, P. J.; Preindl, M.; Emadi, A. State-of-charge estimation of Li-ion batteries using deep neural networks: a machine learning approach. J. Power. Sources. 2018, 400, 242-55.
63. How, D. N. T.; Hannan, M. A.; Lipu, M. S. H.; Sahari, K. S. M.; Ker, P. J.; Muttaqi, K. M. State-of-charge estimation of Li-ion battery in electric vehicles: a deep neural network approach. IEEE. Trans. Ind. Appl. 2020, 56, 5565-74.
64. Jung, G.E; Baek, J.; Liu, J.; et al. The precision SOC estimation method of LiB for EV applications using ANN. In 2021 International Conference on Information and Communication Technology Convergence (ICTC), Jeju Island, Korea, Republic of, October 20-22, 2021; IEEE, 2021; pp 1052-4.
65. Bian, C.; Yang, S.; Liu, J.; Zio, E. Robust state-of-charge estimation of Li-ion batteries based on multichannel convolutional and bidirectional recurrent neural networks. Appl. Soft. Comput. 2022, 116, 108401.
66. Bhattacharjee, A.; Verma, A.; Mishra, S.; Saha, T. K. Estimating state of charge for xEV batteries using 1D convolutional neural networks and transfer learning. IEEE. Trans. Veh. Technol. 2021, 70, 3123-35.
67. Hannan, M. A.; How, D. N. T.; Lipu, M. S. H.; et al. SOC estimation of Li-ion batteries with learning rate-optimized deep fully convolutional network. IEEE. Trans. Power. Electron. 2021, 36, 7349-53.
68. Li, J.; Jiang, Z.; Jiang, Y.; Song, W.; Gu, J. The state of charge estimation of lithium-Ion battery based on battery capacity. J. Electrochem. Soc. 2022, 169, 120539.
69. Mohanty, P. K.; Jena, P.; Padhy, N. P. Continuous wavelet transform based CNN model for EV battery state of charge estimation. In2023 IEEE 3rd International Conference on Smart Technologies for Power, Energy and Control (STPEC), 2023;1-5.
71. Kollmeyer, P.; Vidal, C.; Naguib, M.; Skells, M. LG 18650HG2 Li-ion battery data and example deep neural network xEV SOC estimator script. Mendeley. Data. 2020, 3, 2020.
72. Guo, W.; Li, J. A battery SOC prediction method based on GA-CNN network and its implementation on FPGA. InComputer engineering and technology, Xu, W., Xiao, L., Li, J., Zhu, Z., Eds.; Singapore: Springer, 2019; pp 78-90.
73. Mazzi, Y.; Ben, S. H.; Gaga, A.; Errahimi, F. State of charge estimation of an electric vehicle’s battery using tiny neural network embedded on small microcontroller units. Intl. J. of. Energy. Research. 2022, 46, 8102-19.
74. Liu, V.T.; Sun, Y.K.; Lu, H.Y.; Wang, S.K. State of charge estimation for lithium-ion battery using recurrent neural network. In 2018 IEEE International Conference on Advanced Manufacturing (ICAM), Yunlin, Taiwan, November 16-18, IEEE, 2018; pp 376-9.
75. Lipu, M.S.H.; Hannan, M.A.; Hussain, A.; Saad, M.H.M.; Ayob, A.; Muttaqi, K.M. Lithium-ion battery state of charge estimation method using optimized deep recurrent neural network algorithm. In 2019 IEEE Industry Applications Society Annual Meeting, Baltimore, USA, September 29-October 03, 2019; IEEE, 2019; pp 1-9.
76. Wang, Q.; Gu, H.; Ye, M.; Wei, M.; Xu, X. State of charge estimation for lithium-ion battery based on NARX recurrent neural network and moving window method. IEEE. Access. 2021, 9, 83364-75.
77. Sadykov, M.; Haines, S.; Walker, G.; Holmes, D. W. Feed-forward state of charge estimation of LiFePO4 batteries using time-series machine learning prediction with autoregressive models. J. Energy. Storage. 2024, 100, 113516.
78. Wei, M.; Ye, M.; Li, J. B.; Wang, Q.; Xu, X. State of charge estimation of lithium-ion batteries using LSTM and NARX neural networks. IEEE. Access. 2020, 8, 189236-45.
79. Chemali, E.; Kollmeyer, P. J.; Preindl, M.; Ahmed, R.; Emadi, A. Long short-term memory networks for accurate state-of-charge estimation of Li-ion batteries. IEEE. Trans. Ind. Electron. 2018, 65, 6730-9.
80. Ren, X.; Liu, S.; Yu, X.; Dong, X. A method for state-of-charge estimation of lithium-ion batteries based on PSO-LSTM. Energy 2021, 234, 121236.
81. Chen, J.; Lu, C.; Chen, C.; Cheng, H.; Xuan, D. An improved gated recurrent unit neural network for state-of-charge estimation of lithium-ion battery. Appl. Sci. 2022, 12, 2305.
82. Ma, L.; Hu, C.; Cheng, F. State of charge and state of energy estimation for lithium-ion batteries based on a long short-term memory neural network. J. Energy. Storage. 2021, 37, 102440.
83. Yang, F.; Song, X.; Xu, F.; Tsui, K. State-of-charge estimation of lithium-ion batteries via long short-term memory network. IEEE. Access. 2019, 7, 53792-9.
84. Hannan, M. A.; How, D. N. T.; Mansor, M. B.; Hossain, L. M. S.; Ker, P.; Muttaqi, K. State-of-charge estimation of Li-ion battery using gated recurrent unit with one-cycle learning rate policy. IEEE. Trans. on. Ind. Applicat. 2021, 57, 2964-71.
85. Zhang, Z.; Dong, Z.; Lin, H.; et al. An improved bidirectional gated recurrent unit method for accurate state-of-charge estimation. IEEE. Access. 2021, 9, 11252-63.
86. Chen, L.; Song, Y.; Lopes, A. M.; Bao, X.; Zhang, Z.; Lin, Y. Joint estimation of state of charge and state of energy of lithium-ion batteries based on optimized bidirectional gated recurrent neural network. IEEE. Trans. Transp. Electrific. 2024, 10, 1605-16.
87. Bian, C.; He, H.; Yang, S. Stacked bidirectional long short-term memory networks for state-of-charge estimation of lithium-ion batteries. Energy 2020, 191, 116538.
88. Manoharan, A.; Sooriamoorthy, D.; Begam, K.; Aparow, V. R. Electric vehicle battery pack state of charge estimation using parallel artificial neural networks. J. Energy. Storage. 2023, 72, 108333.
89. Jiang, B.; Tao, S.; Wang, X.; Zhu, J.; Wei, X.; Dai, H. Mechanics-based state of charge estimation for lithium-ion pouch battery using deep learning technique. Energy 2023, 278, 127890.
90. Jayaraman, R.; Thottungal, R. Accurate state of charge prediction for lithium-ion batteries in electric vehicles using deep learning and dimensionality reduction. Electr. Eng. 2024, 106, 2175-95.
91. Liu, H.; Liang, F.; Hu, T.; Hong, J.; Ma, H. Multi-scale fusion model based on gated recurrent unit for enhancing prediction accuracy of state-of-charge in battery energy storage systems. JJ. Mod. Power. Syst. Clean. Energy. 2024, 12, 405-14.
92. Liu, Y.; Shu, X.; Yu, H.; et al. State of charge prediction framework for lithium-ion batteries incorporating long short-term memory network and transfer learning. J. Energy. Storage. 2021, 37, 102494.
93. Eleftheriadis, P.; Giazitzis, S.; Leva, S.; Ogliari, E. Transfer learning techniques for the lithium-ion battery state of charge estimation. IEEE. Access. 2024, 12, 993-1004.
94. Lipton, Z. C.; Berkowitz, J.; Elkan, C. A critical review of recurrent neural networks for sequence learning. arXiv 2015, arXiv:1506.00019.
95. Abbas, G.; Nawaz, M.; Kamran, F. Performance comparison of NARX & RNN-LSTM neural networks for LiFePO4 battery state of charge estimation. In 2019 16th International Bhurban Conference on Applied Sciences and Technology (IBCAST), Islamabad, Pakistan, January 08-12, 2019; IEEE, 2019; pp 463-8.
96. Li, C.; Xiao, F.; Fan, Y.; Yang, G.; Zhang, W. A recurrent neural network with long short-term memory for state of charge estimation of lithium-ion batteries. In 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), Chongqing, China, May 24-26, 2019; IEEE, 2019; pp 1712-6.
97. Li, C.; Xiao, F.; Fan, Y. An approach to state of charge estimation of lithium-ion batteries based on recurrent neural networks with gated recurrent unit. Energies 2019, 12, 1592.
98. Jiao, M.; Wang, D.; Qiu, J. A GRU-RNN based momentum optimized algorithm for SOC estimation. J. Power. Sources. 2020, 459, 228051.
99. Duan, W.; Song, C.; Peng, S.; Xiao, F.; Shao, Y.; Song, S. An improved gated recurrent unit network model for state-of-charge estimation of lithium-ion battery. Energies 2020, 13, 6366.
100. Yang, B.; Wang, Y.; Zhan, Y. Lithium battery state-of-charge estimation based on a bayesian optimization bidirectional long short-term memory neural network. Energies 2022, 15, 4670.
101. Chen, J.; Zhang, Y.; Wu, J.; Cheng, W.; Zhu, Q. SOC estimation for lithium-ion battery using the LSTM-RNN with extended input and constrained output. Energy 2023, 262, 125375.
102. Yu, H.; Zhang, L.; Wang, W.; et al. State of charge estimation method by using a simplified electrochemical model in deep learning framework for lithium-ion batteries. Energy 2023, 278, 127846.
103. Wu, X.; Li, M.; Du, J.; Hu, F. SOC prediction method based on battery pack aging and consistency deviation of thermoelectric characteristics. Energy. Rep. 2022, 8, 2262-72.
104. Liu, D.; Wang, S.; Fan, Y.; Fernandez, C.; Blaabjerg, F. An optimized multi-segment long short-term memory network strategy for power lithium-ion battery state of charge estimation adaptive wide temperatures. Energy 2024, 304, 132048.
105. Fawaz H, Forestier G, Weber J, Idoumghar L, Muller P. Deep learning for time series classification: a review. Data. Min. Knowl. Disc. 2019, 33, 917-63.
106. Kazemi, S. M.; Goel, R.; Eghbali, S.; et al. Time2Vec: learning a vector representation of time. arXiv 1907, arXiv:1907.05321.
107. Almaita, E.; Alshkoor, S.; Abdelsalam, E.; Almomani, F. State of charge estimation for a group of lithium-ion batteries using long short-term memory neural network. J. Energy. Storagee. 2022, 52, 104761.
108. Wang, Y.; Chen, Z.; Zhang, W. Lithium-ion battery state-of-charge estimation for small target sample sets using the improved GRU-based transfer learning. Energy 2022, 244, 123178.
109. Che, Y.; Zheng, Y.; Wu, Y.; et al. Battery states monitoring for electric vehicles based on transferred multi-task learning. IEEE. Trans. Veh. Technol. 2023, 72, 10037-47.
111. Guo, S.; Ma, L. A comparative study of different deep learning algorithms for lithium-ion batteries on state-of-charge estimation. Energy 2023, 263, 125872.
112. Pau, D. P.; Aniballi, A. Tiny machine learning battery state-of-charge estimation hardware accelerated. Appl. Sci. 2024, 14, 6240.
113. Cui, S.; Yong, X.; Kim, S.; Hong, S.; Joe, I. An LSTM-based encoder-decoder model for state-of-charge estimation of lithium-ion batteries. Intelligent. Algorithms. in. Software. Engineering. , Silhavy, R., Eds.; Cham, Springer International Publishing, 2020; pp 178-88.
114. Bian, C.; He, H.; Yang, S.; Huang, T. State-of-charge sequence estimation of lithium-ion battery based on bidirectional long short-term memory encoder-decoder architecture. J. Power. Sources. 2020, 449, 227558.
115. Terala, P. K.; Ogundana, A. S.; Foo, S. Y.; Amarasinghe, M. Y.; Zang, H. State of charge estimation of lithium-ion batteries using stacked encoder-decoder Bi-directional LSTM for EV and HEV applications. Micromachines 2022, 13, 1397.
116. Ma, L.; Wang, Z.; Yang, F.; et al. Robust state of charge estimation based on a sequence-to-sequence mapping model with process information. J. Power. Sources. 2020, 474, 228691.
117. Fan, X.; Zhang, W.; Zhang, C.; Chen, A.; An, F. SOC estimation of Li-ion battery using convolutional neural network with U-Net architecture. Energy 2022, 256, 124612.
118. Ardeshiri, R.R.; Ma, C. State of charge estimation of lithium-ion battery using deep convolutional stacked bidirectional LSTM. In 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE), Kyoto, Japan, June 20-23, 2021; IEEE, 2021; pp 1-6.
119. Shen, L.; Li, J.; Zuo, L.; Zhu, L.; Shen, H. T. Source-free cross-domain state of charge estimation of lithium-ion batteries at different ambient temperatures. IEEE. Trans. Power. Electron. 2023, 38, 6851-62.
120. Chen, J.; Feng, X.; Jiang, L.; Zhu, Q. State of charge estimation of lithium-ion battery using denoising autoencoder and gated recurrent unit recurrent neural network. Energy 2021, 227, 120451.
121. Savargaonkar, M.; Oyewole, I.; Chehade, A.; Hussein, A. A. Uncorrelated sparse autoencoder with long short-term memory for state-of-charge estimations in lithium-ion battery cells. IEEE. Trans. Automat. Sci. Eng. 2024, 21, 15-26.
122. Bakas, S.; Akbari, H.; Sotiras, A.; et al. Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data. 2017, 4, 170117.
123. Severson, K. A.; Attia, P. M.; Jin, N.; et al. Data-driven prediction of battery cycle life before capacity degradation. Nat. Energy. 2019, 4, 383-91.
124. Diao, W.; Saxena, S.; Pecht, M. Accelerated cycle life testing and capacity degradation modeling of LiCoO2-graphite cells. J. Power. Sources. 2019, 435, 226830.
125. Hu, L.; Hu, X.; Che, Y.; Feng, F.; Lin, X.; Zhang, Z. Reliable state of charge estimation of battery packs using fuzzy adaptive federated filtering. Appl. Energy. 2020, 262, 114569.
126. Lv, X.; Cheng, Y.; Ma, S.; Jiang, H. State of health estimation method based on real data of electric vehicles using federated learning. Int. J. Electrochem. Sci. 2024, 19, 100591.
127. Wong, K. L.; Tse, R.; Tang, S.; Pau, G. Decentralized deep-learning approach for lithium-ion batteries state of health forecasting using federated learning. IEEE. Trans. Transp. Electrific. 2024, 10, 8199-212.
128. Li, C.; Cui, N.; Wang, C.; Zhang, C. Reduced-order electrochemical model for lithium-ion battery with domain decomposition and polynomial approximation methods. Energy 2021, 221, 119662.
129. Nascimento, R. G.; Corbetta, M.; Kulkarni, C. S.; Viana, F. A. Hybrid physics-informed neural networks for lithium-ion battery modeling and prognosis. J. Power. Sources. 2021, 513, 230526.
