Journal of Hebei University of Technology, Volume. 54, Issue 3, 79(2025)
Lithium-ion battery core temperature estimation based on machine learning and mode decomposition methods
[1] [1] FICHTNER M. Recent research and progress in batteries for electric vehicles[J]. Batteries & Supercaps, 2022, 5(2): e202100224.
[2] [2] ZHANG X H, LI Z, LUO L G, et al. A review on thermal management of lithium-ion batteries for electric vehicles[J]. Energy, 2022, 238: 121652.
[4] [4] WANG Y, FENG X N, HUANG W S, et al. Challenges and opportunities to mitigate the catastrophic thermal runaway of high-energy batteries[J]. Advanced Energy Materials, 2023, 13(15): 2203841.
[5] [5] WANG Z C, DU C Q. A comprehensive review on thermal management systems for power lithium-ion batteries[J]. Renewable and Sustainable Energy Reviews, 2021, 139: 110685.
[8] [8] REN Z, DU C Q. A review of machine learning state-of-charge and state-of-health estimation algorithms for lithium-ion batteries[J]. Energy Reports, 2023, 9: 2993-3021.
[9] [9] CHEN Z, CHEN L Q, SHEN W J, et al. Remaining useful life prediction of lithium-ion battery via a sequence decomposition and deep learning integrated approach[J]. IEEE Transactions on Vehicular Technology, 2022, 71(2): 1466-1479.
[10] [10] DEMIRCI O, TASKIN S, SCHALTZ E, et al. Review of battery state estimation methods for electric vehicles-Part I: SOC estimation[J]. Journal of Energy Storage, 2024, 87: 111435.
[11] [11] LI F, ZUO W, ZHOU K, et al. State of charge estimation of lithium-ion batteries based on PSO-TCN-Attention neural network[J]. Journal of Energy Storage, 2024, 84: 110806.
[12] [12] MIRANDA M H R, SILVA F L, LOURENO M A M, et al. Particle swarm optimization of Elman neural network applied to battery state of charge and state of health estimation[J]. Energy, 2023, 285: 129503.
[13] [13] QUAN R, LIU P, LI Z X, et al. A multi-dimensional residual shrinking network combined with a long short-term memory network for state of charge estimation of Li-ion batteries[J]. Journal of Energy Storage, 2023, 57: 106263.
[14] [14] FENG J Q, CAI F, LI H C, et al. A data-driven prediction model for the remaining useful life prediction of lithium-ion batteries[J]. Process Safety and Environmental Protection, 2023, 180: 601-615.
[15] [15] LIU M L, ZHOU X D, YANG L Z, et al. A novel Kalman-filter-based battery internal temperature estimation method based on an enhanced electro-thermal coupling model[J]. Journal of Energy Storage, 2023, 71: 108241.
[16] [16] RICHARDSON R R, IRELAND P T, HOWEY D A. Battery internal temperature estimation by combined impedance and surface temperature measurement[J]. Journal of Power Sources, 2014, 265: 254-261.
[17] [17] MAY, CUI Y F, MOU H Y, et al. Core temperature estimation of lithium-ion battery for EVs using Kalman filter[J]. Applied Thermal Engineering, 2020, 168: 114816.
[18] [18] SUN L, SUN W, YOU F Q. Core temperature modelling and monitoring of lithium-ion battery in the presence of sensor bias[J]. Applied Energy, 2020, 271: 115243.
[19] [19] ZHU S, HE C N, ZHAO N Q, et al. Data-driven analysis on thermal effects and temperature changes of lithium-ion battery[J]. Journal of Power Sources, 2021, 482: 228983.
[20] [20] WANG Y L, CHEN X J, LI C L, et al. Temperature prediction of lithium-ion battery based on artificial neural network model[J]. Applied Thermal Engineering, 2023, 228: 120482.
[21] [21] BILLERTA M, YU R Y, ERSCHEN S, et al. Improved quantile convolutional and recurrent neural networks for electric vehicle battery temperature prediction[J]. Big Data Mining and Analytics, 2024, 7(2): 512-530.
[22] [22] YUAN L, LI W H, DENG W J, et al. Cell temperature prediction in the refrigerant direct cooling thermal management system using artificial neural network[J]. Applied Thermal Engineering, 2024, 254: 123852.
[23] [23] JIANG L, YAN C K, ZHANG X S, et al. Temperature prediction of battery energy storage plant based on EGA-BiLSTM[J]. Energy Reports, 2022, 8: 1009-1018.
[24] [24] NEBAUER C. Evaluation of convolutional neural networks for visual recognition[J]. IEEE Transactions on Neural Networks, 1998, 9(4): 685-696.
[25] [25] KRIZHEVSKY A, SUTSKEVER II, HINTON G. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84-90.
[26] [26] GRAVESA. Supervised sequence labelling with recurrent neural networks[M]. Berlin: Springer Berlin Heidelberg, 2012.
[28] [28] ZHANG W C, WAN W J, WU W X, et al. Internal temperature prediction model of the cylindrical lithium-ion battery under different cooling modes[J]. Applied Thermal Engineering, 2022, 212: 118562.
[29] [29] WANG N, ZHAO G C, KANG Y Z, et al. Core temperature estimation method for lithium-ion battery based on long short-term memory model with transfer learning[J]. IEEE Journal of Emerging and Selected Topics in Power Electronics, 2023, 11(1): 201-213.
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LI Menghan, ZHU Sichen, LI Ye, ZHAO Jiabao, RAO Zhonghao. Lithium-ion battery core temperature estimation based on machine learning and mode decomposition methods[J]. Journal of Hebei University of Technology, 2025, 54(3): 79
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Received: Mar. 7, 2025
Accepted: Aug. 22, 2025
Published Online: Aug. 22, 2025
The Author Email: RAO Zhonghao (raozhonghao@hebut.edu.cn)