[1] |
OMER A M. Energy, environment and sustainable development[J]. Renewable and Sustainable Energy Reviews, 2008, 12(9): 2265-2300. DOI: 10.1016/j.rser.2007.05.001.
|
[2] |
XU J J, CAI X Y, CAI S M, et al. High-energy lithium-ion batteries: Recent progress and a promising future in applications[J]. Energy & Environmental Materials, 2023, 6(5): e12450. DOI: 10.1002/ee m2.12450.
|
[3] |
RIVERA-BARRERA J P, MUÑOZ-GALEANO N, SARMIENTO-MALDONADO H O. SOC estimation for lithium-ion batteries: Review and future challenges[J]. Electronics, 2017, 6(4): 102. DOI: 10.3390/electronics6040102.
|
[4] |
HOW D N T, HANNAN M A, HOSSAIN LIPU M S, et al. State of charge estimation for lithium-ion batteries using model-based and data-driven methods: A review[J]. IEEE Access, 2019, 7: 136116-136136.
|
[5] |
ZHOU W L, ZHENG Y P, PAN Z J, et al. Review on the battery model and SOC estimation method[J]. Processes, 2021, 9(9): 1685. DOI: 10.3390/pr9091685.
|
[6] |
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. DOI: 10.1016/j.est.2024.111435.
|
[7] |
SHEN Y Q. Adaptive online state-of-charge determination based on neuro-controller and neural network[J]. Energy Conversion and Management, 2010, 51(5): 1093-1098. DOI: 10.1016/j.enc onman.2009.12.015.
|
[8] |
SHI Q S, ZHANG C H, CUI N X. Estimation of battery state-of-charge using ν-support vector regression algorithm[J]. International Journal of Automotive Technology, 2008, 9(6): 759-764. DOI: 10.1007/s12239-008-0090-x.
|
[9] |
SESIDHAR D V S R, BADACHI C, GREEN R C Ⅱ. A review on data-driven SOC estimation with Li-Ion batteries: Implementation methods & future aspirations[J]. Journal of Energy Storage, 2023, 72: 108420. DOI: 10.1016/j.est.2023.108420.
|
[10] |
SONG X B, YANG F F, WANG D, et al. Combined CNN-LSTM network for state-of-charge estimation of lithium-ion batteries[J]. IEEE Access, 2019, 7: 88894-88902.
|
[11] |
JIAO M, WANG D Q, QIU J L. A GRU-RNN based momentum optimized algorithm for SOC estimation[J]. Journal of Power Sources, 2020, 459: 228051. DOI: 10.1016/j.jpowsou r.2020.228051.
|
[12] |
HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735-1780. DOI: 10.1162/neco.1997.9.8.1735.
|
[13] |
CHEMALI E, KOLLMEYER P J, PREINDL M, et al. Long short-term memory networks for accurate state-of-charge estimation of Li-ion batteries[J]. IEEE Transactions on Industrial Electronics, 2018, 65(8): 6730-6739. DOI: 10.1109/TIE.2017.2787586.
|
[14] |
张远进, 吴华伟, 叶从进. 基于AUKF-BP神经网络的锂电池SOC估算[J]. 储能科学与技术, 2021, 10(1): 237-241. DOI: 10.19799/j.cnki.2095-4239.2020.0285.
|
|
ZHANG Y J, WU H W, YE C J. Estimation of the SOC of a battery based on the AUKF-BP algorithm[J]. Energy Storage Science and Technology, 2021, 10(1): 237-241. DOI: 10.19799/j.cnk i.20 95-4239.2020.0285.
|
[15] |
FASAHAT M, MANTHOURI M. State of charge estimation of lithium-ion batteries using hybrid autoencoder and Long Short Term Memory neural networks[J]. Journal of Power Sources, 2020, 469: 228375. DOI: 10.1016/j.jpowsour.2020.228375.
|
[16] |
时玮, 姜久春, 李索宇, 等. 磷酸铁锂电池SOC估算方法研究[J]. 电子测量与仪器学报, 2010, 24(8): 769-774. DOI: 10.3724/SP.J.11 87.2010.00769.
|
|
SHI W, JIANG J C, LI S Y, et al. Research on SOC estimation for LiFePO4 Li-ion batteries[J]. Journal of Electronic Measurement and Instrument, 2010, 24(8): 769-774. DOI: 10.3724/SP.J.1187. 2010.00769.
|
[17] |
JIANG Y H, XU J, HOU W L, et al. A stack pressure based equivalent mechanical model of lithium-ion pouch batteries[J]. Energy, 2021, 221: 119804. DOI: 10.1016/j.energy.2021.119804.
|
[18] |
FIGUEROA-SANTOS M A, SIEGEL J B, STEFANOPOULOU A G. Leveraging cell expansion sensing in state of charge estimation: Practical considerations[J]. Energies, 2020, 13(10): 2653. DOI: 10.3390/en13102653.
|
[19] |
LIU M M, XU J, JIANG Y H, et al. Multi-dimensional features based data-driven state of charge estimation method for LiFePO4 batteries[J]. Energy, 2023, 274: 127407. DOI: 10.1016/j.energy. 2023.127407.
|
[20] |
CUI Z H, WANG L C, LI Q, et al. A comprehensive review on the state of charge estimation for lithium-ion battery based on neural network[J]. International Journal of Energy Research, 2022, 46(5): 5423-5440. DOI: 10.1002/er.7545.
|
[21] |
XU P P, LI J Q, XUE Q, et al. A syncretic state-of-charge estimator for LiFePO4 batteries leveraging expansion force[J]. Journal of Energy Storage, 2022, 50: 104559. DOI: 10.1016/j.est.2022.104559.
|
[22] |
樊慧敏, 彭浩鸿, 孟辉, 等. 储能电池模组膨胀力特性研究及仿真分析[J/OL]. 储能科学与技术, 2025: 1-11. (2025-01-14). https://link.cnki.net/doi/10.19799/j.cnki.2095-4239.2024.1210.
|
|
FAN H M, PENG H H, MENG H, et al. The research and simulation analysis of swelling force characteristics in energy storage battery modules[J/OL]. Energy Storage Science and Technology, 2025: 1-11. (2025-01-14). https://link.cnki.net/doi/10.19799/j.cnki.2095-4239.2024.1210.
|
[23] |
LIU Y F, LI J Q, ZHANG G, et al. State of charge estimation of lithium-ion batteries based on temporal convolutional network and transfer learning[J]. IEEE Access, 2021, 9: 34177-34187.
|
[24] |
朱大奇, 史慧. 人工神经网络原理及应用[M]. 北京: 科学出版社, 2006.
|
|
ZHU D Q, SHI H. Principle and application of artificial neural network[M]. Beijing: Science Press, 2006.
|