梯次利用背景下退役动力电池健康状态评估综述-电气工程与自动化-CSCIED科技核心评价数据库-手机版

梯次利用背景下退役动力电池健康状态评估综述

A review of health state assessment for retired power batteries in the context of cascade utilization

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DOI	10.12208/j.jeea.20250237
刊名	电气工程与自动化 Journal of Electrical Engineering and Automation
年，卷(期)	2025, 4(7)
作者	陈梦婷,
作者单位	南京邮电大学物联网学院江苏南京
摘要	随着新能源汽车产业的快速发展，全球正迎来首轮动力电池退役潮。梯次利用作为实现退役电池价值最大化、缓解环境与资源压力的关键路径，其核心挑战在于如何保障电池在二次利用过程中的安全性与可靠性。本文系统综述了梯次利用背景下退役动力电池健康状态评估的研究现状，重点围绕三个核心层面展开：1. 剖析了电化学模型与等效电路模型等老化特征建模方法的优势与局限性，指出其在应对梯次电池高度异构性和复杂非线性老化时的不足；2. 探讨了模型参数辨识的技术路径，分析了实验驱动、数据驱动及智能算法在应对梯次电池参数高维、动态变化等挑战中的进展与瓶颈；3. 评述了基于直接测试、模型驱动及数据驱动的健康状态评估方法，并强调了新兴的物理信息神经网络等在融合物理机理与数据智能方面的潜力。最后，本文总结了该领域在多尺度耦合机理建模不足、参数动态辨识局限、健康评估方法可解释性差等方面面临的挑战，为未来研究方向提供了展望。
Abstract	With the rapid development of the new energy vehicle industry, the world is witnessing the first wave of power battery retirements. Battery cascade utilization, as a key pathway to maximizing the residual value of retired batteries and alleviating environmental and resource pressures, faces a core challenge—ensuring safety and reliability during secondary use. This paper provides a systematic review of research on health state assessment of retired power batteries in the context of cascade utilization, focusing on three key aspects. 1. It analyzes the advantages and limitations of aging feature modeling methods such as electrochemical models and equivalent circuit models, highlighting their inadequacy in addressing the high heterogeneity and nonlinear degradation of cascaded batteries. 2. It explores parameter identification approaches, summarizing the progress and bottlenecks of experimental-driven, data-driven, and intelligence-driven methods in handling high-dimensional and dynamically varying parameters. 3. It reviews health assessment methods based on direct testing, model-driven, and data-driven frameworks, emphasizing the potential of emerging physics-informed neural networks for integrating physical mechanisms with data intelligence. Finally, this paper summarizes the challenges in multi-scale mechanism modeling, dynamic parameter identification, and interpretability of health assessment methods, and provides insights into future research directions.
关键词	梯次利用；退役动力电池；健康状态评估；电池建模
KeyWord	Cascade utilization; Retired power battery; Health state assessment; Battery modeling
基金项目
页码	68-73

参考文献
相关文献

[1] 应雄,汪寿阳,杨宇瑶.能源转型下的锂、钴、镍资源需求及回收潜力分析——基于电动汽车的视角
[J].中国科学院院刊, 2024,39(07):1226−1234.

[2] Li Jianwei, He Shucheng, Yang Qingqing, et al. A comprehensive review of second life batteries toward sustainable mechanisms: Potential, challenges, and future prospects. IEEE Transactions on Transportation Electrification, 2023, 9(4): 4824−4845.

[3] 何潇,刘泽夷,胡嵩乔,等.动态系统的实时安全性评估技术.自动化学报,2025, 51(2): 249−270.

[4] Yuan Jun, Qin Zhili, Huang Haikun, et al. Progress in the prognosis of battery degradation and estimation of battery states. Science China Materials, 2024, 67(4): 1014−1041.

[5] 孙丙香,庞俊峰,苏晓佳,等.基于中低频阻抗谱的锂离子电池容量快速估计方法研究.中国公路学报, 2024, 37(2): 293−303.

[6] Guo Ruohan, Wang Feng, Hu Cungang, et al. Toward accurate and efficient sorting of retired lithium-ion batteries: A data-driven-based electrode aging assessment approach. IEEE Transactions on Transportation Electrification, 2025, 11(1): 4841−4856.

[7] 刘芳,邵晨,苏卫星,等.基于全新等效电路模型的电池关键状态在线联合估计器. 控制与决策, 2023, 38(6): 1620−1628.

[8] Hu Xiaosong, Deng Xinchen, Wang Feng, et al. A review of second-life lithium-ion batteries for stationary energy storage applications. Proceedings of the IEEE, 2022, 110(6): 735−753.

[9] Shen Jiangwei, Zhang Zheng, Chen Zheng, et al. Temperature estimation of multiple places for lithium-ion batteries based on improved electrochemical thermal modeling. IEEE Transactions on Transportation Electrification, 2025, 11(1): 382−392.

[10] Planella Ferran Brosa, Widanage W. Dhammika. A single particle model with electrolyte and side reactions for degradation of lithium-ion batteries. Applied Mathematical Modelling, 2023, 121: 586−610.

[11] Kong Laiqiang, Fang Sidun, Niu Tao, et al. Fast state of charge estimation for lithium-ion battery based on electrochemical impedance spectroscopy frequency feature extraction. IEEE Transactions on Industry Applications, 2024, 60(1): 13691−1379.

[12] Xie Yizhan, Wang Shuhui, Wang Zhenpo, et al. A novel order-reduced thermal-coupling electrochemical model for lithium-ion batteries. Chinese Physics B, 2024, 33(5): 058203.

[13] Hou Jie, Jiang Yuchao, Liu Jingxiang, et al. Adaptive linear time-varying parameter-varying modeling of lithium-ion batteries considering aging phenomenon. IEEE Transactions on Power Electronics, 2025, 40(11): 16853-16869.

[14] Ghosh Nitika, Garg Akhil, Warnecke Alexander Johannes, et al. A white-box equivalent neural network ensemble for health estimation of lithium-ion batteries. IEEE Transactions on Transportation Electrification, 2025, 11(1): 1863−1874.

[15] Jiang Cong, Wang Yujie, Sun Zhendong, et al. Fractional-order equivalent circuit model for commercial sodium-ion batteries in a wide temperature range considering aging. Journal of Energy Storage, 2025, 105: 114552.

[16] 陶正德,张志超,郭昌梁.基于电化学-热耦合模型的动力电池逆向仿真建模与参数辨识.储能科学与技术, 2024, 13(06):2022−2029.

[17] Shui Zhongyi, Li Xuhao, Feng Yun, et al. Combining reduced-order model with data-driven model for parameter estimation of lithium-ion battery. IEEE Transactions on Industrial Electronics, 2023, 70(2): 1521−1531.

[18] Zhang Dong, Park Saehong, Couto Luis D., et al. Beyond battery state of charge estimation: Observer for electrode-level state and cyclable lithium with electrolyte dynamics. IEEE Transactions on Transportation Electrification, 2023, 9(4): 4846−4861.

[19] Chen Mengting, Xie Xiangpeng, He Chaoyue, et al. An integrated framework for ARX model identification and its application to lithium-ion battery. IEEE Transactions on Instrumentation and Measurement, 2025, 74: 3001414.

[20] Li Huan, Jin Yu, Wu Xuebing, et al. An improved multi-time scale lithium-ion battery model parameter identification algorithm based on discrete wavelet transform method. IEEE Transactions on Instrumentation and Measurement, 2025, 74: 3000416.

[21] Kadem Onur, Kim Jongrae. Real-time state of charge-open circuit voltage curve construction for battery state of charge estimation. IEEE Transactions on Vehicular Technology, 2023, 72(7): 8613−8622.

[22] Carbone Paolo, Angelis Alession De, Moschitta Antonio, et al. Time-domain battery impedance identification under piecewise constant current excitation. IEEE Transactions on Instrumentation and Measurement, 2023, 72: 9002710.

[23] Tian Jinpeng, Xiong Rui, Shen Weixiang, et al. Fractional order battery modelling methodologies for electric vehicle applications: Recent advances and perspectives. Science China Technological Sciences, 2020, 63(11): 2211−2230.

[24] 张文安,林安迪,杨旭升,等.融合深度学习的贝叶斯滤波综述.自动化学报, 2024, 50(8): 1502−1516.

[25] Baggio Giacomo, Carè Algo, Scampicchio Anna, et al. Bayesian frequentist bounds for machine learning and system identification. Automatica, 2022, 146: 110599.

[26] Abrahamyan Lusine, Chen Yiming, Bekoulis Giammis, et al. Learned gradient compression for distributed deep learning. IEEE Transactions on Neural Networks and Learning Systems, 2022, 33(2): 7330−7344.

[27] 张博玮,郑建飞,胡昌华,等.基于流模型的缺失数据生成方法在剩余寿命预测中的应用.自动化学报, 2023, 49(1): 185−196.

[28] Li Wei, Che Jinlin, Wang Zhenyu, et al. IFL-GAN: Improved federated learning generative adversarial network with maximum mean discrepancy model aggregation. IEEE Transactions on Neural Networks and Learning Systems, 2023, 34(12): 10502−10515.

[29] Yu Yanan, Tang Lihua, Liu Zhiping, et al. A novel bearing fault data generation strategy combining physical modeling and cyclegan variant for fault diagnosis without real samples. IEEE Transactions on Instrumentation and Measurement, 2025, 74: 3505717.

[30] Chen Jing, Mao Yawen, Wang Dongqing, et al. Reduced-order identification methods: Hierarchical algorithm or variable elimination algorithm. Automatica, 2025, 172: 111991.

[31] Xing Haoming, Ding Feng, Zhang Xiao, et al. Highly-efficient filtered hierarchical identification algorithms for multiple-input multiple-output systems with colored noises. Systems & Control Letters, 2024, 186: 105762.

[32] 杨博文,刘思垒,冯旭宁,等.锂离子电池温度状态:定义、检测与估计.中国科学:技术科学, 2025, 55: 187−212.

[33] Kong Laiqiang, Fang Sidun, Niu Tao, et al. Fast state of charge estimation for lithium-ion battery based on electrochemical impedance spectroscopy frequency feature extraction. IEEE Transactions on Industry Applications, 2024, 60 (1): 1369−1379.

[34] Huang Zexin, Best Matt, Knowles Jmaes, et al. Adaptive piecewise equivalent circuit model with SOC/SOH estimation based on extended kalman filter. IEEE Transactions on Energy Conversion, 2023, 38(2): 959−970.

[35] Kadem Onur, Kim Jongrae. Real-time state of charge-open circuit voltage curve construction for battery state of charge estimation. IEEE Transactions on Vehicular Technology, 2023, 72(7): 8613−8622.

[36] 朱振宇,高德欣.基于混合网络的锂离子电池健康状态与剩余使用寿命联合估计方法. 信息与控制,2024, 53(1): 120−128.

[37] Lu Jiahuan, Xiong Rui, Tian Jinpeng, et al. Deep learning to estimate lithium-ion battery state of health without additional degradation experiments. Nature Communications, 2023, 14: 2760.

[38] Wang Fujin, Zhai Zhi, Zhao Zhibin, et al. Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis. Nature Communications, 2024, 15: 4332.

[39] Wen Pengfei, Ye Zhisheng, Li Yong, et al. Physics-informed neural networks for prognostics and health management of lithium-ion batteries. IEEE Transactions on Intelligent Vehicles, 2024(1): 2276−2289.

[40] Tao Junjie, Wang Shunli, Cao Wen, et al. An innovative multitask learning-long short-term memory neural network for the online anti-aging state of charge estimation of lithium-ion batteries adaptive to varying temperature and current conditions. Energy, 2025, 314: 134272.

引用本文

陈梦婷. 梯次利用背景下退役动力电池健康状态评估综述 [J]. 电气工程与自动化. 2025; 4; (7). 68 - 73.

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