考虑多地域季节影响的新能源汽车实车动力电池系统健康状态估计

doi:10.3901/JME.260125

摘要/Abstract

摘要： 为了实现新能源汽车管理系统的高效准确运行，动力电池健康状态(State of health, SOH)的精确评估至关重要。考虑到实车运行中存在环境温度等影响因素，提出一种考虑地域季节温度条件的数据驱动模型，用以提高动力电池系统SOH估计精度。首先，分析车辆常行驶地区冬季平均低温，将所研究车辆分为两组，并统计其充电行为，通过电流、电压、温度以及容量增量曲线等选择多个老化特征值，并通过相关系数法进行了筛选；随后，利用夏季和冬季的数据训练分别训练两个贝叶斯优化的卷积神经网络(Bayesian-optimized convolutional neural network, BCNN)模型，并用测试数据验证了这些模型的准确性；最后，将所提方法与基于长短时记忆神经网络(Long short-term memory neural network, LSTM)的估计方法进行比较，并据此提出模型耦合优化方案，以增强估计方法的适应性和广泛性。结果显示，耦合模型可以实现常行驶在冬季低温地区的车辆SOH估计误差不超过6%，而常行驶在温暖地区的车辆误差不超过2%。这表明所提出的耦合模型能够有效地提高不同地域季节温度条件下SOH估计的准确性。

关键词: 锂离子电池, 健康状态估计, 特征值提取, 实车运行数据, 卷积神经网络

Abstract: To ensure the accurate and efficient operation of the management system for new energy vehicles, precise evaluation of the state of health(SOH) of power batteries is crucial. Taking into account the influence of ambient temperature and other factors during actual vehicle operation, a data-driven coupled model that considers regional and seasonal temperature conditions is proposed to improve the accuracy of SOH estimation for power battery systems. Vehicles under analysis are divided into two groups based on the average low temperature in winter in their regular operating regions, and their charging behavior is statistically analyzed. Multiple aging characteristic values are selected based on current, voltage, temperature, and incremental capacity curves, and these values are then filtered using the correlation coefficient method. Subsequently, two Bayesian-optimized convolutional neural network(BCNN) models are trained separately using summer and winter data, and their accuracy is validated with test data. Finally, the proposed method is compared with an estimation method based on the long short-term memory neural network(LSTM), and a model coupling optimization scheme is proposed to enhance the adaptability and broad applicability of the estimation method. The results show that the coupled model can achieve an SOH estimation error of no more than 6% for vehicles in low-temperature regions during winter, and an error of no more than 2% for vehicles in warm regions. This indicates that the proposed coupled model can effectively improve the accuracy of SOH estimation under different regional and seasonal temperature conditions.

Key words: lithium-ion battery, state of health estimation, extraction of health factors, real-world driving data, convolutional neural network

中图分类号:

TM912

吕沫含, 赵深, 高萧, 李晓宇. 考虑多地域季节影响的新能源汽车实车动力电池系统健康状态估计[J]. 机械工程学报, 2026, 62(4): 283-295.

Lü Mohan, ZHAO Shen, GAO Xiao, LI Xiaoyu. Multi-regional Seasonal Temperature Variations Considered Battery State of Health Estimation for New Energy Vehicles Based on Real-world Operation Data[J]. Journal of Mechanical Engineering, 2026, 62(4): 283-295.

参考文献

[1] 朱建功，戴海峰，王学远，等. 从电池管理到电化学数字电源[J]. 电气工程学报，2024，19(1)：3-22. ZHU Jiangong，DAI Haifeng，WANG Xueyuan，et al. Battery management towards the digitalized electrochemical power source[J]. Journal of Electrical Engineering，2024，19(1)：3-22.
[2] LIU J，ZHOU L，ZHANG Y，et al. Aging behavior and mechanisms of lithium-ion battery under multi-aging path[J]. Journal of Cleaner Production，2023，423：138678.
[3] SEPASIAHOOYI S，ABDOLLAHI F. Fault detection of new and aged lithium-ion battery cells in electric vehicles[J]. Green Energy and Intelligent Transportation，2024，3(3)：100165.
[4] 来鑫，陈权威，顾黄辉，等. 面向“双碳”战略目标的锂离子电池生命周期评价：框架、方法与进展[J]. 机械工程学报，2022，58(22)：3-18. LAI Xin，CHEN Quanwei，GU Huanghui，et al. Life cycle assessment of lithium-ion batteries for carbon-peaking and carbon-neutrality：Framework，methods，and progress[J]. Journal of Mechanical Engineering，2022，58(22)：3-18.
[5] LI X，LYU M，LI K，et al. Lithium-ion battery state of health estimation based on multi-source health indicators extraction and sparse Bayesian learning[J]，Energy，2023，282：128445.
[6] VIGNESH S，CHE H S，SELVARAJ J，et al. State of health (SoH) estimation methods for second life lithium-ion battery—Review and challenges[J]. Applied Energy，2024，369：123542.
[7] LI K，GAO X，LIU C，et al. A novel Co-estimation framework of state-of-charge，state-of-power and capacity for lithium-ion batteries using multi-parameters fusion method[J]. Energy，2023，269：126820.
[8] LIU J，PANG H，GENG Y，et al. Thermal-coupled single particle modeling and multi-objective stepwise parameter identification of lithium-ion batteries over different temperatures[J]. Journal of The Electrochemical Society，2024，170(6)：060542.
[9] CHEN S，ZHANG Q，WANG F，et al. An electrochemical-thermal-aging effects coupled model for lithium-ion batteries performance simulation and state of health estimation[J]. Applied Thermal Engineering，2024，239：122128.
[10] LI C，YANG L，LI Q，et al. SOH Estimation method for lithium-ion batteries based on an improved equivalent circuit model via electrochemical impedance spectroscopy[J]. Journal of Energy Storage，2024，86：111167.
[11] HUANG H，MENG J，WANG Y，et al. An enhanced data-driven model for lithium-ion battery state-of-health estimation with optimized features and prior knowledge[J]. Automotive Innovation，2024，5(2)：134-145.
[12] 车云弘，邓忠伟，李佳承，等. 基于数据驱动的电池系统泛化SOH估计方法[J]. 机械工程学报，2022，58(24)：253-263. CHE Yunhong，DENG Zhongwei，LI Jiacheng，et al. Generalized data-driven SOH estimation method for battery systems[J]. Journal of Mechanical Engineering，2022，58(24)：253-263.
[13] LI X，YUAN C，WANG Z. State of health estimation for Li-ion battery via partial incremental capacity analysis based on support vector regression[J]. Energy，2020，203：117852.
[14] FENG R，WANG S，YU C，et al. High precision state of health estimation of lithium-ion batteries based on strong correlation aging feature extraction and improved hybrid kernel function least squares support vector regression machine model[J]. Journal of Energy Storage，2024，90：111834.
[15] POHLMANN S，MASHAYEKH A，STROEBL F，et al. State-of-health prediction of lithium-ion batteries based on a low dimensional Gaussian process regression[J]. Journal of Energy Storage，2024，88：111649.
[16] CHEN K，LI J，LIU K，et al. State of health estimation for lithium-ion battery based on particle swarm optimization algorithm and extreme learning machine[J]. Green Energy and Intelligent Transportation，2024，3(1)：100151.
[17] LU Z，FEI Z，WANG B，et al. A feature fusion-based convolutional neural network for battery state-of-health estimation with mining of partial voltage curve[J]. Energy，2024，288：129690.
[18] XIONG X，WANG Y，JIANG C，et al. End-to-end deep learning powered battery state of health estimation considering multi-neighboring incomplete charging data[J]. Energy，2024，292：130495.
[19] QIN D，LI L，WU H，et al. A composite progressive approach alternating between electrochemical model and data-driven model for the state of health assessment of lithium-ion batteries[J]. Journal of Energy Storage，2024，97：112921.
[20] 赵敬玉，徐铖，李晓宇. 基于实际行驶数据的电动汽车能耗分析与预测[J]. 机械工程学报，2023，59(10)：263-274. ZHAO Jingyu，XU Cheng，LI Xiaoyu. Electric vehicle energy consumption analysis and prediction based on real-world driving data[J]. 2023，59(10)：263-274.
[21] WANG J，YANG H，WANG Z，et al. State of health analysis of batteries at different stages based on real-world vehicle data and machine learning[J]. Journal of Energy Storage，2024，88：111616.
[22] LI R，HONG J，ZHANG H，et al. Data-driven battery state of health estimation based on interval capacity for real- world electric vehicles[J]. Energy，2022，257：124771.
[23] FENG X，ZHANG Y，XIONG R，et al. Comprehensive performance comparison among different types of features in data-driven battery state of health estimation[J]. Applied Energy，2024，369：123555.
[24] HE N，WANG Q，LU Z，et al. Early prediction of battery lifetime based on graphical features and convolutional neural networks[J]. Applied Energy，2024，353：122048.
[25] MIN H，XIONG X，WANG P，et al. A hierarchical LSTM-based vehicle trajectory prediction method considering interaction information[J]. Automotive Innovation，2024，7(1)：71-81.
[26] ELEFTHERIADIS P，GIAZITZIS S，KOWAL J，et al. Joint state of charge and state of health estimation using bidirectional LSTM and Bayesian hyperparameter optimization[J]. IEEE Access，2024，12：80244-80254.
[27] 汪晓璐，赵筛筛，张朝龙. 基于等压能量分析与CNN-GRU-MHA的锂电池SOH估计方法[J]. 电气工程学报，2025，20(3)：233-241. WANG Xiaolu，ZHAO Shaishai，ZHANG Chaolong. SOH estimation method for lithium batteries based on iIsobaric energy analysis and CNN-GRU-MHA[J]. Journal of Electrical Engineering，2025，20(3)：233-241.
[28] 蔡艳平，王新军，姜柯，等. 基于GRNN-GSA-ELM的在线锂离子电池RUL多步预测[J]. 机械工程学报，2024，60(24)：296-308. CAI Yanping，WANG Xinjun，JIANG Ke，et al. Multi-step prediction of online lithium battery remaining useful life based on GRNN-GSA-ELM[J]. Journal of Mechanical Engineering，2024，60(24)：296-308.
[29] 刘牛，吴宁，修晓青，等. 基于恒压充电数据与堆叠模型的锂离子电池SOH估计方法[J]. 电气工程学报，2025，20(3)：250-260. LIU Niu，WU Ning，XIU Xiaoqing，et al. SOH etimation method for lithium-ion batteries based on constant voltage charging data and stacking model[J]. Journal of Electrical Engineering，2025，20(3)：250-260.
[29] 武骥，方雷超，刘兴涛，等. 基于特征优化和随机森林算法的锂离子电池SOH估计[J]. 机械工程学报，2024，60(12)：335-343. WU Ji，FANG Leichao，LIU Xingtao，et al. State of health estimation of lithium-ion battery based on feature optimization and random forest algorithm[J]. Journal of Mechanical Engineering，2024，60(12)：335-343.
[30] 李成，陈球，于莹莹，等. 基于IWOA-LSSVM的锂离子电池RUL预测[J]. 电气工程学报，2024，19(3)：399-411. LI Cheng，CHEN Qiu，YU Yingying，et al. Remaining useful life prediction for lithium-ion batteries based on IWOA-LSSVM[J]. Journal of Electrical Engineering，2024，19(3)：399-411.