基于容量增量曲线与GWO-GPR的锂离子电池SOH估计

doi:10.19799/j.cnki.2095-4239.2023.0458

摘要/Abstract

摘要：

电池健康状态(state of health， SOH）的准确估计是电池管理系统的关键技术之一，对保障电动汽车安全、可靠运行至关重要。针对当前高斯过程回归(gaussian process regression，GPR)中单一核函数泛化性能不足，超参数选取易陷入局部最优导致SOH估计精度较低的问题，提出一种灰狼优化算法(grey wolf optimization，GWO)和组合核函数改进GPR的SOH估计方法。首先，基于容量增量分析法提取用于表征电池老化的特征，对电池恒流充电的容量-电压曲线插值并以差分法计算容量增量(increment capacity，IC)曲线，应用Savitzky-Golay滤波平滑处理，提取峰值高度、峰值电压及峰面积作为健康特征；其次，引入多维尺度变换(multidimensional scaling， MDS)消除特征冗余性同时降低模型计算复杂度，利用Pearson系数验证所提健康特征与SOH的相关性；然后，结合SOH退化轨迹的非线性和电池容量再生的准周期性特点，将神经网络核函数与周期核函数组合作为GPR的协方差核函数，以GWO对组合核函数超参数的初值进行优化；最后，基于NASA电池数据集将所提方法与SVR、ELM、GPR模型作对比，检验GWO-GPR模型的准确性，估计结果的最大均方根误差(root mean square error，RMSE)和平均绝对误差(mean absolute error，MAE)分别为1.03%和0.5%，以第60、80、100个循环为估计起始点，验证模型的鲁棒性，结果显示最大RMSE控制在1.03%以内。

关键词: 锂离子电池, 健康状态, 容量增量曲线, 高斯过程回归, 灰狼优化算法

Abstract:

Accurate estimation of the battery state of health (SOH) is a critical technology in battery management systems, which is crucial for ensuring the safe and reliable operation of electric vehicles. To solve the problem of low SOH estimation accuracy due to insufficient generalization performance of a single kernel function in Gaussian process regression (GPR) and the tendency of hyperparameter selection to fall into local optimality, an SOH estimation method based on the grey wolf optimization algorithm (GWO) and a combined kernel function was proposed. First, the characteristics of battery aging were extracted using incremental capacity analysis (ICA) method. The capacity-voltage curve of constant-current charging of the battery was interpolated and the increment capacity (IC) curve was calculated using the difference method. The IC curve was smoothed using Savitzky-Golay filtering, and the peak height, voltage, and area were extracted as health features. Second, multidimensional scaling (MDS) was presented to eliminate feature redundancy and reduce the computational complexity of the model. The Pearson coefficient was used to verify the correlation between the proposed health features and SOH. Then, considering the nonlinearity of the SOH degradation trajectory and the quasi-periodicity of battery capacity regeneration, the combination of the neural network kernel function and periodic kernel function was used as the covariance kernel function of GPR, and the initial hyperparameters of the combined kernel function were optimized by the GWO method. Finally, the proposed method was compared with SVR, ELM, and GPR models based on the NASA battery data set to verify the accuracy of the GWO-GPR model. The 60^th, 80^th, and 100^th cycles were used as estimation starting points to verify the robustness of the model.

Key words: lithium-ion battery, state of health, increment capacity curve, gaussian process regression, grey wolf optimization algorithm

中图分类号:

TM 912

王琛, 闵永军. 基于容量增量曲线与GWO-GPR的锂离子电池SOH估计[J]. 储能科学与技术, 2023, 12(11): 3508-3518.

Chen WANG, Yongjun MIN. SOH estimation of lithium-ion batteries based on capacity increment curve and GWO-GPR[J]. Energy Storage Science and Technology, 2023, 12(11): 3508-3518.

图/表 12

图1

图2

图3

图4

图5

图6

表1

表2

图7

表3

图8

表4

参考文献 25

1	LI L, YOU S X, YANG C, et al. Driving-behavior-aware stochastic model predictive control for plug-in hybrid electric buses[J]. Applied Energy, 2016, 162: 868-879.
2	ADAIKKAPPAN M, SATHIYAMOORTHY N. Modeling, state of charge estimation, and charging of lithium-ion battery in electric vehicle: A review[J]. International Journal of Energy Research, 2022, 46(3): 2141-2165.
3	RUAN H K, WEI Z B, SHANG W T, et al. Artificial Intelligence-based health diagnostic of Lithium-ion battery leveraging transient stage of constant current and constant voltage charging[J]. Applied Energy, 2023, 336: 120751.
4	肖浩逸, 何晓霞, 梁佳佳, 等. 一种基于模态分解和机器学习的锂电池寿命预测方法[J]. 储能科学与技术, 2022, 11(12): 3999-4009.
	XIAO H Y, HE X X, LIANG J J, et al. A lithium battery life-prediction method based on mode decomposition and machine learning[J]. Energy Storage Science and Technology, 2022, 11(12): 3999-4009.
5	LI J F, WANG D F, DENG L, et al. Aging modes analysis and physical parameter identification based on a simplified electrochemical model for lithium-ion batteries[J]. Journal of Energy Storage, 2020, 31: 101538.
6	戴彦文, 于艾清. 基于健康特征参数的CNN-LSTM&GRU组合锂电池SOH估计[J]. 储能科学与技术, 2022, 11(5): 1641-1649.
	DAI Y W, YU A Q. Combined CNN-LSTM and GRU based health feature parameters for lithium-ion batteries SOH estimation[J]. Energy Storage Science and Technology, 2022, 11(5): 1641-1649.
7	BIAN X L, WEI Z B, LI W H, et al. State-of-health estimation of lithium-ion batteries by fusing an open circuit voltage model and incremental capacity analysis[J]. IEEE Transactions on Power Electronics, 2022, 37(2): 2226-2236.
8	GUHA A, PATRA A. State of health estimation of lithium-ion batteries using capacity fade and internal resistance growth models[J]. IEEE Transactions on Transportation Electrification, 2018, 4(1): 135-146.
9	LING L Y, WEI Y. State-of-charge and state-of-health estimation for lithium-ion batteries based on dual fractional-order extended Kalman filter and online parameter identification[J]. IEEE Access, 2021, 9: 47588-47602.
10	CHU A, ALLAM A, CORDOBA ARENAS A, et al. Stochastic capacity loss and remaining useful life models for lithium-ion batteries in plug-in hybrid electric vehicles[J]. Journal of Power Sources, 2020, 478: 228991.
11	周頔, 宋显华, 卢文斌, 等. 基于日常片段充电数据的锂电池健康状态实时评估方法研究[J]. 中国电机工程学报, 2019, 39(1): 105-111, 325.
	ZHOU D, SONG X H, LU W B, et al. Real-time SOH estimation algorithm for lithium-ion batteries based on daily segment charging data[J]. Proceedings of the CSEE, 2019, 39(1): 105-111, 325.
12	ZHENG Y J, OUYANG M G, LU L G, et al. Understanding aging mechanisms in lithium-ion battery packs: From cell capacity loss to pack capacity evolution[J]. Journal of Power Sources, 2015, 278: 287-295.
13	WU J, ZHANG C B, CHEN Z H. An online method for lithium-ion battery remaining useful life estimation using importance sampling and neural networks[J]. Applied Energy, 2016, 173: 134-140.
14	YAYAN U, ARSLAN A T, YUCEL H. A novel method for SoH prediction of batteries based on stacked LSTM with quick charge data[J]. Applied Artificial Intelligence, 2021, 35(6): 421-439.
15	WANG Z P, YUAN C G, LI X Y. Lithium battery state-of-health estimation via differential thermal voltammetry with Gaussian process regression[J]. IEEE Transactions on Transportation Electrification, 2021, 7(1): 16-25.
16	NUHIC A, TERZIMEHIC T, SOCZKA-GUTH T, et al. Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods[J]. Journal of Power Sources, 2013, 239: 680-688.
17	陈琳, 王惠民, 李熠婧, 等. 用新陈代谢极限学习机实现电池健康状态估算[J]. 汽车工程, 2021, 43(1): 10-18.
	CHEN L, WANG H M, LI Y J, et al. Battery state-of-health estimation by using metabolic extreme learning machine[J]. Automotive Engineering, 2021, 43(1): 10-18.
18	YANG N K, SONG Z Y, HOFMANN H, et al. Robust state of health estimation of lithium-ion batteries using convolutional neural network and random forest[J]. Journal of Energy Storage, 2022, 48: 103857.
19	SHENG H M, LIU X, BAI L B, et al. Small sample state of health estimation based on weighted Gaussian process regression[J]. Journal of Energy Storage, 2021, 41: 102816.
20	RICHARDSON R R, OSBORNE M A, HOWEY D A. Gaussian process regression for forecasting battery state of health[J]. Journal of Power Sources, 2017, 357: 209-219.
21	申江卫, 马文赛, 肖仁鑫, 等. 基于优化高斯过程回归算法的锂离子电池可用容量估算[J]. 中国公路学报, 2022, 35(8): 31-43.
	SHEN J W, MA W S, XIAO R X, et al. Available capacity estimation of lithium-ion batteries based on optimized Gaussian process regression[J]. China Journal of Highway and Transport, 2022, 35(8): 31-43.
22	王萍, 彭香园, 程泽. 基于DTV-IGPR模型的锂离子电池SOH估计方法[J]. 汽车工程, 2021, 43(11): 1710-1719.
	WANG P, PENG X Y, CHENG Z. SOH estimation method for lithium-ion batteries based on DTV-IGPR model[J]. Automotive Engineering, 2021, 43(11): 1710-1719.
23	HUANG H, HU S Y, SUN Y. A discrete curvature estimation based low-distortion adaptive savitzky-golay filter for ECG denoising[J]. Sensors, 2019, 19(7): 1617.
24	陈峥, 李磊磊, 舒星, 等. 基于改进容量增量分析法的锂电池可用容量估计[J]. 中国公路学报, 2022, 35(8): 20-30.
	CHEN Z, LI L L, SHU X, et al. Estimation of available capacity for lithium-ion battery based on improved increment capacity analysis[J]. China Journal of Highway and Transport, 2022, 35(8): 20-30.
25	王瑞洁, 惠周利, 杨明. 基于间接健康指标的高斯过程回归对锂电池SOH预测[J]. 储能科学与技术, 2023, 12(2): 560-569.
	WANG R J, HUI Z L, YANG M. Gaussian process regression based on indirect health indicators for SOH estimation of lithium battery[J]. Energy Storage Science and Technology, 2023, 12(2): 560-569.

健康指标	B0005	B0006	B0007
峰值高度	0.9913	0.9914	0.9871
峰值电压	-0.9550	-0.9865	-0.9562
峰面积	0.9953	0.9922	0.9916

电池型号	Pearson系数
B0005	0.9920
B0006	0.9931
B0007	0.9878

电池型号	GWO-GPR		SVR		ELM		GPR		文献[25]
电池型号	RMSE	MAE	RMSE	MAE	RMSE	MAE	RMSE	MAE	RMSE
B0005	0.0067	0.0038	0.0127	0.0099	0.0190	0.0167	0.0156	0.0128	0.0072
B0006	0.0103	0.0050	0.0282	0.0214	0.0141	0.0092	0.0244	0.0180	0.0196
B0007	0.0073	0.0035	0.0099	0.0074	0.0162	0.0138	0.0232	0.0171	0.0210

起始循环	B0005	B0006	B0007
60	0.0070	0.0103	0.0089
80	0.0067	0.0103	0.0073
100	0.0046	0.0073	0.0052

[1]	李悦, 王博, 吴楠. 石墨烯/Si/SiO _x 纳米复合材料的制备及储锂性能研究[J]. 储能科学与技术, 2023, 12(9): 2752-2759.
[2]	曾少鸿, 吴伟雄, 刘吉臻, 汪双凤, 叶石丰, 冯振宇. 锂离子电池浸没式冷却技术研究综述[J]. 储能科学与技术, 2023, 12(9): 2888-2903.
[3]	陈欣, 李云伍, 梁新成, 李法霖, 张志冬. 基于模态分解的Transformer-GRU联合电池健康状态估计[J]. 储能科学与技术, 2023, 12(9): 2927-2936.
[4]	李纪伟, 刘睿涵, 吕桃林, 潘隆, 马常军, 李清波, 赵芝芸, 杨文, 解晶莹. 基于局部离群点检测和标准差方法的锂离子电池组早期故障诊断[J]. 储能科学与技术, 2023, 12(9): 2917-2926.
[5]	黄晓伟, 李少鹏, 张校刚. 负极补锂锂化裕度对电芯性能的影响及机理研究[J]. 储能科学与技术, 2023, 12(9): 2727-2734.
[6]	周向阳, 胡颖杰, 梁家浩, 周其杰, 文康, 陈松, 杨娟, 唐晶晶. 天然鳞片石墨球化尾料的高性能负极材料制备及储锂特性研究[J]. 储能科学与技术, 2023, 12(9): 2767-2777.
[7]	江婉薇, 梁呈景, 钱历, 刘梅城, 朱孟想, 马骏. 锡基三维石墨烯泡沫调控及其锂电池负极性能[J]. 储能科学与技术, 2023, 12(9): 2746-2751.
[8]	高欣, 王若谷, 高文菁, 邓泽军, 梁睿祺, 杨騉. 基于运行数据的储能电站电池组一致性评估方法[J]. 储能科学与技术, 2023, 12(9): 2937-2945.
[9]	官亦标, 沈进冉, 刘家亮, 渠展展, 高飞, 刘施阳, 郭翠静, 周淑琴, 付珊珊. 以安全高质量应用为导向的储能锂离子电池综合性能评价标准[J]. 储能科学与技术, 2023, 12(9): 2946-2953.
[10]	郭煜, 王亦伟, 钟隽, 杜进桥, 田杰, 李艳, 蒋方明. 基于增量容量曲线的锂离子电池微内短路故障诊断方法[J]. 储能科学与技术, 2023, 12(8): 2536-2546.
[11]	唐程波, 锁要红, 何昭坤. 基于正弦函数的液冷板上流体流向对锂离子电池散热性能的影响[J]. 储能科学与技术, 2023, 12(8): 2547-2555.
[12]	左安昊, 方儒卿, 李哲. 锂离子电池单颗粒动力学表征方法综述[J]. 储能科学与技术, 2023, 12(8): 2457-2481.
[13]	陈满, 程志翔, 赵春朋, 彭鹏, 雷旗开, 金凯强, 王青松. 锂离子电池储能集装箱爆炸危害数值模拟[J]. 储能科学与技术, 2023, 12(8): 2594-2605.
[14]	黄永浩, 臧国景, 朱霨亚, 廖友好, 李伟善. LiF添加剂改善含锂陶瓷隔膜与4.35 V LiNi_0.8Co_0.1Mn_0.1O₂ 正极的界面稳定性[J]. 储能科学与技术, 2023, 12(8): 2361-2369.
[15]	杨佳兴, 张恒运, 徐屹东. 基于电化学-热耦合模型的锂离子电池组件产热分析[J]. 储能科学与技术, 2023, 12(8): 2615-2625.