基于减平均优化算法与双向长短期记忆网络的锂离子电池健康状态估算

doi:10.19799/j.cnki.2095-4239.2024.0526

摘要/Abstract

摘要：

准确的健康状态(state of health，SOH)估算可以确保锂离子电池安全可靠运行，延长其使用寿命。针对当前许多健康特征无法表征电池老化机理，异常工况时无法准确追踪SOH变化趋势的问题，本文提出一种经验模型与数据驱动相结合的SOH估算方法。将锂离子电池负极固体电解质界面(SEI)膜增厚机理融入Arrhenius定律中构建经验模型，然后采用最小二乘法进行参数辨识，并分别计算每个参数与容量的Spearman相关系数。结果表明，它们与容量衰退都具有强相关性，可以作为估算SOH的健康特征。此外，为了克服双向长短期记忆(bidirectional long and short term memory，BiLSTM)网络参数较多且容易陷入过拟合的问题，本文使用减平均优化(subtraction average based optimizer，SABO)算法对BiLSTM的超参数进行寻优，建立SOH估算模型。最后，采用实验测试数据与美国航空航天局(National Aeronautics and Space Administration，NASA)数据验证了所提方法的适应性，并与长短期记忆(long and short-term memory，LSTM)网络、双向长短期记忆网络以及粒子群优化(particle swarm optimization，PSO)的双向长短期记忆网络3种算法的估算结果进行对比。结果表明，采用SABO-BiLSTM算法估算4节电池SOH的平均绝对百分比误差分别为0.043%、0.053%、0.259%、0.230%，相较于LSTM降低了94.58%、92.85%、88.65%、90.13%，相较于BiLSTM降低了89.11%、91.60%、77.90%、76.41%，相较于PSO-BiLSTM降低了58.65%、58.91%、65.37%、69.29%。

关键词: 锂离子电池, Arrhenius定律, 减平均优化算法, 双向长短期记忆网络

Abstract:

Accurate estimation of state of health (SOH) is crucial for ensuring the safe and reliable operation of lithium-ion batteries and extending their service life. To address the challenges posed by many health features that fail to characterize the aging mechanism of batteries and the inability to accurately track SOH trends under abnormal working conditions, this study introduces an SOH estimation approach that combines empirical models with data-driven techniques. The lithium-ion battery anode SEI film thickening mechanism is incorporated into Arrhenius' law to develop an empirical model. Parameters are identified using the least-squares method, and Spearman's correlation coefficients are calculated for each parameter and capacity. The results indicate strong correlations between these parameters and capacity decline, confirming their viability as reliable health indicators for estimating SOH. Furthermore, to address the issue of the bidirectional long and short term memory (BiLSTM) network having excessive parameters and being prone to overfitting, this study employs the subtraction average based optimizer (SABO) algorithm to optimize the hyperparameters of the BiLSTM and develop the SOH estimation model. The proposed approach is validated using experimental test data and data from the National Aeronautics and Space Administration data. In addition, the performance of the method is compared with the estimation results from three algorithms: long and short-term memory (LSTM) network, BiLSTM network, and BiLSTM network with Particle Swarm Optimization (PSO). The results reveal that the SABO-BiLSTM model achieves mean absolute percentage errors of 0.043%, 0.053%, 0.259%, and 0.230% for the SOH estimation of four batteries. These values represent reductions of 94.71%, 92.62%, 88.75%, and 90.13% compared to the LSTM model, reductions of 89.11%, 91.60%, 77.90%, and 76.41% compared to the BiLSTM model, and reductions of 58.65%, 58.91%, 65.37%, and 69.29% compared to the PSO-BiLSTM model.

Key words: lithium-ion batteries, Arrhenius law, subtraction average based optimizer, bidirectional long and short-term memory networks

中图分类号:

TM 912

李建萱, 林琛, 周忠凯. 基于减平均优化算法与双向长短期记忆网络的锂离子电池健康状态估算[J]. 储能科学与技术, 2025, 14(1): 358-369.

Jianxuan LI, Chen LIN, Zhongkai ZHOU. State of health estimation based on subtraction average based optimizer and bidirectional long and short term memory networks for lithium-ion batteries[J]. Energy Storage Science and Technology, 2025, 14(1): 358-369.

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步骤	步骤名称	电流	时间	截止条件
1	恒流充电	1/2 C	—	U ≥4.2 V
2	恒压充电	—	—	I≤1.5 A
3	静置	—	15 min	—
4	恒流放电	1 C	—	U≤3 V
5	静置	—	15 min	—
6	重复1~5步	—	—	SOH≤80%

项目	参数
正极材料	LiNi_0.5M_n0.3Co_0.2
额定容量	2500 mAh
额定电压	3.65 V
最大充电倍率	3 C
生产商	天津力神电池股份有限公司

参数	T21	T22	B005	B007
a	0.999	0.999	0.994	0.997
b	-0.999	-0.999	-0.996	-0.997
c	0.985	0.984	0.841	0.886

Cell	Criteria	LSTM	BiLSTM	PSO-BiLSTM	SABO-BiLSTM
T21	MAE/%	0.699	0.341	0.091	0.036
	RMSE/%	1.549	0.439	0.163	0.054
	MAPE/%	0.794	0.395	0.104	0.043
	Training time/s	1.660	9.753	281.812	103.183

T22	MAE/%	0.659	0.562	0.110	0.045
	RMSE/%	1.465	0.673	0.192	0.069
	MAPE/%	0.741	0.631	0.129	0.053
	Training time/s	1.196	8.061	330.265	109.753

Cell	Criteria	LSTM	BiLSTM	PSO-BiLSTM	SABO-BiLSTM
B005	MAE/%	1.816	0.900	0.636	0.207
	RMSE/%	2.502	1.242	0.938	0.278
	MAPE/%	2.282	1.172	0.748	0.259
	Training time/s	0.690	5.326	168.793	64.482

B007	MAE/%	1.957	0.842	0.663	0.205
	RMSE/%	2.717	1.019	0.965	0.267
	MAPE/%	2.331	0.975	0.749	0.230
	Training time/s	0.741	5.131	182.526	59.685