Novel State of Health Estimation for Lithium-Ion Battery Based on Differential Evolution Algorithm-Extreme Learning Machine

doi:10.1007/s12204-024-2727-y

Abstract

Abstract: To ensure a long-term safety and reliability of electric vehicle and energy storage system, an accurate estimation of the state of health (SOH) for lithium-ion battery is important. In this study, a method for estimating the lithium-ion battery SOH was proposed based on an improved extreme learning machine (ELM). Input weights and hidden layer biases were generated randomly in traditional ELM. To improve the estimation accuracy of ELM, the differential evolution algorithm was used to optimize these parameters in feasible solution spaces. First, incremental capacity curves were obtained by incremental capacity analysis and smoothed by Gaussian filter to extract health interests. Then, the ELM based on differential evolution algorithm (DE-ELM model) was used for a lithium-ion battery SOH estimation. At last, four battery historical aging data sets and one random walk data set were employed to validate the prediction performance of DE-ELM model. Results show that the DE-ELM has a better performance than other studied algorithms in terms of generalization ability.

Key words: lithium-ion battery, state of health (SOH), extreme learning machine (ELM), differential evolution (DE) algorithm

摘要： 为了保证电动汽车和储能系统的长期安全可靠运行，锂离子电池健康状态（SOH）的准确估计至关重要。提出了一种基于改进极限学习机（ELM）的锂离子电池健康状态估计方法。传统极限学习机的输入权值和隐层偏差是随机产生的。为了提高极限学习机的估计精度，使用差分进化算法在可行解空间中对这些参数进行优化。首先，通过增量容量分析得到增量容量曲线，并进行高斯滤波平滑，提取健康因子；然后，将基于差分进化算法的极限学习机（DE-ELM模型）用于锂离子电池健康状态估计。最后，利用4个电池历史老化数据集和1个随机漫步数据集验证了DE-ELM模型的预测性能。结果表明，DE-ELM算法在泛化能力方面优于对比算法。

关键词: 锂离子电池，健康状态，极限学习机，差分进化算法

CLC Number:

TK02
TP391

Li Qingwei, Fu Can, Xue Wenli, Wei Yongqiang, Shen Zhiwen. Novel State of Health Estimation for Lithium-Ion Battery Based on Differential Evolution Algorithm-Extreme Learning Machine[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(2): 252-261.

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