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A novel battery health indicator and PSO-LSSVR for LiFePO4 battery SOH estimation during constant current charging

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  • Chen, Junxiong
  • Hu, Yuanjiang
  • Zhu, Qiao
  • Rashid, Haroon
  • Li, Hongkun

Abstract

Efficient battery health indicator (HI) extraction and accurate estimation method are two important issues in the study of battery state of health (SOH) estimation. Although machine learning-based methods have been widely applied to the battery SOH estimation in recent years, the battery HI extraction in most studies is too tedious, the estimation method lacks pertinence, and the aging pattern of the battery aging dataset is simple. To solve the above problems, this paper proposes a novel battery HI based on the charging duration of the equal voltage intervals in the constant current charging process, which can effectively characterize the battery aging characteristics by only 10 continuous charging duration counts directly from the battery management system. Considering the difficulty of collecting battery aging data and the high dimensionality of the extracted HI, the least squares support vector regression (LSSVR), which is suitable for small samples and high dimensional data, is used to build the SOH mapping model and the optimal hyperparameters are found with the help of particle swarm optimization (PSO). The satisfactory SOH estimation accuracy of the proposed method is validated on a public LiFePO4 battery aging dataset containing different temperatures, discharge rates, discharge depths and cycle intervals.

Suggested Citation

  • Chen, Junxiong & Hu, Yuanjiang & Zhu, Qiao & Rashid, Haroon & Li, Hongkun, 2023. "A novel battery health indicator and PSO-LSSVR for LiFePO4 battery SOH estimation during constant current charging," Energy, Elsevier, vol. 282(C).
    • Handle: RePEc:eee:energy:v:282:y:2023:i:c:s036054422302176x
    DOI: 10.1016/j.energy.2023.128782
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    Cited by:

    1. Feng, Juqiang & Cai, Feng & Zhao, Yang & Zhang, Xing & Zhan, Xinju & Wang, Shunli, 2024. "A novel feature optimization and ensemble learning method for state-of-health prediction of mining lithium-ion batteries," Energy, Elsevier, vol. 299(C).
    2. Xiong, Xin & Wang, Yujie & Jiang, Cong & Zhang, Xingchen & Xiang, Haoxiang & Chen, Zonghai, 2024. "End-to-end deep learning powered battery state of health estimation considering multi-neighboring incomplete charging data," Energy, Elsevier, vol. 292(C).
    3. Guangyi Yang & Xianglin Wang & Ran Li & Xiaoyu Zhang, 2024. "State of Health Estimation for Lithium-Ion Batteries Based on Transferable Long Short-Term Memory Optimized Using Harris Hawk Algorithm," Sustainability, MDPI, vol. 16(15), pages 1-19, July.
    4. Wang, Siwei & Xiao, Xinping & Ding, Qi, 2024. "A novel fractional system grey prediction model with dynamic delay effect for evaluating the state of health of lithium battery," Energy, Elsevier, vol. 290(C).

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