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State of Health Estimation for Lithium-Ion Batteries Based on Transferable Long Short-Term Memory Optimized Using Harris Hawk Algorithm

Author

Listed:
  • Guangyi Yang

    (Engineering Research Center of Automotive Electronics Drive Control and System Integration, Harbin University of Science and Technology, Ministry of Education, Harbin 150080, China)

  • Xianglin Wang

    (School of Electrical and Electronic Engineering, Harbin University of Science and Technology, Harbin 150080, China)

  • Ran Li

    (Engineering Research Center of Automotive Electronics Drive Control and System Integration, Harbin University of Science and Technology, Ministry of Education, Harbin 150080, China)

  • Xiaoyu Zhang

    (College of Artificial Intelligence, Nankai University, Tianjin 300110, China)

Abstract

Accurately estimating the state of health (SOH) of lithium-ion batteries ensures the proper operation of the battery management system (BMS) and promotes the second-life utilization of retired batteries. The challenges of existing lithium-ion battery SOH prediction techniques primarily stem from the different battery aging mechanisms and limited model training data. We propose a novel transferable SOH prediction method based on a neural network optimized by Harris hawk optimization (HHO) to address this challenge. The battery charging data analysis involves selecting health features highly correlated with SOH. The Spearman correlation coefficient assesses the correlation between features and SOH. We first combined the long short-term memory (LSTM) and fully connected (FC) layers to form the base model (LSTM-FC) and then retrained the model using a fine-tuning strategy that freezes the LSTM hidden layers. Additionally, the HHO algorithm optimizes the number of epochs and units in the FC and LSTM hidden layers. The proposed method demonstrates estimation effectiveness using multiple aging data from the NASA, CALCE, and XJTU databases. The experimental results demonstrate that the proposed method can accurately estimate SOH with high precision using low amounts of sample data. The RMSE is less than 0.4%, and the MAE is less than 0.3%.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:15:p:6316-:d:1441554
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    References listed on IDEAS

    as
    1. Lin, Mingqiang & Yan, Chenhao & Wang, Wei & Dong, Guangzhong & Meng, Jinhao & Wu, Ji, 2023. "A data-driven approach for estimating state-of-health of lithium-ion batteries considering internal resistance," Energy, Elsevier, vol. 277(C).
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    4. 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).
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