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SSA-BiLSTM Model-Based SOH Estimation for Lithium-Ion Batteries

Author

Listed:
  • Yizeng Wu

    (Tsinghua Shenzhen International Graduate School, Shenzhen 518055, China
    Shenzhen Power Supply Co., Ltd, Guangdong Provincial Key Laboratory of Source-Grid-Load-Storage Interactive Collaborative Technology (No. 2024B1212020004), Shenzhen 518000, China)

  • Bo Rao

    (Shenzhen Power Supply Co., Ltd, Guangdong Provincial Key Laboratory of Source-Grid-Load-Storage Interactive Collaborative Technology (No. 2024B1212020004), Shenzhen 518000, China)

  • Jie Tian

    (Shenzhen Power Supply Co., Ltd, Guangdong Provincial Key Laboratory of Source-Grid-Load-Storage Interactive Collaborative Technology (No. 2024B1212020004), Shenzhen 518000, China)

  • Jinqiao Du

    (Shenzhen Power Supply Co., Ltd, Guangdong Provincial Key Laboratory of Source-Grid-Load-Storage Interactive Collaborative Technology (No. 2024B1212020004), Shenzhen 518000, China)

  • Jiuchun Jiang

    (Zhuhai Campus, Beijing Institute of Technology, Zhuhai 519088, China
    School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China)

Abstract

The State of Health (SOH) of a battery is an important indicator for measuring the performance degradation of batteries. In view of the deficiencies of existing SOH estimation methods in feature processing and model accuracy, this paper conducts research on high-precision SOH estimation methods for lithium-ion batteries. A BiLSTM model optimized by the Sparrow Search Algorithm (SSA) is adopted for SOH estimation. The SSA-BiLSTM model is constructed, and the experiments are conducted on multiple types of battery datasets, such as NCM811 and LFP, and the cross-validation strategy is used to evaluate the model’s performance. The experimental results show that the SOH prediction system software developed based on this model has the functions of rapid estimation and three-dimensional trend visualization. The paper verifies the functions of the SOH prediction system software developed by the model, which has practical reference significance for the development and application of SOH estimation systems in energy storage scenarios.

Suggested Citation

  • Yizeng Wu & Bo Rao & Jie Tian & Jinqiao Du & Jiuchun Jiang, 2026. "SSA-BiLSTM Model-Based SOH Estimation for Lithium-Ion Batteries," Energies, MDPI, vol. 19(6), pages 1-22, March.
    • Handle: RePEc:gam:jeners:v:19:y:2026:i:6:p:1499-:d:1896811
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