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Combined State-of-Charge Estimation Method for Lithium-Ion Batteries Using Long Short-Term Memory Network and Unscented Kalman Filter

Author

Listed:
  • Long Pu

    (School of Mechanical Engineering, Sichuan University of Science and Engineering, Zigong 643000, China)

  • Chun Wang

    (School of Mechanical Engineering, Sichuan University of Science and Engineering, Zigong 643000, China
    Sichuan Provincial Key Lab of Process Equipment and Control, Sichuan University of Science and Engineering, Zigong 643000, China)

Abstract

The state of charge (SOC) of lithium-ion batteries (LIBs) is a pivotal metric within the battery management system (BMS) of electric vehicles (EVs). An accurate SOC is crucial to ensuring both the safety and the operational efficiency of a battery. The unscented Kalman filter (UKF) is a classic and commonly used method among the various SOC estimation algorithms. However, an unscented transform (UT) utilized in the algorithm struggles to completely simulate the probability density function of actual data. Additionally, inaccuracies in the identification of battery model parameters can lead to performance degradation or even the divergence of the algorithm in SOC estimation. To address these challenges, this study introduces a combined UKF-LSTM algorithm that integrates a long short-term memory (LSTM) network with the UKF for the precise SOC estimation of LIBs. Firstly, the particle swarm optimization (PSO) algorithm was utilized to accurately identify the parameters of the battery model. Secondly, feature parameters that exhibited a high correlation with the estimation error of the UKF were selected to train an LSTM network, which was then combined with the UKF to establish the joint algorithm. Lastly, the effectiveness of the UKF-LSTM was confirmed under various conditions. The outcomes demonstrate that the average absolute error (MAE) and the root mean square error (RMSE) for the SOC estimation by the algorithm were less than 0.7%, indicating remarkable estimation accuracy and robustness.

Suggested Citation

  • Long Pu & Chun Wang, 2025. "Combined State-of-Charge Estimation Method for Lithium-Ion Batteries Using Long Short-Term Memory Network and Unscented Kalman Filter," Energies, MDPI, vol. 18(5), pages 1-16, February.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:5:p:1106-:d:1598628
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    References listed on IDEAS

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    1. Zeeshan Ahmad Khan & Prashant Shrivastava & Syed Muhammad Amrr & Saad Mekhilef & Abdullah A. Algethami & Mehdi Seyedmahmoudian & Alex Stojcevski, 2022. "A Comparative Study on Different Online State of Charge Estimation Algorithms for Lithium-Ion Batteries," Sustainability, MDPI, vol. 14(12), pages 1-19, June.
    2. Semeraro, Concetta & Caggiano, Mariateresa & Olabi, Abdul-Ghani & Dassisti, Michele, 2022. "Battery monitoring and prognostics optimization techniques: Challenges and opportunities," Energy, Elsevier, vol. 255(C).
    3. Chai, Xuqing & Li, Shihao & Liang, Fengwei, 2024. "A novel battery SOC estimation method based on random search optimized LSTM neural network," Energy, Elsevier, vol. 306(C).
    4. Chen, Junxiong & Zhang, Yu & Wu, Ji & Cheng, Weisong & Zhu, Qiao, 2023. "SOC estimation for lithium-ion battery using the LSTM-RNN with extended input and constrained output," Energy, Elsevier, vol. 262(PA).
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