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Joint State of Charge (SOC) and State of Health (SOH) Estimation for Lithium-Ion Batteries Packs of Electric Vehicles Based on NSSR-LSTM Neural Network

Author

Listed:
  • Panpan Hu

    (School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, China)

  • W. F. Tang

    (School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, China)

  • C. H. Li

    (School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, China)

  • Shu-Lun Mak

    (School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, China
    Vocational Training Council-Youth College (Kwai Chung), Hong Kong, China)

  • C. Y. Li

    (School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, China)

  • C. C. Lee

    (School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, China)

Abstract

Lithium-ion batteries (LIBs) are widely used in electrical vehicles (EVs), but safety issues with LIBs still occur frequently. State of charge (SOC) and state of health (SOH) are two crucial parameters for describing the state of LIBs. However, due to inconsistencies that may occur among hundreds to thousands of battery cells connected in series and parallel in the battery pack, these parameters can be difficult to estimate accurately. To address this problem, this paper proposes a joint SOC and SOH estimation method based on the nonlinear state space reconstruction (NSSR) and long short-term memory (LSTM) neural network. An experiment testbed was set up to measure the SOC and SOH of battery packs under different criteria and configurations, and thousands of charging/discharging cycles were recorded. The joint estimation algorithms were validated using testbed data, and the errors for SOC and SOH estimation were found to be within 2.5% and 1.3%, respectively, which is smaller than the errors obtained using traditional Ah-Integral and LSTM-only algorithms.

Suggested Citation

  • Panpan Hu & W. F. Tang & C. H. Li & Shu-Lun Mak & C. Y. Li & C. C. Lee, 2023. "Joint State of Charge (SOC) and State of Health (SOH) Estimation for Lithium-Ion Batteries Packs of Electric Vehicles Based on NSSR-LSTM Neural Network," Energies, MDPI, vol. 16(14), pages 1-19, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:14:p:5313-:d:1191699
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    References listed on IDEAS

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    1. Bingyu Sang & Zaijun Wu & Bo Yang & Junjie Wei & Youhong Wan, 2024. "Joint Estimation of SOC and SOH for Lithium-Ion Batteries Based on Dual Adaptive Central Difference H-Infinity Filter," Energies, MDPI, vol. 17(7), pages 1-16, March.

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