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Data-Driven Degradation Modeling and SOH Prediction of Li-Ion Batteries

Author

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  • Bo Pang

    (Department of Mechanical Engineering, Institute for Integrated Energy System, University of Victoria, Victoria, BC V8W 2Y2, Canada)

  • Li Chen

    (Department of Mechanical Engineering, Institute for Integrated Energy System, University of Victoria, Victoria, BC V8W 2Y2, Canada)

  • Zuomin Dong

    (Department of Mechanical Engineering, Institute for Integrated Energy System, University of Victoria, Victoria, BC V8W 2Y2, Canada)

Abstract

Electrified vehicles (EV) and marine vessels represent promising clean transportation solutions to reduce or eliminate petroleum fuel use, greenhouse gas emissions and air pollutants. The presently commonly used electric energy storage system (ESS) is based on lithium-ion batteries. These batteries are the electrified or hybridized powertrain’s most expensive component and show noticeable performance degradations under different use patterns. Therefore, battery life prediction models play a key role in realizing globally optimized EV design and energy control strategies. This research studies the data-driven modelling and prediction methods for Li-ion batteries’ performance degradation behaviour and the state of health (SOH) estimation. The research takes advantage of the increasingly available battery test and data to reduce prediction errors of the widely used semi-empirical modelling methods. Several data-driven modelling techniques have been applied, improved, and compared to identify their advantages and limitations. The data-driven approach and Kalman Filter (KF) algorithm are used to estimate and predict the degradation of the battery during operation. The combined algorithm of Gaussian Process Regression (GPR) and Extended Kalman Filter (EKF) showed higher accuracy than other algorithms.

Suggested Citation

  • Bo Pang & Li Chen & Zuomin Dong, 2022. "Data-Driven Degradation Modeling and SOH Prediction of Li-Ion Batteries," Energies, MDPI, vol. 15(15), pages 1-12, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:15:p:5580-:d:877610
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    References listed on IDEAS

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    1. Chang, Yang & Fang, Huajing, 2019. "A hybrid prognostic method for system degradation based on particle filter and relevance vector machine," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 51-63.
    2. Li Chen & Yuqi Tong & Zuomin Dong, 2020. "Li-Ion Battery Performance Degradation Modeling for the Optimal Design and Energy Management of Electrified Propulsion Systems," Energies, MDPI, vol. 13(7), pages 1-19, April.
    3. Shaheen, Susan A & Lipman, Timothy E, 2007. "Reducing Greenhouse Emissions and Fuel Consumption: Sustainable Approaches for Surface Transportation," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt5c66j062, Institute of Transportation Studies, UC Berkeley.
    4. Shaheen, Susan & Lipman, Timothy, 2007. "Reducing Greenhouse Emissions and Fuel Consumption," Institute of Transportation Studies, Working Paper Series qt69c427zk, Institute of Transportation Studies, UC Davis.
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    Cited by:

    1. Piotr Szewczyk & Andrzej Łebkowski, 2022. "Comparative Studies on Batteries for the Electrochemical Energy Storage in the Delivery Vehicle," Energies, MDPI, vol. 15(24), pages 1-28, December.
    2. Chunling Wu & Juncheng Fu & Xinrong Huang & Xianfeng Xu & Jinhao Meng, 2023. "Lithium-Ion Battery Health State Prediction Based on VMD and DBO-SVR," Energies, MDPI, vol. 16(10), pages 1-16, May.

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