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A Systematic Framework for State of Charge, State of Health and State of Power Co-Estimation of Lithium-Ion Battery in Electric Vehicles

Author

Listed:
  • Tao Zhang

    (Chassis Components Technical, China North Vehicle Research Institute, Beijing 100072, China)

  • Ningyuan Guo

    (National Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering, and Collaborative Innovation Center of Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China)

  • Xiaoxia Sun

    (Chassis Components Technical, China North Vehicle Research Institute, Beijing 100072, China)

  • Jie Fan

    (New Energy Center, China Automotive Engineering Research Institute Co., Ltd., Chongqing 401122, China)

  • Naifeng Yang

    (Chassis Components Technical, China North Vehicle Research Institute, Beijing 100072, China)

  • Junjie Song

    (Chassis Components Technical, China North Vehicle Research Institute, Beijing 100072, China)

  • Yuan Zou

    (National Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering, and Collaborative Innovation Center of Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China)

Abstract

Due to its advantages of high voltage level, high specific energy, low self-discharging rate and relatively longer cycling life, the lithium-ion battery has been widely used in electric vehicles. To ensure safety and reduce degradation during the lithium-ion battery’s service life, precise estimation of its states like state of charge (SOC), capacity and peak power is indispensable. This paper proposes a systematic co-estimation framework for the lithium-ion battery in electric vehicle applications. First, a linearized equivalent circuit-based battery model, together with an affine projection algorithm is used to estimate the model parameters. Then the state of health (SOH) estimator is triggered weekly or semi-monthly offline to update capacity based on the three-dimensional response surface open circuit voltage model and particle swarm optimization algorithm for accurate online SOC and state of power (SOP) estimation. At last, the Unscented Kalman Filter utilizes the estimated model parameters and updated capacity to estimate SOC online and the SOP estimator provides the power limitations considering SOC, current and voltage constraints, taking advantage of the information from both SOH and SOC estimators. Experiments show that the relative error of the SOH estimator is under 1% in all aging states whatever the loading profile is. The mean absolute SOC estimation error is under 1.6% even when the battery undergoes 744 aging cycles. The SOP estimator is validated by means of the calibrated battery model based on the HPPC test and its performance is ideal.

Suggested Citation

  • Tao Zhang & Ningyuan Guo & Xiaoxia Sun & Jie Fan & Naifeng Yang & Junjie Song & Yuan Zou, 2021. "A Systematic Framework for State of Charge, State of Health and State of Power Co-Estimation of Lithium-Ion Battery in Electric Vehicles," Sustainability, MDPI, vol. 13(9), pages 1-19, May.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:9:p:5166-:d:549231
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    References listed on IDEAS

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    Cited by:

    1. Zhang, Xudong & Fan, Jie & Zou, Yuan & Sun, Wei, 2023. "Realizing accurate battery capacity estimation using 4 min 1C discharging data," Energy, Elsevier, vol. 282(C).

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