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An electrochemical model based degradation state identification method of Lithium-ion battery for all-climate electric vehicles application

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  • Xiong, Rui
  • Li, Linlin
  • Li, Zhirun
  • Yu, Quanqing
  • Mu, Hao

Abstract

The Lithium-ion batteries (LiBs) are the core component of the all-climate electric vehicles. The aging state recognition is carried out based on the proposed electrochemical model (EM) instead of the traditional equivalent circuit model (ECM) and black boxes model in this paper. Firstly, a group of mathematical equations are built to describe the physical and chemical behaviors of batteries based on the electrochemical theory. Then, the finite analysis method and the numerical computation method are used to solve the mathematical equations and the model has been built. Next, the optimization algorithm is used for identifying the parameters of the model. The aging state recognition of the battery on whole lifetime is carrying out based on the ageing data. Five aging characteristic parameters are determined to describe the health state of the battery, and their degradation trajectories are obtained. Finally, a battery-in-loop approach is employed to verify the model based degradation recognition. Results show that the maximum voltage error is within 50 mV and the state of health estimation error is bounded to 3%.

Suggested Citation

  • Xiong, Rui & Li, Linlin & Li, Zhirun & Yu, Quanqing & Mu, Hao, 2018. "An electrochemical model based degradation state identification method of Lithium-ion battery for all-climate electric vehicles application," Applied Energy, Elsevier, vol. 219(C), pages 264-275.
    • Handle: RePEc:eee:appene:v:219:y:2018:i:c:p:264-275
    DOI: 10.1016/j.apenergy.2018.03.053
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    References listed on IDEAS

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    1. Xiong, Rui & Duan, Yanzhou & Cao, Jiayi & Yu, Quanqing, 2018. "Battery and ultracapacitor in-the-loop approach to validate a real-time power management method for an all-climate electric vehicle," Applied Energy, Elsevier, vol. 217(C), pages 153-165.
    2. Xiong, Rui & Cao, Jiayi & Yu, Quanqing, 2018. "Reinforcement learning-based real-time power management for hybrid energy storage system in the plug-in hybrid electric vehicle," Applied Energy, Elsevier, vol. 211(C), pages 538-548.
    3. Xiong, Rui & Sun, Fengchun & Chen, Zheng & He, Hongwen, 2014. "A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion olymer battery in electric vehicles," Applied Energy, Elsevier, vol. 113(C), pages 463-476.
    4. Xiong, Rui & Yu, Quanqing & Wang, Le Yi & Lin, Cheng, 2017. "A novel method to obtain the open circuit voltage for the state of charge of lithium ion batteries in electric vehicles by using H infinity filter," Applied Energy, Elsevier, vol. 207(C), pages 346-353.
    5. Waag, Wladislaw & Käbitz, Stefan & Sauer, Dirk Uwe, 2013. "Experimental investigation of the lithium-ion battery impedance characteristic at various conditions and aging states and its influence on the application," Applied Energy, Elsevier, vol. 102(C), pages 885-897.
    6. Xiong, Rui & Tian, Jinpeng & Mu, Hao & Wang, Chun, 2017. "A systematic model-based degradation behavior recognition and health monitoring method for lithium-ion batteries," Applied Energy, Elsevier, vol. 207(C), pages 372-383.
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