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A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion olymer battery in electric vehicles

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  • Xiong, Rui
  • Sun, Fengchun
  • Chen, Zheng
  • He, Hongwen

Abstract

Accurate estimations of battery parameter and state play an important role in promoting the commercialization of electric vehicles. This paper tries to make three contributions to the existing literatures through advanced time scale separation algorithm. (1) A lumped parameter battery model was improved for achieving accurate voltage estimate against different battery aging levels through an electrochemical equation, which has enhanced the relationship of battery voltage to its State-of-Charge (SoC) and capacity. (2) A multi-scale extended Kalman filtering was proposed and employed to execute the online measured data driven-based battery parameter and SoC estimation with dual time scales in regarding that the slow-varying characteristic on battery parameter and fast-varying characteristic on battery SoC, thus the battery parameter was estimated with macro scale and battery SoC was estimated with micro scale. (3) The accurate estimate of battery capacity and SoC were obtained in real-time through a data-driven multi-scale extended Kalman filtering algorithm. Experimental results on various degradation states of lithium-ion polymer battery cells further verified the feasibility of the proposed approach.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:113:y:2014:i:c:p:463-476
    DOI: 10.1016/j.apenergy.2013.07.061
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    References listed on IDEAS

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    1. Waag, Wladislaw & Sauer, Dirk Uwe, 2013. "Adaptive estimation of the electromotive force of the lithium-ion battery after current interruption for an accurate state-of-charge and capacity determination," Applied Energy, Elsevier, vol. 111(C), pages 416-427.
    2. Hu, Chao & Youn, Byeng D. & Chung, Jaesik, 2012. "A multiscale framework with extended Kalman filter for lithium-ion battery SOC and capacity estimation," Applied Energy, Elsevier, vol. 92(C), pages 694-704.
    3. He, Yao & Liu, XingTao & Zhang, ChenBin & Chen, ZongHai, 2013. "A new model for State-of-Charge (SOC) estimation for high-power Li-ion batteries," Applied Energy, Elsevier, vol. 101(C), pages 808-814.
    4. Rui Xiong & Hongwen He & Fengchun Sun & Kai Zhao, 2012. "Online Estimation of Peak Power Capability of Li-Ion Batteries in Electric Vehicles by a Hardware-in-Loop Approach," Energies, MDPI, vol. 5(5), pages 1-15, May.
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