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A new neural network model for the state-of-charge estimation in the battery degradation process

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  • Kang, LiuWang
  • Zhao, Xuan
  • Ma, Jian

Abstract

Battery state-of-charge (SOC) is a key parameter of the battery management system in the electric vehicle. To predict the practicable capacity of the battery in the degradation process, the cycle life model is built based on the aging cycle tests of the 6Ah Lithium Ion battery. Combined with the cycle life model, a new Radial Basis Function Neural Network (RBFNN) model is proposed to eliminate the battery degradation’s effect on the SOC estimation accuracy of the original trained model. This proposed model is verified through the 6Ah Lithium Ion battery. First, Urban Dynamometer Driving Schedule (UDDS) and Economic Commission of Europe (ECE) cycles are experimented on the batteries under different temperatures and aging levels. Then, the robustness of the new RBFNN model against different aging levels, temperatures and loading profiles is tested with the datasets of the experiments and compared against the conventional neural network model. The simulations show that the new model can improve the accuracy of the SOC estimation effectively and has a good robustness against varying aging cycles, temperatures and loading profiles. Finally, the measurement of actual aging cycles of the battery in electric vehicles is discussed for the SOC estimation.

Suggested Citation

  • Kang, LiuWang & Zhao, Xuan & Ma, Jian, 2014. "A new neural network model for the state-of-charge estimation in the battery degradation process," Applied Energy, Elsevier, vol. 121(C), pages 20-27.
    • Handle: RePEc:eee:appene:v:121:y:2014:i:c:p:20-27
    DOI: 10.1016/j.apenergy.2014.01.066
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Ng, Kong Soon & Moo, Chin-Sien & Chen, Yi-Ping & Hsieh, Yao-Ching, 2009. "Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries," Applied Energy, Elsevier, vol. 86(9), pages 1506-1511, September.
    4. Xing, Yinjiao & He, Wei & Pecht, Michael & Tsui, Kwok Leung, 2014. "State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures," Applied Energy, Elsevier, vol. 113(C), pages 106-115.
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