Lithium-ion battery, Capacity fade, Charging voltage curve, Neural networks, Electric vehicle
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Automotive Innovation ›› 2019, Vol. 2 ›› Issue (4): 263-275.doi: 10.1007/s42154-019-00080-2

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A Comparative Study of Charging Voltage Curve Analysis and State of Health Estimation of Lithium-ion Batteries in Electric Vehicle

Xuebing Han1, Xuning Feng1, Minggao Ouyang1, Languang Lu1, Jianqiu Li1, Yuejiu Zheng2, Zhe Li1   

    1. School of Vehicle and Mobility, State Key Laboratory of Automotive Safety and Energy,Tsinghua University, Beijing, China
    2. College of Mechanical EngineeringUniversity of Shanghai for Science and Technology, Shanghai, China
  • 出版日期:2019-12-17 发布日期:2019-12-17

A Comparative Study of Charging Voltage Curve Analysis and State of Health Estimation of Lithium-ion Batteries in Electric Vehicle

Xuebing Han1, Xuning Feng1, Minggao Ouyang1, Languang Lu1, Jianqiu Li1, Yuejiu Zheng2, Zhe Li1   

    1. School of Vehicle and Mobility, State Key Laboratory of Automotive Safety and Energy,Tsinghua University, Beijing, China
    2. College of Mechanical EngineeringUniversity of Shanghai for Science and Technology, Shanghai, China
  • Online:2019-12-17 Published:2019-12-17

摘要: Lithium-ion (Li-ion) cells degrade after repeated cycling and the cell capacity fades while its resistance increases. Degradation of Li-ion cells is caused by a variety of physical and chemical mechanisms and it is strongly influenced by factors including the electrode materials used, the working conditions and the battery temperature. At present, charging voltage curve analysis methods are widely used in studies of battery characteristics and the constant current charging voltage curves can be used to analyze battery aging mechanisms and estimate a battery’s state of health (SOH) via methods such as incremental capacity (IC) analysis. In this paper, a method to fit and analyze the charging voltage curve based on a neural network is proposed and is compared to the existing point counting method and the polynomial curve fitting method. The neuron parameters of the trained neural network model are used to analyze the battery capacity relative to the phase change reactions that occur inside the batteries. This method is suitable for different types of batteries and could be used in battery management systems for online battery modeling, analysis and diagnosis.

关键词: Lithium-ion battery, Capacity fade, Charging voltage curve, Neural networks, Electric vehicle">Lithium-ion battery, Capacity fade, Charging voltage curve, Neural networks, Electric vehicle
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Abstract: Lithium-ion (Li-ion) cells degrade after repeated cycling and the cell capacity fades while its resistance increases. Degradation of Li-ion cells is caused by a variety of physical and chemical mechanisms and it is strongly influenced by factors including the electrode materials used, the working conditions and the battery temperature. At present, charging voltage curve analysis methods are widely used in studies of battery characteristics and the constant current charging voltage curves can be used to analyze battery aging mechanisms and estimate a battery’s state of health (SOH) via methods such as incremental capacity (IC) analysis. In this paper, a method to fit and analyze the charging voltage curve based on a neural network is proposed and is compared to the existing point counting method and the polynomial curve fitting method. The neuron parameters of the trained neural network model are used to analyze the battery capacity relative to the phase change reactions that occur inside the batteries. This method is suitable for different types of batteries and could be used in battery management systems for online battery modeling, analysis and diagnosis.

Key words: Lithium-ion battery, Capacity fade, Charging voltage curve, Neural networks, Electric vehicle">Lithium-ion battery, Capacity fade, Charging voltage curve, Neural networks, Electric vehicle')">">