A Comparative Study of Charging Voltage Curve Analysis and State of Health Estimation of Lithium-ion Batteries in Electric Vehicle
作者机构：School of Vehicle and MobilityState Key Laboratory of Automotive Safety and EnergyTsinghua UniversityBeijing 100084China College of Mechanical EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China
出 版 物：《Automotive Innovation》 (汽车创新工程（英文）)
年 卷 期：2019年第2卷第4期
页 面：263-275页
核心收录：
基 金：This work is supported by the Beijing Natural Science Foundation under the Grant No.3184052 the National Natural Science Foundation of China(NSFC)under the Grant No.51807108 and No.U1564205 International Science and Technology Cooperation Program of China under contract No.2016YFE0102200
主 题：Lithium-ion battery Capacity fade Charging voltage curve Neural networks Electric vehicle
摘 要：Lithium-ion(Li-ion)cells degrade after repeated cycling and the cell capacity fades while its resistance ***-dation 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 *** 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)*** 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 *** 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 *** method is suitable for different types of batteries and could be used in battery management systems for online battery modeling,analysis and diagnosis.
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