An Improved Test Method of LiFePO4/Graphene Hybrid Cathode Lithium-Ion Battery and the State of Charge Estimation

Author:

Li Meiying1,Guo Zhiping1,Li Yuan23,Wu Wenliang1

Affiliation:

1. College of Mechanical Engineering, Inner Mongolia University of Technology, Hohhot 010050, Inner Mongolia, China

2. College of Energy and Power Engineering, Inner Mongolia University of Technology, Hohhot 010050, Inner Mongolia, China;

3. School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm 10044, Sweden

Abstract

Abstract The state of charge (SoC) of the battery is a typical characterization of the operating state of the battery and criterion for the battery management system (BMS) control strategy, which must be evaluated precisely. The establishment of an accurate algorithm of SoC estimation is of great significance for BMS, which can help the driver judge the endurance mileage of electric vehicle (EV) correctly. In this paper, a second-order resistor-capacity (RC) equivalent circuit model is selected to characterize the electrical characteristics based on the electrochemical model of the LiFePO4/graphene (LFP/G) hybrid cathode lithium-ion battery. Moreover, seven open circuit voltage (OCV) models are compared and the best one of them is used to simulate the dynamic characteristics of the battery. It is worth mentioning that an improved test method is proposed, which is combined with least square for parameters identification. In addition, the extended Kalman filter (EKF) algorithm is selected to estimate the SoC during the charging and discharging processes. The simulation results show that the EKF algorithm has the higher accuracy and rapidity than the KF algorithm.

Funder

National Science Foundation

Publisher

ASME International

Subject

Mechanical Engineering,Mechanics of Materials,Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electronic, Optical and Magnetic Materials

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. SOC Prediction for Lithium Battery Via LSTM-Attention-R Algorithm;Frontiers in Computing and Intelligent Systems;2023-07-20

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