基于灰色多变量模型锂离子电池荷电状态预测  

Lithium-Ion Battery SOC Prediction Based on Gray Multivariate Model

作　　者：于仲安 赵凯贤 YU Zhong-an;ZHAO Kai-xian(College of Electrical and Automation,Jiangxi University of Science and Technology,Ganzhou Jiangxi 341000,China)

机构地区：[1]江西理工大学电气工程与自动化学院,江西赣州341000

出　　处：《计算机仿真》2019年第1期138-140,共3页Computer Simulation

基　　金：国家自然科学基金(51177066);江西省教育厅立项课题(GJJ150678)

摘　　要：传统的荷电状态预测方法不准确,为了进一步提高荷电状态预测的准确性,针对电动汽车电池荷电状态的预测,提出了基于灰色多变量模型对锂离子电池的SOC预测进行研究的方法。利用MATLAB中Simulink仿真软件建立实验的仿真模型,并用粒子群优化算法对预测模型中的参数进行了辨识。通过分析电池的放电倍率和电压对荷电状态的影响,研究结果显示,所提出的SOC预测方法具有较高的准确性,其可以用来进行SOC的预测。仿真模型具有良好的预测性提高了SOC预测的精度并在SOC预测领域内具有实用价值。In order to improve the accuracy of the state of charge( SOC) prediction,a SOC prediction method of lithium-ion battery based on gray multivariate model is proposed for electric vehicle. Simulink simulation software was used to establish the simulation model in MATLAB,and the parameters in the prediction model were identified by particle swarm optimization algorithm. By analyzing the effect of the discharge rate and voltage on the charge state of the battery,the results show that the proposed SOC prediction method has high accuracy. The simulation model has good predictability to improve the accuracy of SOC prediction and has practical value in the field of SOC prediction.

关 键 词：锂离子电池 荷电状态 灰色预测 粒子群优化 

分 类 号：TM912[电气工程—电力电子与电力传动]