基于灰色多变量模型锂离子电池荷电状态预测
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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.
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.
引文
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[2] C E Burns. Battery management system and method[M]. US,6983212. 2006-01-03.
[3]路保虎.基于STM32F103ZET6的动力电池管理系统设计[D].南京理工大学,2013.
[4]黄继.灰色多变量GM(1,N|T,r)模型及其粒子群优化算法[J].系统工程理论与实践,2009,(10):145-151.
[5]时小春,范献胜.非等间隔GM(1,N|t,r)模型的性质研究[J].武汉理工大学学报,2013,35(6):825-827.
[6]魏学哲,孙泽昌,田佳卿.锂离子动力电池参数辨识与状态估计[J].同济大学学报(自然科学版),2008,36(2):231-235.
[7]林成涛,王军平,陈全世.电动汽车SOC估计方法原理与应用[J].电池,2004,34(5):376-378.
[8]何新天.电源管理系统设计及参数估计策略研究[D].中国科学技术大学,2011.
[9]王丰效.非等间距组合灰色预测模型[J].数学的实践与认识,2007,(21):39-43.
[10]尹安东,等.基于神经网络的磷酸铁锂电池SOC预测研究[J].电子测量与仪器学报,2011,(5):433-437.
[11]张利彪.基于粒子群优化算法的研究[D].吉林大学,2004.
[12]汪定伟,等.智能优化算法[M].北京:高等教育出版社,2007.
[13]钱吴永,党耀国,王叶梅.加权累加生成的GM(1,1)模型及其应用[J].数学的实践与认识,2009,(15):47-51.