基于改进果蝇算法优化的最小二乘支持向量机动力锂电池荷电状态预测

State of charge of power lithium battery predicted by least square support vector machines based on improved fruit fly algorithm(IFOA)

荷电状态(SOC)是动力锂电池管理系统的重要参数,使用传统算法优化锂电池SOC预测模型参数,收敛性相对较差,容易陷入局部最优解。对此,采用改进果蝇算法(IFOA)对最小二乘支持向量机(LSSVM)的参数进行优化,通过引入自适应松弛项来提高预测精度和收敛速度,获取全局最优解。选用磷酸锂电池为研究对象,测量其工作电压、工作电流和SOC,并将数据作为测试集,在MATLAB平台上建立基于IFOA优化的最小二乘支持向量机SOC预测模型。结果表明:IFOA优化的LSSVM动力锂电池SOC预测结果和实测结果吻合良好,平均绝对误差(MAPE)为1.02%,泛化能力强,预测精度相较果蝇算法最小二乘支持向量机(FOA LSSVM)和贝叶斯算法最小二乘支持向量机(BEF LSSVM)模型的精度更高。

State of charge(SOC)is an important parameter of power lithium battery management system,and the convergence is relatively poor,and it is easy to fall into the local optimal solution using traditional algorithms to optimize the parameters of the SOC prediction model for lithium battery.In this regard,the improved fruit fly algorithm(IFOA)was used to optimize the parameters of the least squares support vector machines(LSSVM),and the adaptive relaxation term was introduced to improve the prediction accuracy and convergence speed,so as to obtain the global optimal solution.The LSSVM optimized by improved fruit fly algorithm(IFOA)was used to predict the SOC of lithium-phosphate battery,and its operating voltage,operating current and SOC were measured,and the data was used as the test set to establish the SOC prediction model of LSSVM based on the optimization of IFOA on the platform of MATLAB.Results showed that the SOC prediction of lithium-phosphorus battery based on IFOA optimized LSSVM was in very good agreement with the measured results.The simulation results showed that the LSSVM optimized by IFOA had a good agreement with the measured results,the mean absolute error(MAPE)was 1.02%,the generalization ability was strong,and the prediction accuracy was higher than that of the fruit fly algorithm(FOA)-LSSVM and bayesian evidence framework(BEF)-LSSVM models.