摘要
锂离子电池荷电状态(State of Charge,SOC)直接影响着锂离子电池使用性能和效率。为了实现准确的SOC在线预测,提出一种粒子群优化最小二乘支持向量机软测量方法。该方法使用最小二乘支持向量机(Least Squares Support Vector Machine,LSSVM)建立非线性系统模型,以锂离子电池工作电压、电流为输入量,电池SOC为输出量。建立软测量模型时,LSSVM正则化参数λ和径向基核宽度μ直接影响着模型的准确度,采用粒子群算法(Particle Swarm Optimization,PSO)对这两个关键参数进行优化。用型号为BTS6050C4的NBT电池测试系统进行样本数据采集,通过MATLAB仿真软件进行模型训练并校正。实验和仿真结果表明采用PSO-LSSVM优化算法精确度高、易实现,且在正常和过充工作环境下均可有效预测锂离子电池SOC。
The prediction accuracy of lithium ion battery of(SOC)direcdy affects the performance and efficiency of lithium ion battery.In order to realize accurate SOC online prediction,this paper puts forward a kind of(LSSVM)soft meas_urement methods,work in lithium ion battery voltage and current as the input,the battery SOC prediction model is established for the output.Particle(PSO)is used to optimize LSSVM regularization parameters and radial basis width.The NBT battery test system of BTS6050C4 was used to collect the sample data,and the soft measurement model was trained and corrected by MATLAB simulation software.The experiment and simulation results show that the optimized algorithm of PSO-LSSYM is highly accurate and easy to achieve,and it can effectively predict the SOC battery in both normal and overcharge environments.
作者
黄永红
沈洋洋
陈坤华
周杰
李冬
Huang Yonghong;Shen Yangyang;Chen Kunhua;Zhou Jie;Li Dong(School of Electvial and Information Engineering,Jiangsu University,Zhenjiang 212013,Jiangsu,China;State Grid Suzhou,Suzhou 215004,Jiangsu,China)
出处
《电测与仪表》
北大核心
2018年第16期26-31,共6页
Electrical Measurement & Instrumentation
基金
镇江市科技计划项目(GY2010005)
关键词
荷电状态
最小二乘支持向量机
锂离子电池
软测量
粒子群
state of charge,least squares support vector machine,lithium ion battery,soft measurement,particle swarm optimization
作者简介
黄永红(1970—),女,汉族,江苏如东人,教授,博士,研究方向为电能质量分析与控制。Email:hyh@ujs.edu.cn;沈洋洋(1991一),男,汉族,硕士研究生,研究方向为电池管理系统研究;陈坤华(1981—),男,汉族,博士研究生,研究方向为混合动力汽车能量系统;周杰(1993—),男,汉族,硕士研究生,研究方向为电力系统电能质量控制.;李冬(1982—),男,汉族,髙级工程师,研究方向为微电网关键参数的检测与运行控制研究。
