基于PSO-RBF混合算法锂离子电池SOC估算

被引:14
作者
吴铁洲
吴笑民
杨蒙蒙
熊金龙
机构
[1] 湖北工业大学太阳能高效利用湖北省协同创新中心
关键词
锂离子电池; 粒子群算法; 径向基神经网络; 荷电状态; 权值优化;
D O I
暂无
中图分类号
TM912 [蓄电池];
学科分类号
080802 [电力系统及其自动化];
摘要
为提高锂离子电池荷电状态的预测精度,将粒子群算法引入到径向基神经网络中,建立锂离子电池荷电状态混合估算算法。采用粒子群算法对径向基神经网络隐层节点中心和宽度及连接权值进行优化,降低径向基神经网络参数取值的繁杂度,提高荷电状态预测精度。利用Arbin BT2000多功能蓄电池测试平台,获取到锂离子电池放电数据,进行模拟训练和预测。实验表明:混合算法相对RBF网络具有更好的预测能力,满足荷电状态估算精度误差小于5%的要求,验证了该模型是有效、可行的。
引用
收藏
页码:982 / 985
页数:4
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