摘要
对电池荷电状态(SoC)与电池健康状态(SoH)进行有效准确地估算可延长电池的循环寿命。为同时对SoC-SoH进行估计,对双扩展卡尔曼滤波算法进行多尺度优化,加入噪声修正实现进一步改进,提出改进多尺度双扩展卡尔曼滤波算法(Improved MDEKF,IMDEKF),对电池组模拟实际工况的测试算法进行仿真。结果表明,该算法可同时估计SoC与SoH,且噪声抑制性能良好,当错误设定初值时,该算法依然能够自我修正,计算后输出的电池状态值仍能收敛于真实值,整个估计过程SoC平均估计误差为2.1%,SoH平均估计误差为4%,达到较高的估算要求。
Effective and accurate estimation of battery state of charge(SoC)and battery health state(SoH)can improve battery cycle life.In order to realize the simultaneous calculation of the SoC and SoH of the battery,the DEKF algorithm was multi-scale optimized,and noise correction improvements were added.The actual performance of the battery pack was simulated to test the estimated performance of the algorithm.The simulation results show that the algorithm can calculate SoC and SoH simultaneously,and the noise suppression performance is good.When the initial value is set incorrectly,the algorithm can correct it,and the calculated battery state value can still converge to the true value.The average estimation error of SoC in the entire estimation process is 2.1%,and SoH average estimation error is 4%,which meets the system estimation requirements.
作者
海涛
范恒
陆代强
黄日光
陈永鉴
隆茂田
HAI Tao;FAN Heng;LU Daiqiang;HUANG Riguang;CHEN Yongjian;LONG Maotian(College of Electrical Engineering,Guangxi University,Nanning 530004,China;Nanning University,Nanning 530200,China)
出处
《实验室研究与探索》
CAS
北大核心
2021年第7期111-115,139,共6页
Research and Exploration In Laboratory
基金
国家自然科学基金项目(51867003)。
关键词
荷电状态
健康状态
多尺度
噪声
state of charge(SoC)
state of health(SoH)
multi-scale
noise
