摘要
估计电池的荷电状态(SOC)和健康状态(SOH)是锂离子电池管理中最复杂的任务之一。目前,针对SOC和SOH的估计存在跟踪值误差较大、噪声误差较大和计算量大等问题,引入多元宇宙优化(MVO)算法,对照电池的实际输出电压,模型的拟合度可达95.3%。通过14次迭代得到SOC的稳定估计值,与传统的循环次数法对比,SOH估计的稳定性提高了119%,并减小了78%的计算空间需求。
Estimating the state of charge(SOC)and state of health(SOH)of the battery is one of the most complex tasks in Li-ion battery management.Currently,for the estimation of SOC and SOH,there are issues such as large tracking value error,large noise error and large computation,multi-verse optimization(MVO)algorithm are introduced,the fit of the model is up to 95.3%against the actual output voltage of the battery.The stable estimation of SOC is obtained through 14 iterations,which improves the stability of SOH estimation by 119%and reduces the computational space requirement by 78%compared with the traditional cycle number method.
作者
朱冰
夏天
ZHU Bing;XIA Tian(College of Communication and Information Engineering,Shanghai Technical Institute of Electronics&Information,Shanghai 201411,China;School of Computer and Information Engineering,Shanghai Polytechnic University,Shanghai 201209,China)
出处
《电池》
CAS
北大核心
2024年第5期688-692,共5页
Battery Bimonthly
基金
教育部教育管理中心“十三五”重点课题(JYB-ZJ1716)。
