摘要
锂离子电池随着循环充放电次数的增长,其健康状态SOH(state-of-health)会随之发生一定程度的衰减。针对以上问题,设计了一种基于改进的多目标布谷鸟搜索IMOCS(improved multi-objective Cuckoo search)-BP神经网络的锂离子电池健康状态估计方法,在避免算法陷入局部最优的同时自适应改变布谷鸟搜索CS(Cuckoo search)算法更新概率和搜索步长,解决CS算法收敛速度慢和求解精度低的问题。以IMOCS算法和BP神经网络结合,对节点空间范围进行全局搜索,降低权值和阈值的初值对BP神经网络的影响,实现参数优化。通过Matlab仿真,验证了基于IMOCS-BP神经网络的SOH估计算法误差低、性能强,实现了锂电池SOH的精准预测。
As the number of charge and discharge cycles of a lithium-ion battery increases,its state-of-health(SOH)will degrade to some degree accordingly.Aimed at this problem,a method for estimating the SOH of lithium-ion battery based on an improved multi-objective Cuckoo search(IMOCS)-BP neural network is designed,which adaptively changes the update probability and search step size of the Cuckoo search(CS)algorithm while avoiding the algorithm from falling into the local optimum,thereby solving the problems of slow convergence speed and low solution accuracy in the CS algorithm.The IMOCS algorithm is combined with BP neural network to conduct a global search in the node space,reduce the influence of initial values of weight and threshold on BP neural network,and realize the parameter optimization.Through Matlab simulations,it is verified that the SOH estimation algorithm based on IMOCS-BP neural network has a low error and a strong performance,thus realizing an accurate SOH prediction of lithium-ion battery.
作者
王雪
游国栋
房成信
张尚
WANG Xue;YOU Guodong;FANG Chengxin;ZHANG Shang(College of Electronic Information and Automation,Tianjin University of Science and Technology,Tianjin 300222,China)
出处
《电源学报》
CSCD
北大核心
2024年第1期94-100,共7页
Journal of Power Supply
基金
天津市重点研发计划资助项目(17YFZCNC00230)
天津市应用基础与前沿技术研究计划(自然科学基金)重点资助项目(13JCZDJC29100)。
关键词
锂离子电池
健康状态
布谷鸟搜索算法
BP神经网络
Lithium-ion battery
state-of-health(SOH)
Cuckoo search(CS)algorithm
BP neural network
