基于改进灰狼算法优化核极限学习机的锂电池动力电池荷电状态估计

State of Charge Estimation for Lithium Battery Based on Kernel Extreme Learning Machine Optimized by Improved Grey Wolf Algorithm

动力电池荷电状态(SOC)是电动汽车电池管理系统(BMS)的一个重要技术指标,针对锂电池SOC难以精确估算的问题,提出一种基于改进灰狼算法(IGWO)优化核极限学习机(KELM)的SOC估计方法.为了克服标准GWO算法存在早熟收敛、易陷入局部最优等缺陷,算法首先采用混沌映射和反向学习策略产生初始灰狼种群,其次引入收敛因子非线性调整机制来提升算法的整体收敛速度,最后利用高斯变异及贪婪选择算子更新最优解位置,以降低算法陷入局部极值的概率.通过5个标准测试函数的仿真实验证明了该算法具有更好的寻优能力.利用IGWO算法对核极限学习机的参数进行寻优,并建立起基于KELM的电池SOC估计模型,分别采用美国城市动态循环驱动工况(UDDS)模拟工况下数据和恒流放电实验数据进行仿真研究,结果表明本文所提的方法估算效果优于ELM、KELM、GWO-KELM、扩展卡尔曼滤波器(EKF)和无迹卡尔曼滤波器(UKF),对锂电池系统优化管理具有指导意义.

The state of charge(SOC)of lithium batteries is an important performance indicator in an electric vehicle’s battery management system(BMS).However,online estimation of SOC is difficult due to technological limitations.We propose a SOC estimation method for lithium battery based on kernel extreme learning machine(KELM)optimized by improved grey wolf algorithm(IGWO).Aiming to reduce the search time of the grey wolf optimization algorithm(GWO),we utilize the chaotic tent mapping and opposition-based learning to initialize individual position and then integrate the nonlinear decline of convergence factor to improve the search ability.Finally,we employ the Gaussian mutation operator and greedy strategy to update the global optimum of each generation.The IGWO is applied to optimize the parameters of the KELM model,which is then employed to estimate the SOC of the lithium battery.Several experiments are conducted based on the measuring data from the ADVISOR operation mode and the constant discharge test for the lithium battery,where the results show that the estimation effect of the proposed model is better than that of ELM,KELM,GWO-KELM,EKF,and unscented Kalman filter.Hence,the proposed method has a good guiding significance for a BMS.