利用改进SCE算法的锂离子电池参数辨识

Parameter identification of lithium ion battery using improved SCE algorithm

针对传统参数辨识方法中存在的易陷入局部最优和精度低问题,提出一种改进洗牌复杂演化算法(shuffed complex evolution, SCE).首先,提出描述电池的动态特性的二阶RC等效电路模型,并根据恒流放电工况测试数据集进行锂离子电池等效模型确定待辨识参数.其次,将模型模拟端电压值与电池真实测试端电压均方根误差作为目标函数,并通过所提出的优化算法来寻找模型最优参数.最后,使用DST、 FUDS的锂离子电池动态工况数据集进行仿真验证,并与粒子群算法、灰狼算法、遗传算法进行比较.仿真结果表明,本方法在辨识精度方面具有优势,算法的参数辨识均方根误差(ERMS)平均值是0.016 6 V,相比较其他优化算法,分别降低了7.8%、 8.3%、 14.9%.

Aiming at the problems of local optimization and low precision in traditional parameter identification methods,shuffed complex evolution(SCE)is proposed to improve the competitive evolution algorithm of conventional shuffling complex evolution algorithms.Firstly,a second-order RC equivalent circuit model was proposed to describe the dynamic characteristics of the battery,and the parameters to be identified were determined by using the equivalent model of the lithium-ion battery based on the constant current discharge condition test data set.Secondly,the RMS error between the simulated terminal voltage and the real battery test terminal voltage is taken as the objective function,and the optimal parameters of the model are found through the proposed optimization algorithm.Finally,the dynamic working condition data sets of DST and FUDS were used for simulation verification,and compared with particle swarm optimization algorithm,gray wolf algorithm and genetic algorithm.The simulation results show that this method has advantages in the identification accuracy.The average E RMS error of the algorithm is 0.0166 V,which is reduced by 7.8%,8.3%and 14.9%,respectively,compared with other optimization algorithms.