基于IALO算法的蓄电池参数辨识

合理的等效电路模型及准确的模型参数对蓄电池荷电状态(SOC)的准确估计具有重要影响.针对蓄电池三阶Thevenin等效电路模型,基于改进蚁狮优化算法,提出了一种模型参数辨识方法.引入混沌Logistic映射初始化,使初始化群体遍及解空间,有利于寻找全局最优解;引入自适应惯性权重加随机柯西变异策略,有效提高了算法收敛速度...

Parameter Identification of Battery Based on IALO Algorithm

A reasonable equivalent circuit model and accurate model parameters have an important impact on the accurate estimation of the battery SOC. Aiming at the third-order Thevenin equivalent circuit model of battery, a parameter identification method based on ant lion optimization algorithm was proposed. The introduction of chaotic logistic map initialization could make the initialization population spread over the solution space, which was beneficial to find the global optimal solution. The introduction of adaptive inertia weight and random Cauchy mutation strategy could effectively improve the convergence speed of the algorithm. Elite reverse learning strategy was introduced to effectively improve the diversity of population and avoid the algorithm trapping into local optimal solution. The test results of five test functions showed that compared with ant lion optimization algorithm, particle swarm optimization algorithm and salp optimization algorithm, the improved ant lion optimization algorithm had the faster convergence speed and higher accuracy. The parameter identification of third-order Thevenin equivalent circuit model of battery showed that the improved ant lion optimization algorithm had the higher identification accuracy than ant lion optimization algorithm.