回溯搜索算法改进RBF算法的锂离子电池SOC估算研究

随着电动汽车的发展和应用,动力电池SOC估算的意义越来越重要,为了提高SOC估算的精度,本文在标准RBF网络模型的基础上提出了利用回溯搜索算法改进RBF神经网络模型。通过对锂电池模型中的目标函数进行优化求解,利用寻找最佳的目标权值和阈值提高RBF网络模型的SOC估算精度。最后搭建了实验仿真平台,对改进前后的算法SOC估算进行了仿真对比分析,实验结果证明了改进后RBF网络比标准RBF网络算法SOC估算精度更高,并把估算误差降低到2%以内,对锂离子电池有较好的估算精度,具有一定的理论研究意义。

Research on Lithium-ion Battery SOC Estimation Based on Retrospective Search Algorithm and Improved RBF Algorithm

With the development and application of electric vehicles, the significance of power battery SOC estimation is becoming more and more important. In order to improve the accuracy of SOC estimation, this paper proposes to use the backtracking search algorithm to improve the RBF neural network model based on the standard RBF network model. By optimizing and solving the objective function in the lithium battery model, the SOC estimation accuracy of the RBF network model is improved by finding the best objective weights and thresholds. Finally, an experimental simulation platform was built to compare and analyze the algorithm SOC estimation before and after the improvement. The experimental results prove that the improved RBF network has higher SOC estimation accuracy than the standard RBF network algorithm, and reduces the estimation error to less than 2%. Lithium-ion batteries have good estimation accuracy and have certain theoretical research significance.