SOC estimation optimization method based on parameter modified particle Kalman Filter algorithm

作     者：Zhang, Shouzhen Xie, Changjun Zeng, Chunnian Quan, Shuhai 

作者机构：Wuhan Univ Technol Sch Automot Engn Wuhan 430070 Hubei Peoples R China Wuhan Univ Technol Sch Automat Wuhan 430070 Hubei Peoples R China 

出 版 物：《CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS》 (簇计算)

年 卷 期：2019年第22卷第3-Sup期

页      面：S6009-S6018页

核心收录：

学科分类：07[理学] 0703[理学-化学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China Hubei Science Fund for Distinguished Young Scholars [2017CFA049] Wuhan youth morning project Fundamental Research Funds for the Central Universities [WUT: 2017II40GX] 

主　　题：SOC estimation Particle Filter algorithm Optimization modified parameters Recommended parameters 

摘      要：Traditional Kalman Filter algorithm requires the system noise to be Gaussian distribution, but the power battery operating condition generally can not meet the requirement due to complexity and disturbance by the environment. However, the Particle Filter algorithm can adapt to various forms of system noise. In this work, the calculation process of the standard Particle Filter algorithm is improved based on the engineering characteristics of SOC estimation. In the calculation process, the key parameters including the total number of particles and the effective particle threshold are optimized and verified under FTP75 and NEDC conditions. The systematic error under different conditions is evaluated, based on the vehicle platform computing capacity, the proposed total number of particles is 1000, the effective particle threshold is 0.01. In this case, the SOC estimation accuracy can reach 1-2%, meeting the practical requirements.