摘要
为提高电动汽车电池SOC估计精度、收敛速度和鲁棒性,提出了一种改进的锂电池RC滞后模型及自适应粒子滤波的SOC估计方法。在传统RC模型基础上加入滞后模块,使用粒子群算法搜索的方法求解模型参数,综合考虑计算量和模型精度,确定了一阶RC滞后模型作为锂电池等效模型。在传统粒子滤波基础上,提出了观测噪声方差自适应估计方法。仿真结果表明,SOC初值误差较大时,自适应粒子滤波收敛速度和鲁棒性、SOC估计精度和稳定性明显优于传统算法。
To improve SOC estimation accuracy, rate of convergence and robustness of battery on electric vehicle, an improved Li-battery RC lagging model and adaptive particle filter SOC estimation method are proposed. Lagging model is introduced to traditional RC model, and Particle Swarm algorithm is used to search optimal model parameters. Considering calculation and model accuracy, first-order lagging model is chosen as the equivalent model of Li-battery. On the basis of traditional particle filter, observation noise variance adaptive adjustment method is proposed. Simulation results show that, rate of convergence and robustness of adaptive particle filter, SOC estimation accuracy and stability are superior to traditional algorithm when SOC original error is big.
作者
孙川
褚端峰
李海波
王建宇
Sun Chuan;Chu Duanfeng;Li Haibo;Wang Jianyu(Huanggang Normal University, Huanggang 438000;Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063;Dongfeng Motor Corporation Technical Center, Wuhan 430056)
出处
《汽车技术》
CSCD
北大核心
2019年第4期23-28,共6页
Automobile Technology
基金
国家自然科学基金项目(51675390)
大学生创新创业训练计划项目(20170514011)
湖北省自然科学基金计划项目(2018CFC863)
