Abstract

Aimed at improving SOC estimation accuracy, speed and robust of battery on electric vehicle, SOC estimation method based on adaptive particle filter is proposed. 1-order RC and lag model, 2-order RC and lag model, 3-order RC and lag model are built. Particle Swarm algorithm is used to search optimal parameters. Considering calculation and model accuracy, 1-order lag model is chosen. Traditional particle filter principle is analyzed. State estimation is a substitute to observation equation, and observation estimation is gotten. Observation noise variance is adjusted adaptively through observation error. Verification by simulation, convergence speed and robust of adaptive particle filter are superior to traditional algorithm when SOC original error is large. Besides, SOC estimation accuracy and stability is superior to traditional algorithm obviously.

中文翻译：

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基于自适应粒子滤波的电动汽车SOC估计新方法

摘要

为了提高电动汽车电池SOC估计的准确性，速度和鲁棒性，提出了一种基于自适应粒子滤波的SOC估计方法。建立了1阶RC和滞后模型，2阶RC和滞后模型，3阶RC和滞后模型。粒子群算法用于搜索最优参数。考虑计算和模型精度，选择一阶滞后模型。分析了传统的粒子过滤器原理。状态估计可以代替观测方程，从而获得观测估计。观察噪声方差可通过观察误差进行自适应调整。通过仿真验证，SOC原始误差较大时，自适应粒子滤波器的收敛速度和鲁棒性均优于传统算法。除了，