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汽车安全与节能学报

汽车安全与节能学报 ›› 2020, Vol. 11 ›› Issue (3): 379-389.DOI: 10.3969/j.issn.1674-8484.2020.03.014

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基于无迹 Kalman 滤波算法的动力电池荷电状态估计

王  超，陈德海 * ，王昱朝，朱正坤，邹争明
  

  1. （江西理工大学 电气工程与自动化学院，赣州 341000，中国） 
  • 收稿日期:2020-04-22 出版日期:2020-09-30 发布日期:2020-10-20
  • 通讯作者: 陈德海，副教授。E-mail ：1442966206@qq.com。
  • 作者简介:第一作者 / First author ： 王超（1996—），男（汉），湖南，硕士研究生。E-mail: 1025359112@qq.com。
  • 基金资助:
    江西省自然科学基金资助项目 (20151BAB206034)。

Estimation of state of charge for power battery based on an unscented Kalman filter algorithm 

WANG Chao, CHEN Dehai* , WANG Yuzhao, ZHU Zhengkun, ZOU Zhengming   

  1. （Science and Technology School of Electrical Engineering and Automation, Jiangxi University, Gangzhou 341000, China）
  • Received:2020-04-22 Online:2020-09-30 Published:2020-10-20

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摘要： 提出了一种动力电池容量标定方法，结合无迹 Kalman 滤波（UKF）算法，对动力电池的荷电 状态（SOC）进行在线估计。根据温度系数和电流概率来标定电池的实际容量，建立二阶阻容（RC）等 效模型模拟电池动态响应特性，调用含遗忘因子的递推最小二乘法的无迹 Kalman 滤波（RLS-UKF） 算法对电池的 SOC 进行在线估计，在估计过程中考虑电流突变导致开路电压与SOC 曲线变化的影响。 通过 MATLAB 搭建仿真模型，用动态应力测试（DST）工况和恒流工况来验证算法的精度。结果表 明：RLS-UKF 算法在 DST 循环工况的平均误差为1.2%，在恒流放电工况的平均误差为1.41%。因此， 较目前主流的预测方法，本方法有更好的预测效果。

关键词: 动力电池, 荷电状态（SOC), 无迹 Kalman 滤波算法（UKF）, 递推最小二乘法（RLS）, 温度系数, 电流概率, 二阶 RC 等效模型

Abstract: A new battery capacity calibration method was proposed to estimate the state of charge (SOC) of power batteries online with combining an unscented Kalman filter (UKF) algorithm. The actual capacities of the batteries were calibrated according to temperature coefficient and current probability, a second-order RC equivalent model was established to simulate the dynamic response characteristics of the battery, and to call the unscented Kalman filter (RLS-UKF) algorithm of recursive least squares with forgetting factor to estimate battery SOC online. The estimation process considered the influence of the change of the OCV (open circuit voltage) -SOC curve caused by the sudden current change. The DST (dynamic stress test) working conditions and the constant current conditions were used to verify the algorithm’s accuracy through MATLAB simulation model. The results show that the RLSUKF algorithm has an average error of 1.2% at DST cycle conditions, and of 1.41% at constant current discharge conditions. Therefore, the method has a better prediction effect than the current mainstream prediction methods.

Key words: power battery, state of charge (SOC), unscented Kalman filter (UKF), recursive least squares (RLS); temperature coefficient, current probability, second-order RC equivalent model

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