车用锂离子电池SOC估算算法的研究

J4 ›› 2012, Vol. 34 ›› Issue (12): 169-173.

车用锂离子电池SOC估算算法的研究

鲍可进，金玲

(江苏大学计算机科学与通信工程学院，江苏镇江 212013)

收稿日期:2011-06-13 修回日期:2012-09-11 出版日期:2012-12-25 发布日期:2012-12-25

Study on SOC Estimation Algorithm of Lithiumion Battery of Electric Vehicle

BAO Kejin,JIN Ling

（School of Computer Science and Telecommunication Engineering,Jiangsu University,Zhenjiang 212013,China)

Received:2011-06-13 Revised:2012-09-11 Online:2012-12-25 Published:2012-12-25

摘要/Abstract

摘要：

针对纯电动汽车的锂离子电池容量损失而导致估算电池电荷状态（SOC）精度降低的问题，本文分析了影响电池容量损失的因素，提出容量修正算法。通过改进电池模型，把电池容量作为状态变量，将电池容量修正算法运用于Kalman滤波算法估计SOC，解决了锂离子电池容量损耗使得误差累积的问题。实验证明，本文提出的基于容量修正的Kalman最优滤波算法提高了SOC估算的精度，并且对初始误差有很强的修正作用，可以保证纯电动汽车锂离子电池的稳定工作。

关键词: 纯电动汽车, 电池容量损耗, SOC估计, Kalman滤波算法

Abstract:

For the problem of SOC estimation accuracy reducing that is caused by the lithiumion battery capacity losing in pure electric vehicle, the paper analyzes the influence factors of battery capacity losing and put forward a capacity correction algorithm. By improving battery model, the battery capacity is as state variables and the capacity correction algorithm is used in kalman filter to estimate the SOC. This solves the error accumulation problem that is caused by the lithiumion battery capacity losing. The test proves that the based on the capacity fixed kalman optimal filter algorithm is of benefit to improving the accuracy of SOC estimation and performs well when initial error happens. The method can ensure the stability of lithiumion of pure electric vehicle.

Key words: pure electric vehicle;battery capacity losing;SOC estimation;Kalman filter algorithm

鲍可进，金玲. 车用锂离子电池SOC估算算法的研究[J]. J4, 2012, 34(12): 169-173.

BAO Kejin,JIN Ling. Study on SOC Estimation Algorithm of Lithiumion Battery of Electric Vehicle[J]. J4, 2012, 34(12): 169-173.