摘要
锂离子电池组容量和内部参数随温度变化明显,在不同温度下准确估计电池电荷状态(state of charge,SOC)是电动汽车电池管理系统研究的关键技术。基于Thevenin模型,采用无损卡尔曼滤波(unscented Kalman filtering,UKF)实现不同温度和不同放电电流条件下对锂离子电池组SOC的估计。实验研究表明,UKF算法适应不同放电电流下的电池SOC估计。随着温度降低,虽然UKF方法对锂离子电池组SOC估计的收敛速度变慢,但对初始误差有较强的修正作用,且有较高的稳态精度。因此,UKF方法适合不同温度和放电电流下对锂离子电池组SOC的估计。
The capacity of lithium-ion battery and internal parameters obviously vary with temperature, so state of charge of cell exact estimation at various temperatures is the key technology of the battery management system in the electric vehicle. Based on the Thevenin model, using unscented kalman filter（UKF）, the state of charge（SOC）estimation of Li-ion battery at various temperatures and discharge currents was estimated. Experimental study showes that UKF algorithm was adapted to the SOC estimation of Li-ion battery at various discharge currents. With the temperature decreasing, though the UKF convergence rate of estimation of Li-ion battery SOC becomes slow,there is strong correct function to initial error, and steady state accuracy is high. Therefore, UKF algorithm is suitable for the estimation of Li-ion battery SOC at various temperatures and discharge currents.
出处
《电源技术》
CAS
CSCD
北大核心
2014年第5期828-831,共4页
Chinese Journal of Power Sources
基金
国家自然科学基金(61104183)
教育部新世纪优秀人才支持计划(NCET-10-0437)
关键词
锂离子电池组
温度
电荷状态
无损卡尔曼滤波
lithium-ion battery
temperature
state of charge
unscented Kalman filter
