摘要
锂电池的荷电状态(SOC,State-of-Charge)直接反映电池的剩余电量,是电池组管理的核心参数。然而,电池循环次数的增加、瞬间大电流、温度等因素将导致电池特性发生变化,因此使用扩展卡尔曼滤波(EKF,extended kalman filtering)对锂电池的荷电状态进行估计会引入较大的误差甚至算法发散。为了有效抑制参数扰动和系统的非线性,基于一种改进的二阶RC等效电路模型,应用粒子滤波算法(PF,particle filtering)实现了锂电池荷电状态的估计。最后,根据锂电池放电实验所得数据进行仿真,结果显示了该算法的优越性。
As the key parameter for power battery management, the STATE-OF-Charge（SOC） of the lithium battery directly indicates the residual capacity. However, with the increase of cycles, instantaneous high current, abnormal temperatures and other factors would change cell characteristics, which might introduce larger error even divergence over time if the extended Kalman filtering（EKF） algorithm were applied to the SOC estimation. To suppress the parameters disturbance and system nonlinear, a method based on particle filtering（PF） algorithm was proposed to realize accurate SOC estimation on the improved RC equivalent circuit model of the lithium battery. The superiority of this method is validated by simulation results.
出处
《电源技术》
CAS
CSCD
北大核心
2017年第3期367-369,452,共4页
Chinese Journal of Power Sources
基金
陕西省自然科学基金项目(2013JM8026)
关键词
锂电池模型
荷电状态
粒子滤波
lithium battery model
state-of-charge
particle filtering
