摘要
根据18650型锂离子单体电池的特性分析,建立了电路等效模型和电化学模型相结合的电池模型,以实时在线辨识锂离子电池欧姆内阻为目标,利用无迹Kalman(UKF)滤波算法,实现了对电池欧姆内阻的在线辨识,开展了锂离子电池健康状况(SOH)估计实验,建立了适用于18650型锂离子电池的SOH估计模型。仿真结果显示,该模型同时考虑电池内阻在不同工况下的变化趋势和充放电电流大小等因素,为实现锂离子电池健康状况精确估计提供了较好的理论基础。
The characteristics of the 18650-type lithium battery were analyzed. An improved battery model combined with the equivalent circuit model and the electrochemical model was established. The main efforts of our study were：Firstly, the Ohmic resistance of the battery model was identified online based on the Unscented Kalman Filtering（UKF） algorithm. Secondly, the estimation model of the State of Health（SOH） for the 18650-type battery was established. Thirdly, an improved battery SOH estimation method based on UKF algorithm was provided. The experimental results indicate that the new battery model considers the different-value of the battery internal resistance on the different working condition（like different voltage, current and temperature）. Besides, our new estimation algorithm had practical value to the further study of filtering algorithm for other electrical vehicle systems.
出处
《电源技术》
CAS
CSCD
北大核心
2016年第3期543-546,共4页
Chinese Journal of Power Sources
基金
浙江省公益项目(2015C33225)
浙江省自然科学基金(LQ16F010004)
