A New Method for State of Charge Estimation of Lithium-Ion Battery Based on Strong Tracking Cubature Kalman Filter

Energies

Bizhong Xia²  Haiqing Wang²  Mingwang Wang¹  Wei Sun¹  Zhihui Xu¹  Yongzhi Lai¹ 

[1] Sunwoda Electronic Co. Ltd., Shenzhen 518108, China;Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China;

关键词: strong tracking cubature Kalman filter;    state of charge;    lithium-ion battery;    electric vehicle;   

DOI  :  10.3390/en81212378

来源: mdpi

【 摘 要 】

The estimation of state of charge (SOC) is a crucial evaluation index in a battery management system (BMS). The value of SOC indicates the remaining capacity of a battery, which provides a good guarantee of safety and reliability of battery operation. It is difficult to get an accurate value of the SOC, being one of the inner states. In this paper, a strong tracking cubature Kalman filter (STCKF) based on the cubature Kalman filter is presented to perform accurate and reliable SOC estimation. The STCKF algorithm can adjust gain matrix online by introducing fading factor to the state estimation covariance matrix. The typical second-order resistor-capacitor model is used as the battery’s equivalent circuit model to dynamically simulate characteristics of the battery. The exponential-function fitting method accomplishes the task of relevant parameters identification. Then, the developed STCKF algorithm has been introduced in detail and verified under different operation current profiles such as Dynamic Stress Test (DST) and New European Driving Cycle (NEDC). Making a comparison with extended Kalman filter (EKF) and CKF algorithm, the experimental results show the merits of the STCKF algorithm in SOC estimation accuracy and robustness.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.