State of Charge Estimation of Lithium-Ion Batteries Using an Adaptive Cubature Kalman Filter

Energies

Bizhong Xia²  Haiqing Wang²  Yong Tian²  Mingwang Wang¹  Wei Sun¹  Zhihui Xu¹ 

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

关键词: Adaptive Cubature Kalman filter;    state of charge;    lithium-ion battery;    electric vehicle;   

DOI  :  10.3390/en8065916

来源: mdpi

【 摘 要 】

Accurate state of charge (SOC) estimation is of great significance for a lithium-ion battery to ensure its safe operation and to prevent it from over-charging or over-discharging. However, it is difficult to get an accurate value of SOC since it is an inner sate of a battery cell, which cannot be directly measured. This paper presents an Adaptive Cubature Kalman filter (ACKF)-based SOC estimation algorithm for lithium-ion batteries in electric vehicles. Firstly, the lithium-ion battery is modeled using the second-order resistor-capacitor (RC) equivalent circuit and parameters of the battery model are determined by the forgetting factor least-squares method. Then, the Adaptive Cubature Kalman filter for battery SOC estimation is introduced and the estimated process is presented. Finally, two typical driving cycles, including the Dynamic Stress Test (DST) and New European Driving Cycle (NEDC) are applied to evaluate the performance of the proposed method by comparing with the traditional extended Kalman filter (EKF) and cubature Kalman filter (CKF) algorithms. Experimental results show that the ACKF algorithm has better performance in terms of SOC estimation accuracy, convergence to different initial SOC errors and robustness against voltage measurement noise as compared with the traditional EKF and CKF algorithms.

【 授权许可】

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