An Online State of Charge Estimation Algorithm for Lithium-Ion Batteries Using an Improved Adaptive Cubature Kalman Filter

Zhibing Zeng, Jindong Tian, Dong Li and Yong Tian
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Zhibing Zeng: College of Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China
Jindong Tian: College of Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China
Dong Li: College of Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China
Yong Tian: College of Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China

Energies, 2018, vol. 11, issue 1, 1-16

Abstract: An accurate state of charge (SOC) estimation of the on-board lithium-ion battery is of paramount importance for the efficient and reliable operation of electric vehicles (EVs). Aiming to improve the accuracy and reliability of battery SOC estimation, an improved adaptive Cubature Kalman filter (ACKF) is proposed in this paper. The battery model parameters are online identified with the forgetting factor recursive least squares (FRLS) algorithm so that the accuracy of SOC estimation can be further improved. The proposed method is evaluated by two driving cycles, i.e., the New European Driving Cycle (NEDC) and the Federal Urban Driving Schedule (FUDS), and compared with the existing unscented Kalman filter (UKF) and standard CKF algorithms to verify its superiority. The experimental results reveal that comparing with the UKF and standard CKF, the improved ACKF algorithm has a faster convergence rate to different initial SOC errors with higher estimation accuracy. The root mean square error of SOC estimation without initial SOC error is less than 0.5% under both the NEDC and FUDS cycles.

Keywords: state of charge; adaptive cubature Kalman filter; lithium-ion battery; battery model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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