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Energies 2018, 11(1), 59; https://doi.org/10.3390/en11010059

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

College of Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China
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Received: 17 November 2017 / Revised: 21 December 2017 / Accepted: 25 December 2017 / Published: 1 January 2018
(This article belongs to the Section Energy Storage and Application)
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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. View Full-Text
Keywords: state of charge; adaptive cubature Kalman filter; lithium-ion battery; battery model state of charge; adaptive cubature Kalman filter; lithium-ion battery; battery model
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Zeng, Z.; Tian, J.; Li, D.; Tian, Y. An Online State of Charge Estimation Algorithm for Lithium-Ion Batteries Using an Improved Adaptive Cubature Kalman Filter. Energies 2018, 11, 59.

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