Design of an Effective State of Charge Estimation Method for a Lithium-Ion Battery Pack Using Extended Kalman Filter and Artificial Neural Network

Dao Van Quan, Minh-Chau Dinh, Chang Soon Kim, Minwon Park, Chil-Hoon Doh, Jeong Hyo Bae, Myung-Kwan Lee, Jianyong Liu and Zhiguo Bai
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Dao Van Quan: Department of Electrical Engineering, Changwon National University, Changwon 51140, Korea
Minh-Chau Dinh: Institute of Mechatronics, Changwon National University, Changwon 51140, Korea
Chang Soon Kim: Institute of Mechatronics, Changwon National University, Changwon 51140, Korea
Minwon Park: Department of Electrical Engineering, Changwon National University, Changwon 51140, Korea
Chil-Hoon Doh: Distributed Power System Research Center, Korea Electrotechnology Research Institute, Changwon 51543, Korea
Jeong Hyo Bae: Distributed Power System Research Center, Korea Electrotechnology Research Institute, Changwon 51543, Korea
Myung-Kwan Lee: Battery Solution Co., Ltd., Jeonnam 58324, Korea
Jianyong Liu: IES Co., Ltd., Busan 46744, Korea
Zhiguo Bai: IES Co., Ltd., Busan 46744, Korea

Energies, 2021, vol. 14, issue 9, 1-20

Abstract: Currently, Lithium-ion batteries (LiB) are widely applied in energy storage devices in smart grids and electric vehicles. The state of charge (SOC) is an indication of the available battery capacity, and is one of the most important factors that should be monitored to optimize LiB’s performance and improve its lifetime. However, because the SOC relies on many nonlinear factors, it is difficult to estimate accurately. This paper presented the design of an effective SOC estimation method for a LiB pack Battery Management System (BMS) based on Kalman Filter (K F ) and Artificial Neural Network (ANN). First, considering the configuration and specifications of the BMS and LiB pack, an ANN was constructed for the SOC estimation, and then the ANN was trained and tested using the Google TensorFlow open-source library. An SOC estimation model based on the extended KF (EKF) and a Thevenin battery model was developed. Then, we proposed a combined mode EKF-ANN that integrates the estimation of the EKF into the ANN. Both methods were evaluated through experiments conducted on a real LiB pack. As a result, the ANN and KF methods showed maximum errors of 2.6% and 2.8%, but the EKF-ANN method showed better performance with less than 1% error.

Keywords: Artificial neural network; battery management system; Kalman filter; lithium-ion battery; state of charge estimation (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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