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Open AccessArticle

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

1
Department of Electrical Engineering, Changwon National University, Changwon 51140, Korea
2
Institute of Mechatronics, Changwon National University, Changwon 51140, Korea
3
Distributed Power System Research Center, Korea Electrotechnology Research Institute, Changwon 51543, Korea
4
Battery Solution Co., Ltd., Jeonnam 58324, Korea
5
IES Co., Ltd., Busan 46744, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Andrei Blinov
Energies 2021, 14(9), 2634; https://doi.org/10.3390/en14092634
Received: 6 April 2021 / Revised: 27 April 2021 / Accepted: 27 April 2021 / Published: 4 May 2021
(This article belongs to the Special Issue Power Electronics and Energy Management for Battery Storage Systems)
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 (KF) 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. View Full-Text
Keywords: Artificial neural network; battery management system; Kalman filter; lithium-ion battery; state of charge estimation Artificial neural network; battery management system; Kalman filter; lithium-ion battery; state of charge estimation
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MDPI and ACS Style

Dao, V.Q.; Dinh, M.-C.; Kim, C.S.; Park, M.; Doh, C.-H.; Bae, J.H.; Lee, M.-K.; Liu, J.; Bai, Z. Design of an Effective State of Charge Estimation Method for a Lithium-Ion Battery Pack Using Extended Kalman Filter and Artificial Neural Network. Energies 2021, 14, 2634. https://doi.org/10.3390/en14092634

AMA Style

Dao VQ, Dinh M-C, Kim CS, Park M, Doh C-H, Bae JH, Lee M-K, Liu J, Bai Z. Design of an Effective State of Charge Estimation Method for a Lithium-Ion Battery Pack Using Extended Kalman Filter and Artificial Neural Network. Energies. 2021; 14(9):2634. https://doi.org/10.3390/en14092634

Chicago/Turabian Style

Dao, Van Q.; Dinh, Minh-Chau; Kim, Chang S.; Park, Minwon; Doh, Chil-Hoon; Bae, Jeong H.; Lee, Myung-Kwan; Liu, Jianyong; Bai, Zhiguo. 2021. "Design of an Effective State of Charge Estimation Method for a Lithium-Ion Battery Pack Using Extended Kalman Filter and Artificial Neural Network" Energies 14, no. 9: 2634. https://doi.org/10.3390/en14092634

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