State of Charge Estimation of Lithium Battery Based on Integrated Kalman Filter Framework and Machine Learning Algorithm

Hongyuan Yuan (), Jingan Liu, Yu Zhou and Hailong Pei ()
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Hongyuan Yuan: School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China
Jingan Liu: School of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330013, China
Yu Zhou: China Energy Construction Group Guangdong Electric Power Design and Research Institute Co., Ltd., Guangzhou 510700, China
Hailong Pei: School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China

Energies, 2023, vol. 16, issue 5, 1-16

Abstract: Research on batteries’ State of Charge (SOC) estimation for equivalent circuit models based on the Kalman Filter (KF) framework and machine learning algorithms remains relatively limited. Most studies are focused on a few machine learning algorithms and do not present comprehensive analysis and comparison. Furthermore, most of them focus on obtaining the state space parameters of the Kalman filter frame algorithm models using machine learning algorithms and then substituting the state space parameters into the Kalman filter frame algorithm to estimate the SOC. Such algorithms are highly coupled, and present high complexity and low practicability. This study aims to integrate machine learning with the Kalman filter frame algorithm, and to estimate the final SOC by using different combinations of the input, output, and intermediate variable values of five Kalman filter frame algorithms as the input of the machine learning algorithms of six main streams. These are: linear regression, support vector Regression, XGBoost, AdaBoost, random forest, and LSTM; the algorithm coupling is lower for two-way parameter adjustment and is not applied between the machine learning and Kalman filtering framework algorithms. The results demonstrate that the integrated learning algorithm significantly improves the estimation accuracy when compared to the pure Kalman filter framework or the machine learning algorithms. Among the various integrated algorithms, the random forest and Kalman filter framework presents the highest estimation accuracy along with good real-time performance. Therefore, it can be implemented in various engineering applications.

Keywords: Kalman Filter; random forest (RF); XGBoost; AdaBoost; support vector regression (SVR); long short-term memory (LSTM) (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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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