Lithium-Ion Battery Estimation in Online Framework Using Extreme Gradient Boosting Machine Learning Approach

Sadiqa Jafari, Zeinab Shahbazi, Yung-Cheol Byun and Sang-Joon Lee
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Sadiqa Jafari: Department of Computer Engineering, Institute of Information Science & Technology, Jeju National University, Jeju 63243, Korea
Zeinab Shahbazi: Department of Computer Engineering, Institute of Information Science & Technology, Jeju National University, Jeju 63243, Korea
Yung-Cheol Byun: Department of Computer Engineering, Institute of Information Science & Technology, Jeju National University, Jeju 63243, Korea
Sang-Joon Lee: Department of Computer Engineering, Institute of Information Science & Technology, Jeju National University, Jeju 63243, Korea

Mathematics, 2022, vol. 10, issue 6, 1-17

Abstract: The battery management system in an electric vehicle must be reliable and durable to forecast the state of charge. Considering that battery degradation is generally nonlinear, state of charge (SOC) estimation with lower degradation can be challenging. Lithium-ion batteries are highly dependent on the knowledge of aging, which is usually costly or not available online. In this paper, we suggest the state of charge estimation of lithium-ion battery systems by using an extreme gradient boosting algorithm for electric vehicles application, which acquires the nonlinear relationship model can with offline training. The extreme gradient boosting algorithm is the tree on based learning, which effectively performs and speeds. Voltage-time data used as an input of this system from the partial constant current phase; the proposed algorithm improves the accuracy of predicting the relevant. Additionally, no initial state of charge is required in our proposed method; thus, estimating the state of charge can consider each battery state.

Keywords: lithium-ion battery; capacity; state of charge; extreme gradient boosting (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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