Lithium-Ion Battery SOH Estimation Based on XGBoost Algorithm with Accuracy Correction

Shuxiang Song, Chen Fei and Haiying Xia
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Shuxiang Song: Department of Electronic Engineering, Guangxi Normal University, Guilin 541004, China
Chen Fei: Department of Electronic Engineering, Guangxi Normal University, Guilin 541004, China
Haiying Xia: Department of Electronic Engineering, Guangxi Normal University, Guilin 541004, China

Energies, 2020, vol. 13, issue 4, 1-13

Abstract: SOH (state of health) estimation is important for battery management. Since the electrochemical reaction inside LIBS (lithium-ion battery system) is extremely complex and the external working environment is uncertain, it is difficult to achieve accurate determination of SOH. To improve the accuracy of SOH estimation, we propose a SOH estimation method for lithium-ion battery based on XGBoost algorithm with accuracy correction. We extract several features, including average voltage, voltage difference, current difference, and temperature difference, to describe the aging process of batteries. Due to the higher prediction accuracy and generalization ability of ensemble learning algorithm, the XGBoost model is established to estimate the SOH of lithium-ion battery. Then, the estimation values are corrected by Markov chain. Compared with the methods by XGBoost, random forest, k-nearest neighbor algorithm (KNN), SVM, linear regression, our proposed method shows an accuracy improvement by 10% to 20%. Additionally, the errors of our method are also superior to the others in terms of the average absolute error, root mean square error, and root mean square error.

Keywords: lithium-ion battery; SOH estimation; XGBoost; accuracy correction (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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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