Online Remaining Useful Life Prediction for Lithium-Ion Batteries Using Partial Discharge Data Features

Muhammad Umair Ali, Amad Zafar, Sarvar Hussain Nengroo, Sadam Hussain, Gwan-Soo Park and Hee-Je Kim
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Muhammad Umair Ali: School of Electrical Engineering, Pusan National University, Pusan 46241, Korea
Amad Zafar: Department of Electrical Engineering, Wah Engineering College, University of Wah, Wah Cantt 47040, Pakistan
Sarvar Hussain Nengroo: School of Electrical Engineering, Pusan National University, Pusan 46241, Korea
Sadam Hussain: School of Electrical Engineering, Pusan National University, Pusan 46241, Korea
Gwan-Soo Park: School of Electrical Engineering, Pusan National University, Pusan 46241, Korea
Hee-Je Kim: School of Electrical Engineering, Pusan National University, Pusan 46241, Korea

Energies, 2019, vol. 12, issue 22, 1-14

Abstract: Online accurate estimation of remaining useful life (RUL) of lithium-ion batteries is a necessary feature of any smart battery management system (BMS). In this paper, a novel partial discharge data (PDD)-based support vector machine (SVM) model is proposed for RUL prediction. The proposed algorithm extracts the critical features from the voltage and temperature of PDD to train the SVM models. The classification and regression attributes of SVM are utilized to classify and predict accurate RUL. The different ranges of PDD were analyzed to find the optimal range for training the SVM model. The SVM model trained with optimal PDD features classifies the RUL into six different classes for gross estimation, and the support vector regression is used to estimate the accurate value of the last class. The classification and predictive performance of SVM model trained using the full discharge data and PDD are compared for publicly available data. Results show that the SVM classification and regression model trained with PDD features can accurately predict the RUL with low storage pressure on BMS. The PDD-based SVM model can be utilized for online RUL estimation in electric vehicles.

Keywords: battery management system (BMS); remaining useful life (RUL); support vector machine (SVM); partial discharge data (PDD); classification (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: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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