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

• Published in: Energies (Basel) 2019-11, Vol.12 (22), p.4366
• Main Authors: Ali, Muhammad Umair, Zafar, Amad, Nengroo, Sarvar Hussain, Hussain, Sadam, Park, Gwan-Soo, Kim, Hee-Je
• Format: Article
• Language: English
• Subjects: Accuracy; Autoregressive moving average; Autoregressive processes; battery management system (bms); Bayesian analysis; classification; Electric vehicles; Electrical engineering; Flux density; Gaussian process; Kalman filters; Life prediction; Lithium; Lithium-ion batteries; Methods; Neural networks; partial discharge data (pdd); remaining useful life (rul); support vector machine (svm); Support vector machines; Useful life
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en12224366

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.