A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimation. (1st December 2015)

• Record Type: Journal Article
• Authors: Patil, Meru A.; Tagade, Piyush; Hariharan, Krishnan S.; Kolake, Subramanya M.; Song, Taewon; Yeo, Taejung; Doo, Seokgwang
• Is Part Of: Applied energy. Volume 159(2015:Dec. 01)
• Page Start: 285
• Page End: 297
• Publication Date: 2015-12-01
• Subjects: Remaining Useful Life -- Classification -- Regression -- Support Vector Machine -- Battery life models; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2015.08.119
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Novel multi step data analytic approach combining classification and regression. Extraction of minimal set of critical features from battery cycling data. Validation based on open source data of various types of batteries. Accurate and fast estimation of RUL of multi-cell data. Abstract: Real-time prediction of remaining useful life (RUL) is an essential feature of a robust battery management system (BMS). In this work, a novel method for real-time RUL estimation of Li ion batteries is proposed that integrates classification and regression attributes of Support Vector (SV) based machine learning technique. Cycling data of Li-ion batteries under different operating conditions are analyzed, and the critical features are extracted from the voltage and temperature profiles. The classification and regression models for RUL are built based on the critical features using Support Vector Machine (SVM). The classification model provides a gross estimation, and the Support Vector Regression (SVR) is used to predict the accurate RUL if the battery is close to the end of life (EOL). By the critical feature extraction and the multistage approach, accurate RUL prediction of multiple batteries is accomplished simultaneously, making the proposed method generic in nature. In addition to accuracy, the multistage approach results in faster computations, and hence a trained model can potentially be used for real-time onboard RUL estimation for electric vehicle battery packs.