A Real-time Data-driven Method for Battery Health Prognostics in Electric Vehicle Use

Anthony Barr´; Frédéric Suard; Mathias Gérard; Delphine Riu

doi:10.36001/phme.2014.v2i1.1514

A Real-time Data-driven Method for Battery Health Prognostics in Electric Vehicle Use

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Published Jul 8, 2014

DOI https://doi.org/10.36001/phme.2014.v2i1.1514

Anthony Barr´

CEA, LIST, Information Models and Machine Learning, 91191 Gif sur Yvette CEDEX, France

Frédéric Suard

CEA, LIST, Information Models and Machine Learning, 91191 Gif sur Yvette CEDEX, France

Mathias Gérard

CEA, LITEN, 17 rue des martyrs, 38054 Grenoble CEDEX 9, France

Delphine Riu

G2Elab, UMR 5269, 38402 Saint Martin d’H`eres, France

Abstract

Online prognostics of the battery capacity is a major challenge as ageing process is a complex phenomenon, hardly directly measurable. This paper offers a new methodology for real-time estimating of the global battery performances for Electric Vehicle (EV) use. The presented data-driven framework build a model based on the modifications in battery signals behavior, according to the performance level. A first pattern extraction step consists in the selection of battery signals corresponding to specific acceleration profiles in real uses, allowing to highlight the battery behavior. These extracted voltage and current patterns are then considered to determine the battery behavior for each State of Health (SOH) feature. Studied patterns are compared using signal processing techniques, allowing the estimation of the battery performance, through statistical learning methods. The application of signal processing and Relevance Vector Machines (RVM) model
with multiple kernels, provides a powerful tool to diagnose battery health online, only based on real signals. Furthermore, this methodology also allows the prediction of battery Remaining Useful Life (RUL) during real use. The proposed algorithm is validated using datasets from real EV uses. Presented diagnostics results on real data demonstrate the good accuracy of this new framework for battery SOH prognostics in real-time constraints, with uncontrolled conditions.

How to Cite

Barr´, A., Suard, F., Gérard, M., & Riu, D. (2014). A Real-time Data-driven Method for Battery Health Prognostics in Electric Vehicle Use. PHM Society European Conference, 2(1). https://doi.org/10.36001/phme.2014.v2i1.1514

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