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

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Anthony Barr´ Frédéric Suard Mathias Gérard Delphine Riu

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
Abstract 143 | PDF Downloads 190

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Keywords

Data-driven prognostics, electric vehicle, Battery Health, Applied statistics

References
Aach, J., & Church, G. M. (2001). Aligning gene expression time series with time warping algorithms. Bioinformatics, 17(6), 495-508.
Bahlmann, C., Haasdonk, B., & Burkhardt, H. (2002). Online handwriting recognition with support vector machines - a kernel approach. In Frontiers in handwriting recognition, 2002. proceedings. eighth international workshop on (p. 49-54).
Bar-Joseph, Z., Gerber, G., Gifford, D. K., Jaakkola, T. S., & Simon, I. (2002). A new approach to analyzing gene expression time series data. In Proceedings of the sixth annual international conference on computational biology (pp. 39–48). New York, NY, USA: ACM.
Barré, A., Deguilhem, B., Grolleau, S., Gérard, M., Suard, F., & Riu, D. (2013). A review on lithium-ion battery ageing mechanisms and estimations for automotive applications. Journal of Power Sources, 241(0), 680 - 689.
Barré, A., Suard, F., Gérard, M., Montaru, M., & Riu, D. (2014). Statistical analysis for understanding and predicting battery degradations in real-life electric vehicle use. Journal of Power Sources, 245(0), 846 - 856.
Lei, H., & Sun, B. (2007). A study on the dynamic time warping in kernel machines. In Signal-image technologies and internet-based system, 2007. third international ieee conference on (p. 839-845).
Peters, A., & D¨utschke, E. (2014). How do consumers perceive electric vehicles? a comparison of german consumer groups. Journal of Environmental Policy & Planning, 0(0), 1-19.
Petitjean, F., Kurtz, C., Passat, N., & Ganarski, P. (2012). Spatio-temporal reasoning for the classification of satellite image time series. Pattern Recognition Letters, 33(13), 1805 - 1815.
Saha, B., & Goebel, K. (2008). Uncertainty management for diagnostics and prognostics of batteries using bayesian techniques. Aerospace Conference, IEEE, 1-8.
Sakoe, H., & Chiba, S. (1971). A dynamic programming approach to continuous speech recognition. In Proceedings of the seventh international congress on acoustics, budapest.
Suard, F., & Mercier, D. (2009). Using kernel basis with relevance vector machine for feature selection. In Artificial neural networks icann 2009 (Vol. 5769, p. 255-264). Springer Berlin Heidelberg.
Tipping, M. E. (2001). Sparse bayesian learning and the relevance vector machine. J. Mach. Learn. Res., 1, 211–244.
Section
Technical Papers

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