Data-Driven Lead-Acid Battery Prognostics Using Random Survival Forests

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Erik Frisk Mattias Krysander Emil Larsson

Abstract

Problems with starter batteries in heavy-duty trucks can cause costly unplanned stops along the road. Frequent battery changes can increase availability but is expensive and sometimes not necessary since battery degradation is highly dependent on the particular vehicle usage and ambient conditions. The main contribution of this work is a case-study where prognostic information on remaining useful life of lead-acid batteries in individual Scania heavy-duty trucks is computed. A data- driven approach using random survival forests is proposed where the prognostic algorithm has access to fleet management data including 291 variables from 33603 vehicles from 5 different European markets. The data is a mix of numerical values such as temperatures and pressures, together with histograms and categorical data such as battery mount point. Implementation aspects are discussed such as how to include histogram data and how to reduce the computational complexity by reducing the number of variables. Finally, battery lifetime predictions are computed and evaluated on recorded data from Scania’s fleet-management system.

How to Cite

Frisk, E. ., Krysander, M. ., & Larsson, E. (2014). Data-Driven Lead-Acid Battery Prognostics Using Random Survival Forests. Annual Conference of the PHM Society, 6(1). https://doi.org/10.36001/phmconf.2014.v6i1.2370
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Keywords

prognostics, Data Driven, battery, random survival forests, heavy-duty trucks

References
Breiman, L. (1996). Bagging predictors. Machine learning, 24(2), 123–140.

Breiman, L. (2001). Random forests. Machine learning, 45(1),5–32.

Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984).Classification and regression trees. CRC press.

Cox, D. R., & Oakes, D. (1984). Analysis of survival data (Vol. 21). CRC Press.

Harrell, F. E., Califf, R. M., Pryor, D. B., Lee, K. L., & Rosati, R. A. (1982). Evaluating the yield of medical tests.

Jama, 247(18), 2543–2546.Ishwaran, H., & Kogalur, U. (2013). Random forests for survival, regression and classification (rf-src) [Computer software manual]. manual. Retrieved from http://cran.r-project.org/ web/packages/randomForestSRC/ (R pack- age version 1.4)

Ishwaran, H., & Kogalur, U. B. (2010). Consistency of random survival forests. Statistics & probability letters, 80(13), 1056–1064.

Ishwaran, H., Kogalur, U. B., Blackstone, E. H., & Lauer, M. S. (2008). Random survival forests. The Annals of Applied Statistics, 841–860.

Ishwaran, H., et al. (2007). Variable importance in binary regression trees and forests. Electronic Journal of Statis- tics, 1, 519–537.

Linxia, L., & Köttig, F. (2014, March). Review of hybrid prognostics approaches for remaining useful life prediction of engineered systems, and an application to battery life prediction. IEEE Transactions on Reliability, 63(1), 191–207.

R Core Team. (2014). R: A language and environment for statistical computing [Computer software manual]. Vienna, Austria. Retrieved from http://www.R-project .org/

Wager, S., Hastie, T., & Efron, B. (2014). Confidence intervals for random forests: The jackknife and the infinitesimal jackknife. Journal of Machine Learning Research, 15, 1625-1651.
Section
Technical Papers