Data-Driven Prognostics of Lithium-Ion Rechargeable Battery using Bilinear Kernel Regression

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Charlie Hubbard John Bavlsik Chinmay Hegde Chao Hu

Abstract

Reliability of lithium-ion (Li-ion) rechargeable batteries has been recognized as of high importance from a broad range of stakeholders, including battery manufacturers, manufacturers of battery-powered devices, regulatory agencies, researchers, and the public. Assessing the current and future health of Li-ion batteries is essential to ensure the batteries operate safely and reliably throughout their lifetime. This paper presents a new data-driven approach for prediction of battery remaining useful life (RUL) in the presence of corruptions (or errors) in capacity features. The approach leverages bilinear kernel regression to build a nonlinear mapping between the capacity feature space and the RUL state space. Specific innovations of the approach include: i) a general framework for robust sparse prognostics that effectively incorporates sparsity into kernel regression and implicitly compensates for errors in capacity features; and ii) two numerical procedures for error estimation that efficiently derives optimal values of the regression model parameters. Results of 10 years’ continuous cycling test on Li-ion prismatic cells suggest that the proposed approach achieves robust RUL prediction despite random noise in the capacity features.

How to Cite

Hubbard, C., Bavlsik, J., Hegde, C., & Hu, C. (2016). Data-Driven Prognostics of Lithium-Ion Rechargeable Battery using Bilinear Kernel Regression. Annual Conference of the PHM Society, 8(1). https://doi.org/10.36001/phmconf.2016.v8i1.2550
Abstract 14 | PDF Downloads 11

##plugins.themes.bootstrap3.article.details##

Keywords

prognostics, Remaining useful Life, Lithium-ion battery, Bilinear Kernel Regression

References
Boyd, S., & Vandenberghe, L. (2004). Convex optimization. Cambridge university press.
Chen, Y., Caramanis, C., & Mannor, S. (2013). Robust Sparse Regression under Adversarial Corruption. In ICML (3) (pp. 774-782).
Coble, J. B., & Hines, J. W. (2008, October). Prognostic algorithm categorization with PHM challenge application. In Prognostics and Health Management, 2008. PHM 2008. International Conference on (pp. 1-11). IEEE.
Dickerson, A., Rajamani, R., Boost, M., & Jackson, J. (2015). Determining Remaining Useful Life for Li-ion Batteries (No. 2015-01-2584). SAE Technical Paper.
Gebraeel, N. Z., Lawley, M. A., Li, R., & Ryan, J. K. (2005). Residual-life distributions from component degradation signals: A Bayesian approach. IiE Transactions, 37(6), 543-557.
Gebraeel, N., & Pan, J. (2008). Prognostic degradation models for computing and updating residual life distributions in a time-varying environment. IEEE Transactions on Reliability, 57(4), 539-550.
Goebel, K., Eklund, N., & Bonanni, P. (2006, March). Fusing competing prediction algorithms for prognostics. In 2006 IEEE Aerospace Conference(pp. 10-pp). IEEE.
He, W., Williard, N., Chen, C., & Pecht, M. (2013). State of charge estimation for electric vehicle batteries using unscented Kalman filtering.Microelectronics Reliability, 53(6), 840-847.
Heimes, F. O. (2008, October). Recurrent neural networks for remaining useful life estimation. In Prognostics and Health Management, 2008. PHM 2008. International Conference on (pp. 1-6). IEEE.
Herman, M. A., & Strohmer, T. (2010). General deviants: An analysis of perturbations in compressed sensing. IEEE Journal of Selected Topics in Signal Processing, 4(2), 342-349.
Holland, P. W., & Welsch, R. E. (1977). Robust regression using iteratively reweighted least-squares. Communications in Statistics-theory and Methods,6(9), 813-827.
Hu, C., Youn, B. D., & Chung, J. (2012). A multiscale framework with extended Kalman filter for lithium-ion battery SOC and capacity estimation.Applied Energy, 92, 694-704.
Hu, C., Youn, B. D., Wang, P., & Yoon, J. T. (2012). Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life. Reliability Engineering & System Safety, 103, 120-135.
Hu, C., Jain, G., Schmidt, C., Strief, C., & Sullivan, M. (2015). Online estimation of lithium-ion battery capacity using sparse Bayesian learning. Journal of Power Sources, 289, 105-113.
Hu, C., Jain, G., Tamirisa, P., & Gorka, T. (2014). Method for estimating capacity and predicting remaining useful life of lithium-ion battery. Applied Energy, 126, 182-189.
Hu, X., Jiang, J., Cao, D., & Egardt, B. (2016). Battery health prognosis for electric vehicles using sample entropy and sparse Bayesian predictive modeling. IEEE Transactions on Industrial Electronics, 63(4), 2645-2656.
Lee, S., Kim, J., Lee, J., & Cho, B. H. (2008). State-of-charge and capacity estimation of lithium-ion battery using a new open-circuit voltage versus state-of-charge. Journal of power sources, 185(2), 1367-1373.
Liu, J., Saxena, A., Goebel, K., Saha, B., & Wang, W. (2010). An adaptive recurrent neural network for remaining useful life prediction of lithium-ion batteries. NATIONAL AERONAUTICS AND SPACE ADMINISTRATION MOFFETT FIELD CA AMES RESEARCH CENTER.
Liu, J., Wang, W., Ma, F., Yang, Y. B., & Yang, C. S. (2012). A data-model-fusion prognostic framework for dynamic system state forecasting.Engineering Applications of Artificial Intelligence, 25(4), 814-823.
Lu, L., Han, X., Li, J., Hua, J., & Ouyang, M. (2013). A review on the key issues for lithium-ion battery management in electric vehicles. Journal of power sources, 226, 272-288.
Luo, J., Pattipati, K. R., Qiao, L., & Chigusa, S. (2008). Model-based prognostic techniques applied to a suspension system. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 38(5), 1156-1168.
Plett, G. L. (2004). Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 2. Modeling and identification. Journal of power sources, 134(2), 262-276.
Plett, G. L. (2004). Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. State and parameter estimation. Journal of Power Sources, 134(2), 277-292.
Roth, V. (2001, August). Sparse kernel regressors. In International Conference on Artificial Neural Networks (pp. 339-346). Springer Berlin Heidelberg.
Saha, B., & Goebel, K. (2009). Modeling Li-ion battery capacity depletion in a particle filtering framework. In Proceedings of the annual conference of the prognostics and health management society (pp. 2909-2924).
Saha, B., Goebel, K., Poll, S., & Christophersen, J. (2009). Prognostics methods for battery health monitoring using a Bayesian framework. IEEE Transactions on Instrumentation and Measurement, 58(2), 291-296.
Si, X. S., Wang, W., Hu, C. H., & Zhou, D. H. (2011). Remaining useful life estimation–A review on the statistical data driven approaches. European Journal of Operational Research, 213(1), 1-14.
Si, X. S., Wang, W., Hu, C. H., Chen, M. Y., & Zhou, D. H. (2013). A Wiener-process-based degradation model with a recursive filter algorithm for remaining useful life estimation. Mechanical Systems and Signal Processing,35(1), 219-237.
Tikhonov, A. N., & Arsenin, V. Y. (1977). Methods for solving ill-posed problems. John Wiley and Sons, Inc.
Tipping, M. E. (2001). Sparse Bayesian learning and the relevance vector machine. Journal of machine learning research, 1(Jun), 211-244.
Trefethen, L. N., & Bau III, D. (1997). Numerical linear algebra (Vol. 50). Siam.
Van Den Berg, E., & Friedlander, M. P. (2008). Probing the Pareto frontier for basis pursuit solutions. SIAM Journal on Scientific Computing, 31(2), 890-912.
Wang, D., Miao, Q., & Pecht, M. (2013). Prognostics of lithium-ion batteries based on relevance vectors and a conditional three-parameter capacity degradation model. Journal of Power Sources, 239, 253-264.
Wang, P., Youn, B. D., & Hu, C. (2012). A generic probabilistic framework for structural health prognostics and uncertainty management. Mechanical Systems and Signal Processing, 28, 622-637.
Wang, T., Yu, J., Siegel, D., & Lee, J. (2008, October). A similarity-based prognostics approach for remaining useful life estimation of engineered systems. In Prognostics and Health Management, 2008. PHM 2008. International Conference on (pp. 1-6). IEEE.
Xiong, R., Sun, F., Chen, Z., & He, H. (2014). A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion olymer battery in electric vehicles. Applied Energy, 113, 463-476.
Section
Technical Papers