Data-driven techniques based on Bayesian framework like Gaussian Process Regression (GPR) can not only predict the lithium-ion battery Remaining Useful Life (RUL), but also provide the uncertainty representation. However, it is always difficult to choose the covariance function of GPR and the confidence bound is usually large if the training data are not enough. In order to solve this problem, a combining method is proposed, it is a prognostic framework based on GPR model combined with Empirical Model (EMGPR) to realize the lithium-ion battery RUL prediction. EMGPR has the advantages of predicting the tendency and uncertainty management for RUL estimation. The modeling process of EMGPR consists of two steps. The self-deterministic part, which reflects the real physical process of battery degradation, is approximated by the empirical model. And the disturbance part, which is caused by random noise such as measurement and environment noise, is expressed by the GPR model. In application, two key factors of EMGPR are focused. Firstly, the prediction result is not accurate enough if the training data are not very reliable. In this case, more reliable training data should be selected optimized. Secondly, the characteristic of the disturbance is involved to determine the kernel function of GPR model. With this EMGPR framework, the RUL result is estimated with uncertainty representation, as well, the covariance function of GPR is easy to choose. Experiments with NASA PCoE and CALCE battery data show the satisfactory result can be obtained with the EMGPR approach.
How to Cite
empirical model, Remaining useful Life, Lithium-ion battery, fusion prognostics, GPR
Cristianini, N., & Taylor J. S. (2000). An introduction to support vector machine and other kernel-based learning. Cambridge MA: MIT Press.
Dalal, M., Ma, J., & He, D. (2011). Lithium-ion battery life prognostic health management system using particle filtering framework, Institution of Mechanical Engineers, vol. 225, pp. 81-90.
Do, D. V., Forgez, C., Benkara, K. E. K., & Friedrich, G. (2009). Impedance Observer for a Li-Ion Battery Using Kalman Filter. IEEE Transaction on Vehicular Technology, vol. 58, pp. 3930-3937.
Erdinc, O., Vural B., &Uzunoglu, M. (2009). A dynamic lithium-ion battery model considering the effects of temperature and capacity fading. International Conference on Clean Electric Power, June 9-11, Capri, Italy, pp.383-386.
Gao, L. J., Liu, S. Y., & Dougal, R. A. (2002). Dynamic lithium-ion Battery Model for System Simulation. IEEE Transaction on Components and Packaging Technology, vol. 25, pp.495-505.
Goebel, K., Saha, B., & Saxena, A. (2009). A Comparison of Three Data-Driven Techniques for Prognostics, http://ti.arc.nasa.gov/m/pub-archive/1442h/1442%20(G oebel).pdf.
Goebel, K., Saha, B., Saxena, A., Celaya, J. R., & Christophersen, J. P. (2009). Prognostics in Battery Health Management, IEEE Instrumentation &Measurement Magazine. Vol. 8, pp. 33-40.
He, W., Williard, N., Osterman, M., & Pecht, M. (2011). Prognostics of Lithium-ion Batteries Based on Dempster-Shafer Theory and the Bayesian Monte Carlo Method, Journal of Power Sources, vol. 196, pp. 10314-10321.
He, W., Williard, N., Osterman, M. & Pecht, M. (2011). Prognostics of Lithium-ion Batteries using Extended Kalman Filtering, http://www.prognostics.umd.edu/calcepapers/Prognosti cs_Lithium-ion_Batteries_Kalman_Filtering.pdf.
Li, J., & Zhang, Y. P. (2010). Chaotic Time Series Single-step and Multi-step Prediction Based on Gaussian Process. Acta Phys. Sin. Vol. 60, pp. 1-10.
Liu, D., Pang, J., Zhou, J., Peng, Y. (2010). Prognostics for state of health estimation of lithium-ion batteries based on combination Gaussian process functional regression, Microelectronics Reliability, Vol. 53, pp. 832-839
Mohanty, S., Das, S., Chattopadhyay, A., & Peralta, P. (2009). Gaussian Process Time Series Model for Life Prognosis of Metallic Structures. Journal of Intelligent Material Systems and Structures, vol. 20, 887-896.
Rasmussen, C. E. (2006). Gaussian Processes in Machine Learning. The MIT Press Cambridge MA.
Rong, P. (2006). An Analytical Model for Predicting the Remaining Battery Capacity of Lithium-ion Batteries. IEEE Transaction on Very Large Scale Integration (VLSI) Systems, vol. 15, pp.441-451.
Saha, B., Goebel, K., & Christophersen, J. (2009). Comparison of prognostic algorithms for estimating remaining useful life of batteries. Transactions of Institute of Measurement and Control, vol. 31, pp:293-308. doi: 10.1177/0142331208092030
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, vol. 58, pp. 291-297.
Scholkopf, B., & Smola A. J. (2002). Learning with Kernels. Cambridge MA: MIT Press.
Snelson, E. L. (2007). Flexible and efficient Gaussian process models for machine learning, Doctoral dissertation, University of London.
Widodo, A., Shim, M. C., Caesarendra, W., & Yang, B. S. (2011). Intelligent prognostics for battery healthy monitoring based on sample entropy. Expert System with Application, vol. 38, pp. 11763-11769.
Xing, Y. J., Ma, E. W. M., Tsui, K. L., & Pecht, M. (2012). A Case Study on Battery Life Prediction Using Particle Filtering, IEEE Conference on Prognostics and System Health Management (PHM), June 18-21, Beijing, China, pp. 1-6.
Yang, Z. G., Ye, F., Guo, H., & Ma, C. F. (2012). Progress of space power technology. Chemical Industry and Engineering Progree, vol. 31, pp. 1231-1237.
Zhang, J. L., & Lee, J. (2011). A review on prognostics and health monitoring of Li-ion battery. Journal of Power Sources, vol. 196, pp. 6007-6014.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.