Prognostics and Health management (PHM) using a proper condition-based maintenance (CBM) deployment is a worldwide-accepted strategy and has grown very popular in many industries and academia over the past decades. PHM can provide a state assessment of the future health of systems or components, e.g. when a degraded state has been found. Using this technology, one can estimate how long it will take before the equipment will reach a failure threshold, in future operating conditions and future environmental conditions. This paper deals with the improvement of prognostic accuracy for battery discharge prediction and compare with previous results done by the other researchers. In this paper, physical models and measurement data were used in the prognostic development in such a way that the degradation behaviour of the battery could be modelled and simulated in order to predict the end-of-discharge (EoD). A particle filter turned out to be the method of choice in performing the state assessment and predicting the future degradation.
Prognostics, particle filter, Battery, EoD
Candy, J. V. (2011), Bayesian signal processing: classical, modern and particle filtering methods. John Wiley \& Sons: Wiley.
Daigle, M. J. & Goebel, K. (2011), ‘A model-based prognostics approach applied to pneumatic valves’, International Journal of Prognostics and Health Management Volume 2 p. 84-99.
Khodadadi, A., Mirabadi, A. & Moshiri, B. (2010), ‘Assessment of particle filter and Kalman filter for estimating velocity using odometery system’, Sensor Review 30(3), 204–209.
Mishra M. Model-based Prognostics for Prediction of Remaining Useful Life [Internet] [Licentiate dissertation]. 2015. (Licentiate thesis / Luleå University of Technology)
Orchard, M. E. & Vachtsevanos, G. J. (2009), ‘A particlefiltering approach for on-line fault diagnosis and failure prognosis’, Transactions of the Institute of Measurement and Control, SAGE publications.
Saha, B., & Goebel, K. (2009). Modeling Li-ion Battery Capacity Depletion in a Particle Filtering Framework. Paper presented at the Annual Conference of the PHM Society, San Diego, CA
Saha, B., Celaya, J. R., Wysocki, P. F. & Goebel, K. F. (2009a), Towards prognostics for electronics components, in ‘Aerospace Conference, 2009 IEEE’, pp. 1–7.
Saha, B., Goebel, K. & Christophersen, J. (2009b), ‘Comparison of prognostic algorithms for estimating remaining useful life of batteries’, Transactions of the Institute of Measurement and Control 31, 3–4, pp. 293–308
Saxena, A., Celaya, J. R., Roychoudhury, I., Saha, S., Saha, B. & Goebel, K. (2012), Designing data-driven battery prognostic approaches for variable loading profiles: Some lessons learned, in ‘European Conference of Prognostics and Health Management Society’, pp. 72–732.
Saxena, A. Celaya J., Balaban E., Goebel K., Saha B., Saha S., and Schwabacher M., “Metrics for evaluating performance of prognostic techniques,” in Proceeding of International Conference on Prognostics and Health Management, Denver, CO, USA, pp. 1–17, 2008.