Abstract
Rechargeable batteries are widely used in many electronic products and systems to provide power sources. Because of the influence of charge/discharge cycling and some significant battery degradation factors, such as discharge rate, temperature, depth of charge, etc., on battery health condition, battery degrades over time. In this chapter, several state space models based prognostic methods are proposed to predict battery remaining useful life. Firstly, a particle filtering based state space model for battery remaining useful life prediction at a constant discharge rate is introduced. Secondly, to improve particle filtering and its application to battery prognostics, spherical cubature Kalman filtering is introduced to provide an importance function for the use of particle filtering at a constant discharge rate. Thirdly, to extend battery prognostics at a constant discharge rate to battery prognostics at different discharge rates, a more general battery degradation model is presented. Based on the developed model, a battery prognostic method at different discharge rates is designed. Some discussions are made at last.
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Acknowledgement
This work was supported in part by General Research Fund of City University of Hong Kong under Project 11216014, in part by the National Natural Science Foundation of China under Project 11471275 and Project 51505307), and in part by the Research Grants Council Theme-Based Research Scheme under Project T32-101/15-R. The authors would like to thank the reviewer for his/her valuable comments on this book chapter.
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Wang, D., Tsui, KL. (2017). State Space Models Based Prognostic Methods for Remaining Useful Life Prediction of Rechargeable Batteries. In: Chen, DG., Lio, Y., Ng, H., Tsai, TR. (eds) Statistical Modeling for Degradation Data. ICSA Book Series in Statistics. Springer, Singapore. https://doi.org/10.1007/978-981-10-5194-4_16
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DOI: https://doi.org/10.1007/978-981-10-5194-4_16
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