Model-Based Prognostic Approach for Battery Variable Loading Conditions: Some Accuracy Improved
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Abstract
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.
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Prognostics, particle filter, Battery, EoD
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