Abstract
Storage battery is one of the most important power sources in portable devices, marine systems, automotive vehicles, aerospace systems, and so on. For this kind of battery, it is essential to prognose its remaining useful life before its end of life, which would reduce some unnecessary sudden disasters caused by battery failure. In this article, we propose an improved unscented particle filter method for prognosing the remaining useful life of storage battery, in which the sigma samples of unscented transformation in traditional unscented particle filter are generated by singular value decomposition, and then, those sigma points are propagated by the standard unscented Kalman filter to generate a sophisticated proposal distribution. When both improved unscented particle filter and unscented particle filter methods were used for prognosing the remaining useful life of storage battery, it shows that the performance of improved unscented particle filter is better than unscented particle filter; the proposed method is more robust in remaining useful life prognosis procedure.
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Schedule a callMore From: Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability
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