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A Dual-level approach is proposed for lithium-ion battery remaining useful life prognosis. The Dual-level approach is based on the Verhulst model, the logarithmic model and the PF. The first level prediction produces a rough capacity prediction and then the second level prediction improves the accuracy of the first prediction result. The logarithmic model has the ability to describe the accelerated...
Prognostics and health management has become a subject of great interest to many electrical systems. However, the lithium-ion batteries are a core component of many machines and critical to system's functional capabilities. Remaining useful life prediction is central to the PHM of the lithium-ion batteries. The remaining useful life of lithium-ion batteries is defined as length of time from current...
Accurate estimation of the remaining useful life of lithium-ion batteries plays an important role in the prognostic and health management (PHM). The traditional empirical data-driven approaches for RUL prediction usually need multidimensional input physical characteristics including the current, voltage, usage duration, battery temperature, and ambient temperature. From the capacity fading analysis...
When neural networks are used to forecast short-term power load, it can learn the experience by training and generate mapping rules, but these rules are not directly understood in the network. By using the method of integrating neural networks and fuzzy logic, neural networks only settle historical load information. Moreover, fuzzy logic considers the factors which have great effect to load varying,...
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