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Ecee ma 810 pdf12/18/2022 ![]() ![]() ![]() ![]() To address this issue, this paper presents unified SOC forecasting models using a Minimized Akaike Information Criterion (m-AIC) algorithm. Existing works on hybrid techniques either perform in-sample predictions, provide limited information of the underlying model, or do not consider varying charging-discharging rate (C-rate) dynamics of the battery. Conventional standalone machine learning techniques applied in recent works suffer from an accuracy standpoint and thus have been replaced by high-fidelity hybrid machine learning techniques. ![]() Recently, machine learning techniques have been increasingly applied to forecast one such battery information metric, State-of-Charge % (SOC). Lithium-Ion batteries require step-ahead information to apply contingency plans to prevent them from operating beyond their safe operation thresholds in grid storage and electric vehicle applications. A summary of the existing QB researches is also presented which are categorized into particle- and chemistry-driven (quantum) battery researches. This is followed by a review of the underlying operation of a QC and its application in performing battery research. Also, the explanations of various postulates and their corresponding formulations leading to the QM framework behind a QB is presented. This paper attempts to piece together the QM and its applications in a QC, directed towards the identification of an innovative QB, by providing a review of the existing researches. The applicable storage system under research using QC, termed Quantum Battery (QB) is considered to be a theoretical construct, which means that it is not directly observable. Quantum Computers (QC) provide the capability to develop an innovative energy storage system, but its operating principles encompass the domains of Quantum Physics (QP) and Quantum Mechanics (QM) and thus limit the understanding of its underlying functionality. An across-the-board view of this technology identifies that the intersecting research of interest which covers both areas is the identification of innovative energy storage technologies. Quantum Computing technology is currently prevailing as a promising candidate for expanding the research horizons in the areas of power engineering and transportation. ![]()
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