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This paper addresses estimation of battery state-of-charge (SOC) from the joint perspectives of dynamic data-driven and model-based recursive analysis. The proposed SOC estimation algorithm is built upon the concepts of symbolic time series analysis (STSA) and recursive Bayesian filtering (RBF) that is a generalization of the conventional Kalman filtering. A special class of Markov models, called...
This paper presents a dynamic data-driven method of pattern classification for identification of the state-of-charge (SOC) parameter in battery systems for diverse applications (e.g., plug-in electric vehicles and hybrid locomotives). The underlying theory is built upon the concept of symbolic dynamics, which represents the behavior of battery system dynamics at different levels of SOC as probabilistic...
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