A dynamic programming algorithm is proposed to find the optimal charging strategies. Based on this energy cost prediction framework from real electric vehicle data, multistage optimal charging decision making models are introduced, including a deterministic model for average outcome decision making and a robust model for safest charging strategies. A real-time updating method is designed to construct this prediction model from new consecutive data points in an adaptive way for real-world applications. This is performed by analyzing a large scale electric vehicle data set. A datadriven method is introduced to establish a stochastic energy consumption prediction model with consideration of realistic uncertainties. the choice of charging station and the amount of charged energy, by considering constraints from personal daily itineraries and existing charging infrastructure. This framework aims to provide charging strategies, i.e. May 2017 - This study introduces an optimal charging decision making framework for connected and automated electric vehicles under a personal usage scenario. Data-driven optimal charging decision making for connected and automated electric vehicles: A personal usage scenario
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