Data-driven optimal charging decision making for connected and automated electric vehicles: A personal usage scenario rights and content


A data-driven stochastic EV energy consumption prediction framework is proposed.

A real-time updating framework for EV energy cost prediction is introduced.

Multistage charging decision making frameworks for CAEVs are constructed.

Optimal charging strategies are achieved by performing dynamic programming.


This study introduces an optimal charging decision making framework for connected and automated electric vehicles under a personal usage scenario. This framework aims to provide charging strategies, i.e. the choice of charging station and the amount of charged energy, by considering constraints from personal daily itineraries and existing charging infrastructure. A data-driven method is introduced to establish a stochastic energy consumption prediction model with consideration of realistic uncertainties. This is performed by analyzing a large scale electric vehicle data set. 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. 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 dynamic programming algorithm is proposed to find the optimal charging strategies. Detailed simulations and case studies demonstrate the performance of the proposed algorithms to find optimal charging strategies. They also show the potential capability of connected and automated electric vehicles to reduce the range anxiety and charging infrastructure dependency.


Data-driven method
Charging decision making
Connected and automated electric vehicles
Energy consumption prediction
Multistage decision making
Dynamic programming
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