Mobility and Energy Management in Electric Vehicle Based Mobility-on-Demand Systems: Models and Solutions

An electric vehicle based mobility-on-demand (EMoD) system provides shared transportation (e.g., car-sharing or ride-sharing) to satisfy customers’ individual mobility demands. It has been recognized as a vital alternative form of transportation between public and private transportations in future sustainable cities. Constrained by the long charging time and limited driving range of EVs, an operator of an EMoD system demands for decision-making models and algorithms to manage the mobility and energy of EVs to best serve customers with least costs. In this paper, we propose a stochastic dynamic program (DP) to model three operational decisions of the EMoD system: i) dispatching EVs to serve mobility demand from customers, ii) repositioning EVs to accommodate the unbalanced mobility demands between service regions, and iii) recharging EVs to maintain their sufficient state-of-charge levels. To handle this large-scale DP problem, we first observe and prove that it has a coordinate-wise concave value function. Based on this structural property, we propose to use a separable piecewise linear function to approximate the value function and design an approximation-based algorithm to efficiently derive the decision policy. Numerical tests show that our proposed algorithm significantly outperforms the existing model-free approaches (e.g., greedy heuristic and Q-learning) that fail to take into account the structural properties of the DP problem.