Groupe d’études et de recherche en analyse des décisions

Sharing sustainable mobility in smart cities

Wei Qi Professeur adjoint, Faculté de gestion Desautels, Université McGill, Canada

Many cities worldwide are embracing electric vehicle (EV) sharing as a flexible and sustainable means of urban transit. However, it remains challenging for the operators to charge the fleet due to limited or costly access to charging facilities. In this work, we focus on answering the core question - how to charge the fleet to make EV sharing viable and profitable. Our work is motivated by the recent setback that struck San Diego, California, where car2go ceased its EV sharing operations. We integrate charging infrastructure planning and vehicle repositioning operations that were often considered separately in the literature. More interestingly, our modeling emphasizes the operator-controlled charging operations and customers' EV picking behavior, which are both central to EV sharing but were largely overlooked. Motivated by the actual data of car2go, our model explicitly characterizes how customers endogenously pick EVs based on energy levels, and how the operator dispatches EV charging under a targeted charging policy. We formulate the integrated model as a nonlinear optimization program with fractional constraints. We then develop both lower- and upper-bound formulations as mixed-integer second order cone programs, which are computationally tractable with small optimality gap.

Contrary to car2go's practice, we find that the viability of EV sharing can be enhanced by concentrating limited charger resources at selected locations. Charging EVs in a proactive fashion (rather than car2go's policy of charging EVs only when their energy level drops below 20%) can boost the profit by 10.7%. Given the demand profile in San Diego, the fleet size may reduce by up to 34% without incurring significant profit loss. Moreover, sufficient charger availability is crucial when collaborating with a public charger network. Finally, increasing the charging power relieves the charger resource constraint, whereas extending per-charge range or adopting unmanned repositioning improves profitability. In summary, our work demonstrates a data-verified and high-granularity modeling approach. Both the high-level planning guidelines and operational policies can be useful for practitioners. We also highlight the value of jointly managing demand fulfilment and EV charging.


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