The recent advent of electric vehicles (EVs) marks the beginning of a new positive era in the transportation sector. Although the environmental benefits of EVs are well-known today, planning and managing EV charging infrastructure are activities that are still not well-understood. In this paper, we are investigating how the so-called EV-enabled parking lot (EVPL), a parking lot that is equipped with a certain number of chargers, can define an appropriate parking policy in such a way that satisfies two challenges: EV owners’ needs for recharging as well as the parking lot operator’s goal of profit maximization. Concretely, we present three parking policies that are able to simultaneously deal with both EVs and internal combustion engine vehicles. Detailed sensitivity analysis, based on realworld data and simulations, evaluates the proposed parking policies in a case study concerning parking lots in Melbourne, Australia. Our study produces results that are highly prescriptive in nature because they inform a decision maker under which circumstances a certain parking policy operates optimally. Most notably, we find that the dynamic parking policy, which takes the advantage of advanced information technology (IT) and charging infrastructure by dynamically changing the role of parking spots with chargers, often outperforms the other two parking policies because it maximizes the profit and minimizes the chance of cars being rejected by the parking lot. We also discuss how making a few parking spots EV-exclusive might be a good policy when the number of available chargers is small and/or the required IT infrastructure is not in place for using the dynamic policy. We conclude our work by proposing a technology roadmap for transforming parking lots into smart EV-enabled parking lots based on the three studied parking policies.

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doi.org/10.1109/ACCESS.2017.2777098, hdl.handle.net/1765/103224
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IEEE Access

Babic, J. (Jurica), Carvalho, A., Ketter, W., & Podobnik, V. (Vedran). (2017). Evaluating Policies for Parking Lots Handling Electric Vehicles. IEEE Access. doi:10.1109/ACCESS.2017.2777098