To provide a quantitative method to estimate where the charging facilities should be built, and with what capacities, given the service levels required by EV customers and based on scenarios derived from the earlier modules of this research.
Associated with the adoption of EVs are the dual concerns of range anxiety and the amount of time required for recharging in the midst of daily activities. The type and distribution of recharging infrastructure is quite relevant to addressing and reducing these consumer concerns. Associated with the optimization problem will be the issue of what the actual objective should be. Solutions that propose an aggregate minimization of customer effort could likely result in urbanized charging infrastructure associated with high density areas. On the other hand, if the objective is for consumers to never be too far away from charging infrastructure, then solutions that are more heavily weighted to rural and outlying areas might be better because they would do the most to temper the range anxiety of consumers. These are the types of issues that will be addressed through this research.
The "time" variable will be another matter of concern for the research and is also related to consumer range anxiety. One element of time is whether the charging infrastructure is available 24 hours per day. Another element is how long it takes to charge an EV's battery. Both of these are variables that could affect the adoption rate of EVs. In terms of charging infrastructure there is the question of whether an optimal network should have differing charging capabilities at different locations to provide the best overall solution.
This module is being led by Dr. Kai Huang from the McMaster DeGroote School of Business and his Master’s student Xiaozhou (Joe) Zhang. Although work was not slated to start on this module until later in the research, Dr. Huang saw an opportunity to initiate methodological progress on the module through his Master's level student Xiaozhou (Joe) Zhang.
The results of this methodological analysis has been a paper that has been submitted to Transportation Research D and is titled: “The design of electric vehicle charging network.” The research establishes a methodology for the identification of optimal public locations for level 2 and level 3 chargers. The method is based on a number of assumptions the relaxation of which requires information about the spatial distribution of the adoption of electric vehicles. The plan is to apply this new method to several Canadian cities once the outputs from the consumer and geodemographic analysis become available.
Joe Zhang investigated the literature as it relates to charging infrastructure and is ran initial optimization scenarios. The results of these efforts will be prototype for the optimization process and some initial sense of the issues that may arise. Once the results from other modules become available, they will serve as useful inputs into the actual optimization scenarios that will underlie the overall project.