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This paper addresses the problem of optimally placing charging stations in urban areas. Two optimization criteria are used: maximizing the number of reachable households and minimizing overall e-transportation energy cost. The decision making models used for both cases are mixed integer programming with linear and nonlinear energy-aware constraints. A multi-objective optimization model that handles both criteria (number of reachable households and transportation energy) simultaneously is also presented. A number of simulation results are provided for two different cities in order to illustrate the proposed methods. Among other insights, these results show that the multi-objective optimization provides improved placement results.

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Special section 'Design, modelling and control of electric vehicles') This study provides a detailed deterministic and stochastic sensitivity analysis of the propulsion energy cost of electric vehicles (EVs) with respect to environmental variables. In particular, the effects of wind speed, rolling resistance, parasitic power and temperature are highlighted. The study provides exact analytical expressions as well as simulations to illustrate the key results. It is shown that the sensitivity of energy consumption with respect to the four environmental variables greatly vary with operating conditions of the vehicle. These environmental effects can have a profound effect on the overall energy consumption of EVs and drastically affect range. The significance of the authors' findings for vehicle range estimation is discussed and potential avenues to exploit the strong dependency between propulsion energy and environmental factors are proposed.