Driving range

Abstract: To enhance environmental sustainability, many countries will electrify their transportation systems in their future smart city plans, so the number of electric vehicles (EVs) running in a city will grow significantly. There are many ways to recharge EVs' batteries and charging stations will be considered as the main source of energy. The locations of charging stations are critical; they should not only be pervasive enough such that an EV anywhere can easily access a charging station within its driving range, but also widely spread so that EVs can cruise around the whole city upon being recharged. Based on these new perspectives, we formulate the EV charging station placement problem (EVCSPP) in this paper. We prove that the problem is nondeterministic polynomial-time hard. We also propose four solution methods to tackle EVCSPP, and evaluate their performance on various artificial and practical cases. As verified by the simulation results, the methods have their own characteristics and they are suitable for different situations depending on the requirements for solution quality, algorithmic efficiency, problem size, nature of the algorithm, and existence of system prerequisite.

Abstract: To enhance environmental sustainability, many countries will electrify their transportation systems in their future smart city plans. So the number of electric vehicles (EVs) running in a city will grow significantly. There are many ways to re-charge EVs' batteries and charging stations will be considered as the main source of energy. The locations of charging stations are critical; they should not only be pervasive enough such that an EV anywhere can easily access a charging station within its driving range, but also widely spread so that EVs can cruise around the whole city upon being re-charged. Based on these new perspectives, we formulate the Electric Vehicle Charging Station Placement Problem (EVCSPP) in this paper. We prove that the problem is non-deterministic polynomial-time hard. We also propose four solution methods to tackle EVCSPP and evaluate their performance on various artificial and practical cases. As verified by the simulation results, the methods have their own characteristics and they are suitable for different situations depending on the requirements for solution quality, algorithmic efficiency, problem size, nature of the algorithm, and existence of system prerequisite.

Abstract: Dynamic wireless charging of electric vehicles (EVs) can significantly extend the EVs’ driving range and consequently, the prospect of electrified transportation. In this paper, a comprehensive study is conducted to elaborate the constraints of real driving conditions and propose a solution that could cope with misalignment problem and the dynamics imposed by the charging process and by EVs passing over road-embedded charging pads. A dual-loop primary controller is proposed to regulate primary-side power and current. The controller allows sequential and timely activation of segmented primary coils; it controls the primary coil current at the reference value under no-load and loaded conditions, compensates for power transfer reduction caused by the vehicle lateral misalignment (LTM), and prevents primary overloading. The primary of the dynamic wireless charger is modeled using the generalized state-space averaging method and the model is verified through simulations and experiments. After that, a controller has been designed and implemented and its operation is evaluated through simulations and experimental tests. A 25-kW charging system with two primary coils is built and tested in a real environment. The measured energy efficiency is 86% for the laterally aligned vehicle, with the possibility to be increased over 90% using enhanced schemes for coils’ activation and deactivation. The system is delivering an equal amount of energy for all LTMs in the range of ±15 cm, which improves the expected value of transferred energy by more than 30%.

Abstract: Reasonable charging station positions are critical to prompt the widespread use of electric vehicles (EVs). This paper proposes a bi-level programming model with the consideration of EV’s driving range, for finding the optimal locations of charging stations. In this model, the upper level is to optimize the position of charging stations so as to maximize the path flows that use the charging stations, while the user equilibrium of route choice with the EV’s driving range constraint is formulated in the lower level. In order to find the optimal solution of the model efficiently, we reformulate the proposed model as a single-level mathematical program and further linearize it in designing the heuristic algorithm. The model validity is demonstrated with numerical examples on two test networks. It is shown that the vehicle’s driving range has a great influence on the optimal charging station locations.