Abstract
The promotion of the battery electric vehicle has become a worldwide problem for governments due to its short endurance range and slow charging rate. Besides an appropriate network of charging facilities, a subsidy has proved to be an effective way to increase the market share of battery electric vehicles. In this paper, we investigate the joint optimal policy for a subsidy on electric vehicles and infrastructure construction in a highway network, where the impact of siting and sizing of fast charging stations and the impact of subsidy on the potential electric vehicle flows is considered. A new specified local search (LS)-based algorithm is developed to maximize the overall number of available battery electric vehicles in the network, which can get provide better solutions in most situations when compared with existed algorithms. Moreover, we firstly combined the existing algorithms to establish a multi-stage optimization method, which can obtain better solutions than all existed algorithms. A practical case from the highway network in Hunan, China, is studied to analyze the factors that impact the choice of subsidy and the deployment of charging stations. The results prove that the joint policy for subsidy and infrastructure construction can be effectively improved with the optimization model and the algorithms we developed. The managerial analysis indicates that the improvement on the capacity of charging facility can increase the proportion of construction fees in the total budget, while the improvement in the endurance range of battery electric vehicles is more efficient in expanding battery electric vehicle adoption in the highway network. A more detailed formulation of the battery electric vehicle flow demand and equilibrium situation will be studied in the future.
Highlights
Battery electric vehicles (BEVs) are regarded as a most promising solution to increasingly serious air pollution and global warming [1]
As the budget is limited, to maximize charged the network, a local local search-based algorithm is illustrated to findthe thenumber optimalofsiting andBEVs sizinginscheme of a charging search-based is illustrated to find the optimal siting and sizing scheme of a charging station, station, whichalgorithm can further improve the solution with two-stage optimization
The number of BEVs that can be charged by a charging pile per range of a BEV is assumed to be 150 km
Summary
Battery electric vehicles (BEVs) are regarded as a most promising solution to increasingly serious air pollution and global warming [1]. The widely adoption of BEV can bring many advantages, such as energy conservation, emission reduction and clean energy application [2,3]. The popularization of BEVs is a universal difficulty for the governments worldwide. Compared with traditional internal combustion engine (IDE) vehicles at the same price, the endurance range of BEVs is much shorter [4]. The adoption of BEVs in a highway network relies on a widespread network of charging facilities. Customers are reductant to purchase BEVs due to the long recharging time, even though there may be sufficient charging facilities. Besides constructing basic infrastructures, governments need to increase the ownership of BEVs among citizens
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