Strategies for a sustainable market of electric vehicles and the accompanying charging infrastructure

Abstract

The growing concerns about global climate change and urban emissions have stimulated the growth of electric vehicles (EVs), which are considered an important ingredient in sustainable transportation and a major contender to reduce traffic emissions including greenhouse gas. However, a massive adoption of EVs is hurdled by several barriers: high purchase price, limited driving range, long battery charging time, and lack of sufficient charging infrastructure. The range anxiety due to running out of battery typically arises in inter-city trips and the range will be extended in the future. The long battery charging time will be much shortened by the emerging battery swapping technology or supercharger. Therefore, to jumpstart the EV market, it relies on a massive deployment of the charging infrastructure, as well as price subsidy to reduce the high purchase price of EVs. The aim of this thesis is to analyze the impact of different strategies to increase the EV market penetration, and investigate how to allocate limited resources or budget to maximize the EV market, leading to the largest reduction in the resultant emissions. To achieve the goal, we first develop a mixed user equilibrium model with elastic EV demand to capture the charging behavior of EVs. The main difference between EVs and gasoline vehicles (GVs) lies in that certain EVs with immediate charging need have to traverse a specific station for recharging, while GVs and other EVs without immediate charging need do not have such a requirement. The proportion of EVs with immediate charging need is OD specific, related to their daily commute trip lengths and EV driving ranges, i.e. EVs will need recharging once every few days. The mixed user equilibrium (MUE) conditions state that EV drivers with charging need choose the routes via a charging station while en route to their destinations with minimum travel time cost, electricity cost plus charging station cost; whereas GV drivers and other EV drivers select the routes with minimum travel cost without having to traverse any charging station. The demands for EVs and GVs follow a logit model, whose utility functions are derived from an EV market survey conducted in Hong Kong. We formulate a convex mathematical program to capture the MUE conditions, and develop a double-stage algorithm for efficient solution. Furthermore, the MUE model exhibits the property of link flows preservation, i.e., as the EV market penetration increases up to a certain level, the link flows in the network remain unchanged. Next, based on the MUE model, we propose a mixed network design problem (MNDP) to investigate the optimal strategies for EV market development, which can be formulated as a bi-level programming model. The upper level is to design the combined strategies of the purchase price subsidy and the charging station deployment to maximize EV demand under a budget constraint. The lower level problem is the mixed user equilibrium (MUE) model given the deployment scheme and the price subsidy. A global solution algorithm of range reduction is developed to solve the MNDP. The property of link flows preservation is incorporated in the range reduction to accelerate the computing process. In the numerical studies, we discover that the optimal strategies will set the investment priority on the charging station deployment, while the purchase price subsidy is less effective if the budget is limited. Finally, for analytical evaluation of the cost-effectiveness of the promotion strategies, we develop a passenger car emission unit (PCEU) framework for estimating traffic emissions, targeting at quantifying the emission reduction due to the growth of EV market. The idea is analogous to the use of passenger car unit (PCU) for modeling the congestion effect of different vehicle types. In this approach, we integrate emission modeling and cost evaluation. Different emissions, typically speed-dependent, are integrated as an overall cost via their corresponding external costs. We then derive a speed-dependent standard cost curve and different PCEUs to represent the emission cost of different vehicle types with different emission standards. Numerical studies demonstrate that the PCUE framework achieves high accuracy but obviates tedious inputs typically required for emission estimation. In the future, we will incorporate the PCEU framework with the MUE model to quantify the emission reduction and derive optimal promotion strategies for the sake of reducing traffic emissions in the urban network.

Date of Award	2018
Original language	English
Awarding Institution	The Hong Kong University of Science and Technology