EV mate : a solution to electric vehicles' online hailing

Abstract

Recently, transportation-as-a-service (TaaS) becomes an increasing trend, and online car-hailing companies start to apply Electric Vehicles (EV) to serve passengers. We make the market analysis which shows this is a good entrepreneurial opportunity. However, there are many different challenges compared with traditional vehicle hailing. For instance, since the charging time of EVs is long and non-negligible, it is necessary to smartly arrange the charging periods of EVs during the working schedule. Particularly, in order to maximize the number of accomplished tasks, online EV taxi platforms assign vehicles whose left electric power is enough to serve the dynamically arriving tasks, and schedule suitable idle vehicles to the limited charging stations to recharge. In this thesis technical part, we focus on solving this challenge. We formally define the power-aware electric vehicle hailing (PAEVH) problem to serve as many tasks as possible under the constraints of left power and deadline. However, we prove that the PAEVH problem is NP-hard, and thus intractable. We design a novel strategy to help arrange the schedules of EVs, and propose two approximate approaches with theoretical guarantees to adaptively determine the value of two major parameters of the strategy. Extensive experiments on real-world data sets validate the effectiveness and efficiency of our solutions. Finally, we decide our business strategy.

Date of Award	2019
Original language	English
Awarding Institution	The Hong Kong University of Science and Technology