A Charging-as-a-Service Platform for Charging Electric Vehicles On the Move: New Vehicle Routing Model and Solution

TL;DR

This paper introduces a novel vehicle routing model and solution for a Charging-as-a-Service platform that enables electric vehicles to charge on the move using mobile EV-to-EV charging technology, addressing range anxiety.

Contribution

It develops a new mE2-VRP model and a clustering-aided algorithm to efficiently dispatch charging services, improving scalability and performance in large-scale urban and statewide settings.

Findings

CCWS algorithm outperforms commercial solvers in large-scale tests.

CaaS is more effective in low EV penetration and mild traffic conditions.

Proper pricing strategies can enhance service performance.

Abstract

Range anxiety has been the main challenge for the mass-adoption of electric vehicles (EVs). The emerging mobile electric-vehicle-to-electric-vehicle (mE2) charging technology, which allows an EV with the extra battery to charge the other EV on the move, offers a promising solution. Even though the physical feasibility of this new technology has been confirmed, we still face many operation problems. For example, how can we efficiently pair and route an electricity provider (EP) to a demand (ED) without introducing an extra detour? Motivated by this view, this study develops a Charging-as-a-Service (CaaS) platform, which seeks to dispatches the commercial EPs to serve the EDs, for cultivating this emerging charging service towards the low EV penetration environment. Mathematically, the CaaS platform is modeled as a vehicle routing problem (i.e., mE2-VRP), which optimally dispatches the EPs to approach and serve the EDs on the move while minimizing the EP fleet size and fulfilling all service requests. To adapt the CaaS platform to the online application in practice, we develop the Clustering-aided Clarke and Wright Savings (CCWS) algorithm to efficiently decompose and then solve the large-scale mE2-VRP by parallel computing. Our numerical experiments built upon citywide (Chicago) and statewide (Florida) found that the CCWS algorithm outperforms existing commercial solvers, and it enables us to investigate the performance of the CaaS platform under a realistic large-scale setup in a city or state. The CaaS performs better in low EV penetration markets while traffic congestion is mild and EDs require energy earlier. We can improve the performance by developing proper pricing strategies according to the EDs' energy requests and trip lengths.