  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.
