[agents] Final CFP: Special Issue on Artificial Intelligence to Support the Deployment of Electric Vehicles

[Emmanouil Rigas]

[apologies for cross-posting]

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Artificial Intelligence to Support the Deployment of Electric Vehicles
Research topic in Frontiers in Future Transportation
https://www.frontiersin.org/research-topics/16657/artificial-intelligence-to-support-the-deployment-of-electric-vehicles
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AIMS AND SCOPE
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The negative effects of climate change are evident throughout the  
world. To cope with this threating situation, the transition to  
technologies that produce less or even zero CO2 emissions is vital. In  
this vein, Electric Vehicles (EVs) is currently the main pathway to  
decarbonize the transportation sector and significantly reduce gas  
emissions. However, in order to efficiently deploy large numbers of  
EVs and make them attractive to potential customers a number of  
problems need to be tackled:
1. Given the sparse charging infrastructure and the relatively long EV  
charging time, it is crucial to efficiently schedule their charging.  
This is a challenging problem as it must consider the demand and  
constraints of the customers, the availability of the charging  
stations and the constraints of the electricity distribution network.  
A very important challenge is to ensure that the use of energy from  
renewable sources, which are characterized by intermittent production,  
is maximized in order to make EVs truly environmentally friendly.  
Here, the close collaboration of EVs with the Smart Grid is of great  
importance.
2. The EVs have the ability to use their batteries as storage devices  
when being idle. In this way excess energy can be stored for later use  
when demand exists. This Vehicle-to-Grid (V2G) mode of operation can  
significantly increase the storage capacity of the network and,  
increase renewable energy utilization.
3. The EVs can recuperate energy under braking or when driving  
downhill. Thus, energy efficient routing that exploits this EVs’  
ability is important to increase the range and reduce the energy  
demand of the vehicles. This has a positive impact on the environment  
and the charging infrastructure, as the EVs will need to charge less  
often.
4. Emerging modes of transportation, such as the Autonomous Vehicles  
(AV), Connected Autonomous Vehicles (CAVs) and Mobility-on-Demand  
(MoD), enable different possibilities for the EVs. For example,  
Autonomous Electric Vehicles (AEVs) can fine-tune their acceleration  
profile in order to reduce their energy consumption; CAVs may exploit  
macro-level system decisions, e.g., traffic steering, to obtain  
congestion avoidance or collaborative energy-efficient path planning;  
MoD, especially in conjunction with AVs, may exploit complicated  
optimization problems involving the assignment of EVs to customers.

Controlling EVs demands efficient algorithms that can solve problems  
that involve several heterogeneous entities (e.g., EV owners), each  
one having its own goals, incentives and needs (e.g., amount of energy  
to charge), while they operate in highly dynamic environments (e.g.,  
variable number of EVs) and having to deal with a number of  
uncertainties (e.g., future energy demand). Some of these challenges  
can be tackled by powerful Artificial Intelligence (AI) techniques. In  
this special issue, we focus on the use of Artificial Intelligence  
techniques to cope with the EV-related challenges. We expect research  
and survey papers in one of the following sectors:
- Charging scheduling- Grid-to-Vehicle
- Demand response
- Dis-charging scheduling- Vehicle-to-Grid
- Virtual power plants
- Renewable energy utilization
- Energy efficient routing
- Customer behavior and incentives provision
- Electronic energy auctions
- Electric vehicles and smart grids
- Electric vehicles and smart metering
- Emerging topics (MoD, Autonomous vehicles, Connected Autonomous Vehicles)

A non-exhaustive list of potential AI techniques to be used is:
- Optimization techniques
- Heuristic and meta-heuristic algorithms
- Multi-agent systems
- Electronic auctions
- Mechanism design and game theory
- Machine learning and data analysis
- Internet of Things
- Semantic web
- Knowledge representation

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SUBMISSIONS
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Information about the article types can be found here
https://www.frontiersin.org/journals/future-transportation#article-types
and information for preparing your manuscript here
https://www.frontiersin.org/journals/future-transportation#author-guidelines

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IMPORTANT DATES
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- Deadline for papers submission: February 16, 2022

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GUEST EDITORS
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Emmanouil Rigas (Aristotle University of Thessaloniki)
Christian Vitale (University of Cyprus)
Nick Bassiliades (Aristotle University of Thessaloniki)