Joint research project

A Decision Support System for Innovative V2G Services using Electrical Vehicles Fleets

Project leaders
Raffaele Bruno, Ali Ghandour
LIBANO - CNRS-L - National Council for Scientific Research of Lebanon
CNR/CNRS-L biennio 2018-2019 2018-2019
Engineering, ICT and technologies for energy and transportation
Thematic area
Engineering, ICT and technologies for energy and transportation
Status of the project

Research proposal

As countries seek to address climate changes, urban air quality degradation, sustainable economic development, new policies and technologies capable of accelerating the process of transportation electrification and sustainable energy usage are commonly considered essential. As a matter of fact, transport sector is one of the largest worldwide contributors to air pollution, accounting for 46% of total NOx emissions in 2013 in Europe and about 90% of total emissions in the Arab region. Although several governments are establishing clear deployment goals for Electric Vehicles (EVs), which include Battery-powered EVs (BEVs), Hybrid EVs (HEVs) and Plug-in EVs (PEVs), and carmakers and consumers are also incentivised to embrace this technological shift, the general adoption of EVs is very low. Not only is this due to higher costs of EVs when compared to conventional cars, but most of all is due to limited driving ranges, lack of public charging infrastructures and unsatisfactory business models. However, mobility experts believe that the electrification of vehicles fleets operated by private companies and public entities (such as bus fleets, taxi fleets, car sharing fleets, etc.) may constitute the key driver to accelerate EV adoption. Furthermore, EV fleets, if equipped with smart-grid-enabled technologies, can be used as a smart storage network to offer energy storage, frequency regulation and backup services to the power grid. For instance, electric vehicles can both deliver electricity to the grid in case of peak load demands or absorb electricity fluctuations due to surplus of energy generation from renewable sources. In addition, when there is a large number of electric vehicles parked and connected, they could serve as a micro-grid during power outage in a given neighbourhood. This would be particularly beneficial in Lebanon, which is riddled by frequent electricity outages. Nowadays, Lebanese municipalities and some local energy providers have installed diesel generators in populated areas (and usually very close to housing buildings) to mitigate the impact of outages. The emissions from these generators result in additional air pollution and serious health problems. Replacing existing inefficient old cars with electric vehicles (mainly PEVs) will not only reduce polluting emissions but also will mitigate the need of these highly-polluting diesel generators. To unleash the potentials of EVs, especially in Lebanon where these vehicles have not been adopted yet at any scale, it is necessary to develop a Decision Support System (DSS) for the optimal management of fleets of electric vehicles. In particular, such a tool should be able to evaluate different operative scenarios and services (e.g., public transport, taxis, mobility-on demand services), assess the environmental impact of electric vehicles, and provide solutions to strategic and operational key issues, such as planning of charging infrastructure, design of distributed and/or centralised coordination of charging/discharging processes, and fleet management. To achieve these goals, we intend to pursue the following technical objectives: a) We will develop a data-analytics module to provide an accurate spatial and temporal characterisation of the EV energy demands at different levels, ranging from individual vehicles to entire fleets, and for different urban mobility patterns. On the one hand, our study will leverage on online mobility datasets that are available for car sharing systems, and taxi services. On the other hand, we will design and implement a mobile application to detect transportation mode and collect mobility maps by utilizing the embedded sensors and GPS data. This is particularly important for the Lebanese context, as very limited mobility data is currently available. We will also liaise with local government to collect relevant data from one or more EVs during daily operations; b) We will design and evaluate an optimisation module that exploits the demand predictions to optimise system operations and service provision. Our optimiser will cope with the intrinsic uncertainties of energy demands and mobility patterns by leveraging on robust optimisation methods and stochastic programming. Key problems to address are fleet sizing, charging infrastructure planning, and coordination algorithms for charging/discharging to provide Vehicle-to-Grid (V2G) services and power outage protection. In particular, we will consider the design of an intelligent EV aggregator that implements robust coordination strategies based on the expected daily power demand profiles. The fundamental novelty of our work is to consider emerging charging paradigms, such as power sharing, which allows multiple EVs to be recharged using a single charging facility. A discrete-event simulator will be developed to evaluate the performance of the proposed strategies in different operational scenarios; c) We will analyse policy/governance issues for the acquisition and deployment of EVs in the Lebanese context. A survey will be carried on for all related regulations, if any, that deal with the processes of acquiring and operating an EV, including but not limited to: tax credits and other incentives, vehicle registration requirement and battery disposal policies among others. Existing model assessment, in addition to conclusions and recommendations will be provided regarding the legislative framework of EVs adoption. The two teams involved in the project have complementary expertise, and consolidated experience on the topics of this project. Specifically, CNRS team will primarily work on the data collection, the development of a mobile application for transportation mode detection and the engagement of local authorities for testing EVs. CNR team will work on mining mobility data and DSS development, by leveraging on the outcomes of the ESPRIT project (, funded by H2020.

Research goals

The project focuses on the design of a Decision Support System to facilitate the deployment of fleets of EVs. Such objective meets well with the CNRS-CNR objectives of new solutions for the development of renewable energies and more sustainable transport systems. The main objectives are:
1. Raise and increase public awareness about environmental problems, mainly air pollution resulting from the transport sector.
2. Analyse the efficiency, feasibility and scalability of the usage of fleet of EVs (such as taxis fleet, buses fleet, local government fleets among others) as a smart storage network to provide V2G service and power outage protection.
3. Develop mobile application for transportation mode detection and the collection of mobility data when not available, especially in Lebanon.
4. Develop spatio-temporal energy demand models for electric vehicles under different mobility patterns.
5. Develop robust strategies for charging infrastructure planning and operation based on stochastic mobility processes and expected daily power demand profiles.
6. Assess the proposed strategies under different operational scenarios.
7. Investigate the policy/governance aspect for the deployment of EV, especially in Lebanon where these vehicles have not been adopted yet at any scale.
8. Perform a case study testbed to analyse the integration of EV with local government and collect relevant data in order to evaluate and better understand the compatibility of the proposed framework.

Last update: 27/11/2021