Simulation and artificial intelligence holistic approach for nationwide charging station deployment to solve electric vehicle range anxiety (SAINEVRA)
Introduction
The climate crisis caused by greenhouse gas emissions is the most important problem that humanity is currently facing on a global scale. It threatens ecosystems, society, economies, and even physical security due to extreme weather events. Therefore, urgent action is needed to reduce greenhouse gas emissions. One of the largest contributors to global greenhouse gas emissions is transport, which was responsible of about a quarter of the total CO2 emissions of the EU in 2019, of which 72% came from road transportation. In Spain, transport is the sector with the highest contribution, accounting for 27.7% of total emissions in terms of equivalent CO2 in 2020. In consequence, transport must be decarbonized, making a transition to electric vehicles (EVs) in order to meet the essential climate and environmental targets to which the Spanish government has committed itself.
However, “range anxiety”, a fear of running out of electricity before reaching another available charging station, is one of the biggest barriers (probably the most important one) in the widespread adoption of electric vehicles (EVs), in combination with a very limited availability of charging stations (CSs). Forecasts for 2030 indicate the need for hundreds of thousands of public charging points, a sharp increase over the number available a decade earlier. In Europe as a whole, electric mobility is expected to account for around 55% of all mobility in 2030, considering all types of vehicles, in the existing policies scenario, according to a study carried out by the International Energy Agency. In Spain, road transport is the main transport mode, both for passengers and goods, representing more than 80% of total mobility. The National Integrated Energy and Climate Plan 2021-2030 has set targets for the electrification of road transport in Spain by 2030 of 5 million electric vehicles, including cars, vans, motorbikes and buses. To reach this target, it is necessary to deploy an appropriate public charging infrastructure, which has been estimated for 2030 at around 250,000 public charging points, according to a study by the consultancy firm Everis, while in 2019 there were slightly less than 8,000 charging points.
Therefore, a very large number of charging stations will have to be built in the coming years. In particular, in order to solve the range anxiety problem that is breaking the generalized electrification of road transport, it is essential to build an adequate network of highway CSs at a national level. A key challenge is to determine their optimal location, as this is dependent on the interconnection between different factors: the time required for EVs to travel long distances between cities taking into account fluctuating traffic conditions, the additional distance traveled to access recharging points, the duration of wait times at charging stations, the costs associated with building new stations relative to their proximity to existing electric power grids, and the overall impact on the carbon footprint of highway traffic.
In addition, vehicle traffic is a typical case of a complex system consisting of many interacting components that give rise to emergent properties at the global level, which are difficult to predict in advance except by simulation. One emergent property of traffic is the occurrence of traffic jams, and the location of charging stations is capable of altering the patterns of traffic jams, as we have recently demonstrated. Such traffic patterns determine air pollution levels and thus the global traffic carbon footprint (because fossil fuel vehicles are involved in them), and also affect the CS optimal location as previously indicated.
Therefore, the determination of the optimal location of CSs to solve range anxiety must take into account the feedback and synergies between all the aforementioned variables. However, most previous research focused on determining the optimal configuration of CSs has not considered the feedback between all of them, relying largely on a static approach, considering only the even distribution of resources based on historical traffic flow data, and using only one or two of the aforementioned variables.
Budget
164.500,00€
Scheduling
sep 2024 - Aug 2027
Funder
Agencia Estatal de Investigación (AEI) of the Spanish Ministry of Science and Innovation (MCINN). Call 2023: Knowledge generation projects.
Project Reference
PID2023-151065OB-I00
Project status
Active
Principal Researchers
Research Subject:
Information and communications technology: Computer science and information technology
Research topics
Simulation, Artificial Intelligence, Optimization, Parallel Computing, Energy and mobility, Electric vehicles, Electric range anxiety