Sustainable Mobility: Machine Learning-Driven Deployment of EV Charging Points in Dublin

Alexander Mutiso Mutua and Ruairí de Fréin ()
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Alexander Mutiso Mutua: School of Electrical and Electronic Engineering, Technological University Dublin, D07 EWV4 Dublin, Ireland
Ruairí de Fréin: School of Electrical and Electronic Engineering, Technological University Dublin, D07 EWV4 Dublin, Ireland

Sustainability, 2024, vol. 16, issue 22, 1-37

Abstract: Electric vehicle (EV) drivers in urban areas face range anxiety due to the fear of running out of charge without timely access to charging points (CPs). The lack of sufficient numbers of CPs has hindered EV adoption and negatively impacted the progress of sustainable mobility. We propose a CP distribution algorithm that is machine learning-based and leverages population density, points of interest (POIs), and the most used roads as input parameters to determine the best locations for deploying CPs. The objects of the following research are as follows: (1) to allocate weights to the three parameters in a 6 km by 10 km grid size scenario in Dublin in Ireland so that the best CP distribution is obtained; (2) to use a feedforward neural network (FNNs) model to predict the best parameter weight combinations and the corresponding CPs. CP deployment solutions are classified as successful when an EV is located within 100 m of a CP at the end of a trip. We find that (1) integrating the GEECharge and EV Portacharge algorithms with FNNs optimises the distribution of CPs; (2) the normalised optimal weights for the population density, POIs, and most used road parameters determined by this approach result in approximately 109 CPs being allocated in Dublin; (3) resizing the grid from 6 km by 10 km to 10 km by 6 km and rotating it at an angle of − 350 ∘ results in a 5.7% rise in the overall number of CPs in Dublin; (4) reducing the grid cell size from 1 km 2 to 500 m 2 reduces the mean distance between CPs and the EVs. This research is vital to city planners as we show that city planners can use readily available data to generate these parameters for urban planning decisions that result in EV CP networks, which have increased efficiency. This will promote EV usage in urban transportation, leading to greater sustainability.

Keywords: electric vehicle charging points; charging infrastructure optimisation; charging point placement strategies; EV charging demand; machine learning; feedforward neural networks; sustainable urban planning; smart transport systems; range anxiety; optimisation of infrastructure (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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