The electric vehicle deployment, due to the plans defined according to the energy transition objectives, produces new challenges for the electrical system. These challenges are associated with the charging infrastructure of these vehicles since they require a high current during specific periods, which can increase losses in the network, overload the lines, or cause voltage drops that affect the system’s stability. To solve these challenges, one of the possible solutions is the investment in new network infrastructure to face the increase in demand, such as the construction of new transformation centers or new medium and low-voltage lines. However, in the case of rural networks with a small number of users, these investments may not be viable. This article analyzes the possible impacts of connecting electric vehicles in a rural low-voltage network located in a Spanish municipality, as well as possible implementable solutions that do not require investment in new infrastructure. The number of connected vehicles has been calculated based on the national plan for 2030, and the network model used is based on actual data provided by the distribution company that operates in the area.

Validating wireless power transfer (WPT) systems for electric vehicles (EVs) is a challenge due to efficiency variations caused by coil misalignments and height differences arising from various vehicle designs. Traditional simulation methods, such as finite element analysis (FEM), provide high accuracy but entail significant computational costs and calculation times, limiting the number of case studies and their optimization. This paper presents a methodology that integrates Machine Learning (ML) and Genetic Algorithms (GA) to overcome these limitations. An ML model rapidly and accurately predicts key electromagnetic parameters across a wide range of positions and frequencies. These predictions feed into a GA that optimizes control variables (voltages and frequency) with the objective of maximizing power transfer efficiency, while simultaneously ensuring component integrity at each operating point. Beyond drastically reducing simulation time and experimental effort, this methodology will enable knowledge extraction and its use for formulating design rules. These rules can lay the groundwork for developing simplified, real-time adaptive control strategies, facilitating the reduction of control variables and the narrowing of search ranges.