In the coming years, major transportation sectors will be electrified, in which more accessible and easy solutions for charging Electric Vehicles (EVs) become vital. Wireless charging is considered one of the best and easiest methods to charge EVs anywhere, even during the driving of cars. To get accurate charging power rates while charging wirelessly, advanced mathematical models are needed to accurately present the charging power. Such models must take into consideration all the coils parameters, shapes, the used compensation topology, and if the system is static or dynamic. This paper presents a comprehensive analysis of the wireless charging systems adapted for EVs by stating the most common charging topologies, and architectures, and by concentrating on the corresponding mathematical models used to calculate the given electrical power as a function of the EV’s situation on streets and its speed. Then, it is possible to evaluate the EV autonomy, and an accurate approximation can be determined when EV is on a wireless charging road. Even with the citation and comparison between the two studied models, this paper opens some perspectives for energy transmitter tools and tries to explain how renewable energy can help the deployment of this technology.

This paper presents a straightforward power management algorithm that supervises the contribution of more than one energy source for charging a vehicle, even if the car is in motion. The system is composed of a wireless charging system, photovoltaic (PV) generator, fuel cell (FC), and a battery system. It also contains a group of power converters associated with each energy resource to make the necessary adaptation between the input and output electrical signals. The boost converter relates to the PV/FC, and the boost–buck converter is connected with the battery pack. In this work, the wireless charging, FC, and PV systems are connected in parallel via a DC/DC converter for feeding the battery bank when the given energy is in excess. Therefore, for each of these elements, the mathematical model is formulated, then the corresponding power management loop is built, which presents the significant contribution of this paper. The efficient power management methodology proposed in this work was verified on Matlab/Simulink platforms. The battery state of charge and the hydrogen consumption obtained results were compared to show the effectiveness of this multi-source system.

A photovoltaic-powered electric vehicle is a complex system that necessitates the use of a high-performance control algorithm. This paper aims to boost the performance of a photovoltaic system by employing a suitable algorithm to control the power interface. The main goal is to find an effective and optimal control law that will enable the photovoltaic generator (GPV) to generate the maximum amount of power possible. The main facts dealt with in this article are the mathematical simulation of the photovoltaic system, its function, and its characteristics, considering the synthesis of the step-up converter and the analysis of the maximum power point tracking algorithm. This study examines and compares two control techniques for extracting full power from the solar energy system. These two techniques are the classical “perturbation and observation” (P&O) method and the intelligent solution “particle swarm optimization (PSO) method.” The PSO solution is tested for two versions: the online PSO version and the table PSO version. The Simulink/MATLAB tool is used for simulation and comparative experiments based on the performance metrics provided. The study revealed that smart technology delivers improved efficiency than the classic edition.

The adoption of the brushless DC motor in the electric drive vehicle industry continues to grow due to its robustness and ability to meet torque–speed requirements. This work presents the implementation of a fault-tolerant control (FTC) for a BLDC motor designed for electric vehicles. This paper focuses on studying the defect in the Ha sensor and its signal reconstruction, assuming possible cases, but the same principle is applied to the other two sensors (H_b and H_c ). In this case, the fault diagnosis allows for the correction and reconstruction of the signal in order to compel the system to work despite the presence of a fault. Indeed, several robust control systems are used within the work to regulate the speed of the motor properly, such as control via fuzzy logic and control via a neural network. This paper presents three BLDC control configurations for EVs, PID, fuzzy logic (FL), and an artificial neural network (ANN), discusses the pros and cons, and develops corresponding mathematical models to enhance a fault-tolerant control strategy which is analyzed and studied using MATLAB-based simulations (by discussing the two cases, the steady state and the transient state), allowing for a novel design based on the analytical models developed. The results obtained from the simulation of this system improved the speed controlled by the neural network compared to the fuzzy logic controller. At the same time, the sensor failure had no effect on the system’s operation due to the efficiency of the FTC control.

Wireless power transfer provides a new technology to charge electric vehicles (EVs) without physical contact. This system has to partially resolve the autonomy problem for EVs and buses. The majority of researchers were exposed to two categories of this technique: stationary wireless charging (SWC) and dynamic wireless charging (DWC) systems. Actually, the wireless recharge method is a recent solution that eliminates the problem of waiting time in recharge stations. However, the problem is always related to the vehicle status: if it is in motion and if it is in high‐speed running mode. In this context, this paper defines and examines the mathematical model of the DWC system and explains its function in relation to several parameters related to the vehicle situation. This study explores the mathematical model and identifies the best conditions for using the recharge method when the vehicle is in motion. The analysis study was elaborated using MATLAB/Simulink software, and the obtained results showed the variation of the state of charge, depending on the vehicle speed.