This paper proposes a brief review to study the implementation of fuel cells and management energy in hybrid vehicles. The use of fuel cells (FC) in the automotive field has been growing steadily over the last decades thanks to their simple operation, quiet running, and high efficiency. In addition, fuel cell vehicles have great advantages over electrified vehicles powered by secondary batteries in terms of mileage endurance, energy efficiency, charging speed and climate tolerance. In this paper, the classification and a brief introduction of HEVs/FCs are reviewed, then topologies of fuel cell HEVs are presented that illustrate the hybrid vehicle system of the different FC architectures. Finally, a discussion of control strategies for hydrogen fuel cell vehicles is implemented with the clarification of future prospects for HEVs.

No abstract

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 (Hb and Hc ). 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.