One of the main research areas of the automotive sector is energy consumption and the reduction of pollutant emissions. Therefore, the hybrid electrical vehicle Vehicle electrical hybrid (VEH) is a possible approach to improve the vehicle components or its architecture to maximize system efficiency. This vehicle benefits from levels of consumption and emission of pollutants lower than the thermal vehicle. The problem is then to find the right split power between the two hybrid sources, the internal combustion engine (ICE) and the electrical machines (EM) to minimize fuel consumption. These energy management strategies are the algorithms that achieve instant split power between the two sources in the hybrid electric vehicle (HEV) while controlling the battery state of charge (SOC), in order to improve the fuel economy and optimize the performance HEV. In this research, a parametric study on the impact of optimization of the energy management strategy on the optimal operation of the vehicle is presented. A hybrid vehicle model is described and rules-based energy management strategy is presented. Later these rules are optimized with an optimization algorithm based on Grey Wolf optimizer (GWO). It is a new meta-heuristic based on the population. The results of the simulation make it possible to deduce the impact of this optimal strategy of energy management on fuel consumption and CO2 emissions. The obtained results show that the optimal operation of the vehicle varies according to the best energy management strategies.

Operating is the aim of this paper, the control of a hybrid vehicle movement in the electric mode. A 2DOF PID controller is designed for the velocity, position, and acceleration control of the vehicle. A dynamic model of the electric vehicle is achieved. The simulation results are compared to those obtained from the use of classical PID control approach.

This paper deals with the systematic design of a PID regulators with two degree of freedom 2DOF for a Hybrid vehicle driving cycle based on different variants of the Particle Swarm Optimization (PSO) algorithm. The PID 2DOF problem for the stabilization of the velocity dynamics of the hybrid vehicle are formulated as a constrained optimization problem and solved thanks to improved PSO algorithms. Both PSO algorithm with variable inertia weight (PSO-In), PSO with Constriction factor (PSO-Co), PSO with possibility updating strategies (PSO-gbest) are proposed. Such variants of the PSO algorithm aim to further improve the exploration and exploitation capabilities of such a stochastic algorithm as well as its convergence fastness. All optimized 2DOF PID controllers are then simulated within a Matlab Simulink. Demonstrative simulation results are presented, compared and discussed in order to improve the effectiveness of the proposed PSO-based 2 DOF controllers for the hybrid Vehicle velocity stabilization.

This article deals with the design and modeling of PID regulators with two degrees of freedom for the thermal phase of the hybrid vehicle. It shows how to regulate the speed of a vehicle using a PID control with two degrees of freedom with a set point weighting. Two main objectives of this controller are tracking set points and rejecting load disturbances at the same time. A number of control algorithms have been provided for such a vehicle driving phase, ranging from conventional PID algorithms to the most sophisticated advanced methods. This article proposes a 2DOF PID controller for regulating the thermal phase velocity of a hybrid vehicle.