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.

An optimal control approach is developed for Four-Wheel Active Steering Vehicles. First, parameterized vehicle model is built. Second, active steering model is implemented and coupled to the vehicle model. Finally, an optimal control loop, delivering as final output optimal vehicle performances of Four-Wheel Active Steering Vehicles is developed.

We report a methodological approach for simulating luminary output radiation, which is achieved by mixing light-emitting diodes (LEDs) in order to match any plant absorption spectrum. Various recorded narrow-band LED spectra of different colors were first characterized and then fitted with a multi-Gaussian model. An optimizing procedure computed the optimal weighting of the relevant parameters so as to minimize the discrepancy between the combined spectrum and the reference target curve. The particle swarm optimization (PSO) method was applied because it is the most suitable technique for mono-objective situations. Within the useful spectral interval, the worst relative standard deviation between the optimized curve and recorded LED spectral power distribution (SPD) was 3.4%. When combining different LED types, the simulated light output showed that we could limit ourselves to selecting only five colored sources. This work will help us to design an optimized 200 W laboratory luminaire with a pulse-width switched-mode power supply.

Energy storage is a key issue for hybrid traction vehicles (HEVs). Indeed, it takes more power and energy density, a correct size, a long life of the battery. The HEV seems to be an appropriate device to address these constraints if we find the best strategy for controlling and split power, between the thermal and electrical sources. The purpose of this article is to propose different energy management strategies(EMS) based on rules and using batteries. Two control strategies are developed and compared for different criteria as energy consumption, design and expected battery life. Validation tests are performed and compared to the energy consumption of the thermal vehicle.