This paper presents a hybrid method for accurately predicting Global Horizontal Irradiance (GHI) over the following 24 hours to forecast energy production from a photo-voltaic system in a positive energy building. The input data is preprocessed using the Variational Mode Decomposition (VMD) method to extract wide-bandwidth features and decompose them into smooth modes focused on specific frequency ranges. The Salp Swarm Algorithm (SSA) is utilized to identify the optimal VMD parameters for accurate extraction. The data analysis is employed to identify the most critical modes of input features. The model's efficiency is further enhanced by performing a residual preprocessing step between the observed solar radiance data and the decomposed modes. The Stacking technique (ST) is employed to predict the 24-hour GHI modes and the residual, which are summed to reconstruct the final signal. The proposed method's performance is evaluated using the Normalized Root Mean Square Error (NRMSE) and Normalized Mean Absolute Error (NMAE) metrics on three years of available data (2019–2022) in Rabat, and compared with the model based on raw data. The results show that the proposed method achieved promising results with an NRMSE of 1.35% and NMAE of 0.82% on a cloudy day.

This article deals with the problem of wheel slip control for 4-Wheel Drive (4WD) Electric Vehicles under significant uncertainties and disturbances. Based on a longitudinal four wheels model of the vehicle and the LuGre tire/road friction model, an observer-based integral sliding mode control (ISMC) is proposed. The stability analysis using the Lyapunov function of both the ISMC control law and the nonlinear observer is proved. The theoretical results validation and proposed controller robustness and practicability have been performed by CarSim-Simulink co-simulation. Finally, a comparison with traditional controllers revealed the supremacy of the offered controller.

This paper develops a predictive current controller for an induction motor (IM), based on neural networks. More precisely, the proposed regulator treats this control problem as an optimization problem exploiting a neural predictor for IM currents. The considered objective function is constituted of two components, namely: the current tracking errors and the electromagnetic torque ripples. Moreover, this objective function is computed over a given time horizon, based on the IM currents prediction results. The Particle Swarm Optimization (PSO) algorithm is used to solve this optimization problem. The paper provides simulation results using Matlab/Simulink to prove the effectiveness of the proposed regulator compared to a conventional controller (PI). Indeed, the simulation results show that the proposed controller offers several advantages, including robustness for load variations, rotor resistance variations, and DC bus voltage variations. In addition, these results highlight a reduction in torque ripples and overshoots during transient regimes.

This paper aims to optimize the energy cost for Hybrid Thermal Electric Vehicle (HTEV). The considered HTEV combines an irreversible source fuel tank with two electrical sources, namely a lithium-ion battery and a supercapacitor. The complementarity of these energy sources improves the overall performance of the traction system. More precisely, the main objective of this article is to minimize the cost of the vehicle's mission, by exploiting the on-board energy mix. To establish a compromise between the simplicity of the real time implementation and the optimality of the solution, this paper develops a strategy that combines an optimization technique with a deterministic method. The proposed approach consists firstly in distributing the traction power between the thermal engine and the electric motor. This sharing is treated as an optimization problem under constraints. The corresponding objective function includes the on-board sources operating costs as well as penalty cost on CO2 emissions. This problem is solved using the Bellman algorithm through the dynamic programming. Thereafter, the adopted energy for supplying the electric motor is shared between the battery and the supercapacitor using a deterministic rule, namely frequency separation. To achieve optimum performances from on-board electrical sources, ajudicious choice of the sharing filter cut-off frequency is made based on several criteria, namely: the effective power, the state of charge variation, and the storage devices charge and discharge cycle number. The effectiveness of the proposed energy management system is illustrated with various simulations carried out under the Matlab environment.

The number of Electric vehicles in the world increases rapidly. To satisfy the vehicles owners, a sufficient number of charging stations will be required especially for smart buildings. The sizing of EV infrastructure in residential parking will support the deployment of the electric vehicles. This paper presents a model for optimizing the number of EV charging stations combining the multi-types charging stations and charging strategies. The objective of the optimization model is to find the suitable number of each station type while minimizing the annualized social cost, which includes the investment cost, operation and maintenance costs. The optimization problem was solved with particle swarm optimization (PSO) algorithm. The results show the impact of using different charging station types on the social cost.

In this paper an adaptive control scheme for antilock braking system (ABS) is developed based on LuGre tire-road friction model. ABS is the most important safety system during hard braking. So, it must be robust against the external disturbances, in particular the tire-road friction force variations caused by the road characteristics changes. For this aim, three main objectives are fixed ① Reference signal real time generation of the slip coefficient by using a neural network ② Estimation of the road conditions ③ Backstepping adaptive control law design to track the optimal slip value. The stability of the closed loop system is analyzed by using Lyapunov theory. The theoretical study results are evaluated by simulation under Matlab/Simulink, in different road conditions. The proposed controller supremacy is highlighted by comparing its performance to that of conventional ABS regulators.

Wireless sensor networks (WSN) have known an increased utilization in the last years in different domains including smart homes. One of the most inconvenient of these networks is the energy consumption because generally the nodes are supplied by small batteries with short autonomy. In this paper, we propose a sophisticated routing protocol approach based on Q-learning (QLRP) attempts to optimize the lifetime of the WSN used for smart home applications. The QLRP takes the benefits of the Q-learning to learn for the optimal routing path for data transmission with optimal energy consumption. We compare the proposed routing approach with two other routing protocols which are the direct routing to the sink based on a star topology and the hierarchical routing protocol. The simulation results show that the QLRP has promising advantages in terms of optimization of the energy consumption of the WSN.