The efficient use of the incorporation of photovoltaic generation (PV) and an electric vehicle (EV) with the home energy management system (HEMS) can play a significant role in improving grid stability in the residential area and bringing economic benefit to the homeowner. Therefore, this paper presents an energy management strategy in a smart home that integrates an electric vehicle with/without PV generation. The proposed strategy seeks to reduce the household electricity costs and flatten the load curve based on time-of-use pricing, time-varying household power demand, PV generation profile, and EV parameters (arrival and departure times, minimum and maximum limit of the state-of-charge, and initial state-of-charge). The proposed control strategy is divided into two stages: Stage A, which operates in three operating modes according to the unavailability of PV power generation, and Stage B, which operates in five operating modes according to the availability of PV generation. In this study, the proposed strategy enables controlling the amount of energy absorbed by the EV from the grid and/or PV and the amount of energy injected from the EV to the load to ensure that the household electricity costs are minimized, and the household power load profile is flattened. The findings show that both household electricity costs reduction and flattening of the power load profile are achieved. Moreover, the corresponding simulation results exhibit that the proposed strategy for the smart home with EV and PV provides better results than the smart home with EV and without PV in terms of electricity costs reduction and power load profile flattening.

With the emerging of the smart grid, it has become easier for consumers to control their consumption. The efficient use of the integration of renewable energy sources with electric vehicle (EV) and energy storage systems (ESSs) in the smart home is a popular choice to reduce electricity costs and improve the stability of the grid. Therefore, this study presents optimal energy management based on the Jaya algorithm for controlling energy flow in the smart home that contains photovoltaic generation (PV), integrated with ESS and EV. The objective of the proposed energy management is to reduce electricity cost while meeting the household load demand and energy requirement for the EV trip distance. By using the Jaya algorithm, the modes of home-to-vehicle (H2V) and vehicle-to-home (V2H) are controlled, in addition to controlling the purchase of energy from the grid and sale of the energy to the grid from surplus PV generation and ESS. Before EV participation in the V2H process, the amount of energy stored in the electric vehicle battery will be verified to be more than the energy amount required for the remaining EV trip to ensure that the required energy for the remaining EV trip is satisfied. Simulation results highlight the performance of the optimal energy scheduling to achieve the reduction of the daily electricity cost and meeting of load demand and EV energy required. The simulation results prove that optimal energy management solutions can be found with significant electricity cost savings. In addition, Jaya is compared with the particle swarm optimization (PSO) algorithm in order to evaluate its performance. Jaya outperforms PSO in terms of achieving optimal energy management objectives.

This article presents a new design method of energy management strategy with model-free DC-Bus voltage control for the fuel-cell/battery/supercapacitor hybrid electric vehicle (FCHEV) system to enhance the power performance, fuel consumption, and fuel cell lifetime by considering regulation of DC-bus voltage. First, an efficient frequency-separating based-energy management strategy (EMS) is designed using Harr wavelet transform (HWT), adaptive low-pass filter, and interval type–2 fuzzy controller (IT2FC) to determine the appropriate power distribution for different power sources. Second, the ultra-local model (ULM) is introduced to re-formulate the FCHEV system by the knowledge of the input and output signals. Then, a novel adaptive model-free integral terminal sliding mode control (AMFITSMC) based on nonlinear disturbance observer (NDO) is proposed to force the actual values of the DC-link bus voltage and the power source’s currents track their obtained reference trajectories, wherein the NDO is used to approximate the unknown dynamics of the ULM. Moreover, the Lyapunov theorem is used to verify the stability of AMFITSMC via a closed-loop system. Finally, the FCHEV system with the presented method is modeled on a Matlab/Simulink environment, and different driving schedules like WLTP, UDDS, and HWFET driving cycles are utilized for investigation. The corresponding simulation results show that the proposed technique provides better results than the other methods, such as operational mode strategy and fuzzy logic control, in terms of the reduction of fuel consumption and fuel cell power fluctuations.

In this study, an optimization approach was employed to determine the optimal main parameters that improve the performance of functionally graded composites manufactured using a combination of stirring and horizontal centrifugal casting. Pure aluminum reinforced with silicon carbide particles was used as the material for the composites. The effects of key input parameters such as mold speed, pouring temperature, stirring speed, and radial distance were optimized using a combination of grey relational analysis and response surface methodology. The statistical significance of the predicted grey relational grade model was assessed through an analysis of variance to identify the appropriate main parameters. The results showed that radial distance had the greatest impact on the performance of the composites, followed by pouring temperature. The optimal combination of main parameters was determined to be a mold speed of 1000 rpm, a pouring temperature of 750 °C, a stirring speed of 150 rpm, and a radial distance of 1 mm. Confirmation tests using these optimal values resulted in a 54.69% improvement in the grey relational grade.

This paper proposes an optimal $\alpha $ -variable model-free adaptive barrier-function fractional-order nonlinear sliding mode control ( $\alpha (t)$ -MF-ABFFONSMC) for the load frequency control (LFC) problem of a four-area interconnected hybrid power system with boiler dynamics and physical constraints. The proposed $\alpha (t)$ -MF-ABFFONSMC is comprised of the ultra-local model (ULM)-based sliding mode disturbance observer (SMDO), proportional-differential (PD) controller, and adaptive barrier-function fractional-order nonlinear sliding mode control (ABFFONSMC). The ULM mechanism is utilized to re-formulate the complex four-area interconnected hybrid power system so as to reduce the controller’s design complexity, wherein SMDO is utilized to observe and eliminate the uncertain dynamics or lumped disturbance. Then, the SMDO based-iPD controller is designed. However, there always exists non-null estimation error under the SMDO method and the control performance cannot be ensured. Therefore, the ABFFONSMC is proposed and inserted into the SMDO-iPD controller to avoid the impact of estimation error and improve the control performance. In addition, an adaptive gain based on barrier function is formulated to approximate the upper bound of SMDO’s estimation error and thus decrease the undesired chattering on the sliding surface. Correspondingly, the $\alpha (t)$ -MF-ABFFONSMC is established. Moreover, the parameter optimizer based on the Marine Predator Algorithm (MPA) is proposed to tune the parameters of the proposed $\alpha (t)$ -MF-ABFFONSMC controller. Furthermore, using the Lyapunov theorem, the stability of $\alpha (t)$ -MF-ABFFONSMC via a closed-loop system is verified. To validate the performance of the proposed controller, the numerical simulation on a four-area interconnected hybrid power system is carried out in a Matlab/Simulink environment. The corresponding simulation results are presented to show the superiority and effectiveness of the proposed technique.