Electric vehicles (EV) and photovoltaic (PV) systems are increasingly becoming environmentally friendly and more affordable solutions for consumers. This article discusses the integration of PV and EV in a residential system to meet the requirements of residential loads taking into account the PV supplied power, availability and the state of charge (SOC) of EVs. A hybrid control model has been proposed to control the residential system. The combined PI-Fuzzy logic controller is employed to control the buck-boost bi-directional converter. The DC-AC grid-side converter is controlled by the ADRC controller. The effectiveness of PI-Fuzzy logic controller in reducing voltage and current ripples and ADRC controller in rejecting disturbances is demonstrated in each case. A rule-based energy management strategy has been proposed to control the flow of energy between the components of the residential system. The suggested energy management system (EMS) covers every scenario that might occur. Whether the EV is linked to the home or not, and also takes into account the owner using the EV in an emergency situation. The EV operates in two modes, Home-to-Vehicle (H2V) mode and Vehicle-to-Home (V2H) mode, depending on the power produced by the PV and the conditions related to the EV. All possible scenarios are tested and validated. The simulation results show that the proposed EMS is a reliable solution that can reduce the power grid intervention.

This article introduces an innovative approach called Adaptive Deadbeat Predictive Control (ADPC), specifically designed to regulate the operation of a Permanent Magnet Synchronous Motor (PMSM) within a solar-powered electric vehicle (SEV). Considering the influence of factors such as magnetic saturation, material aging, and temperature fluctuations, discrepancies may arise between actual and nominal parameter values. Notably, the stator inductance, permanent magnet flux linkage, and stator resistance are crucial variables in this context. The article conducts an in-depth theoretical examination of the consequences of these parameter mismatches on the overall stability of the system. To mitigate the effects of stator resistance variability, an adaptive control methodology is proposed. This strategy hinges on real-time control adaptation, achieved by continuously estimating the resistance value through diligent consideration of the actual winding temperature. The proposed Adaptive Deadbeat Predictive Control framework is seamlessly implemented and simulated within the MATLAB Simulink environment. The simulation results eloquently validate the efficacy of the proposed control approach, showcasing its adeptness in mitigating the system’s sensitivity to stator resistance deviations, thereby fortifying the overall control resilience.