The urgent need for sustainable energy solutions in light of escalating global energy demands and environmental concerns has brought hydrogen to the forefront as a promising renewable resource. This study provides a comprehensive analysis of the technologies essential for the production and operation of hydrogen fuel cell vehicles, which are emerging as a viable alternative to traditional combustion engine vehicles. It examines various fuel cell types, hydrogen storage methods, refueling logistics, and the role of batteries in fuel cell vehicles. The paper also explores the potential impact of advancements in artificial intelligence and quantum computing on the development of fuel cell vehicles. A global assessment reveals that South Korea (19,270) and the United States (12,283) are leading in the adoption of fuel-cell vehicles, particularly in the passenger car segment (82%), followed by buses (9.2%) and trucks (8.7%). The study highlights the challenges hindering fuel cell vehicle implementation, such as the need for consistent investment and collaboration among industry stakeholders to promote sustainable transportation systems. The analysis underscores the practicality of fuel cell vehicles, exemplified by models like the Toyota Mirai and Hyundai Nexo, which offer significant driving ranges and demonstrate the integration of advanced technologies. The paper discusses the environmental benefits of fuel cell vehicles, including their ability to operate with zero emissions when paired with renewable energy sources. Graphical Abstract

Within the past decade, since impediments in nonrenewable fuel sources and the contamination they cause, utilizing green energies, such as those that are sun-oriented, in tandem with electric vehicles, is a developing slant. Coordinating electric vehicle (EV) charging stations with sun-powered boards (PV) reduces the burden of EV charging on the control framework. This paper presents a state-of-the-art literature review on remote control transmission frameworks for charging the batteries of electric vehicles utilizing sun-based boards as a source of power generation. The goal of this research is to advance knowledge in the wireless power transfer (WPT) framework and explore more about solar-powered electric vehicle charging stations. To do this, a variety of solar-powered electric vehicle charging station types are thoroughly studied. Following a study of many framework elements, the types of WPT components are explored in a different section. Within the wireless power transmission framework for solar-powered electric vehicle charging, compensators and various coil structures are also investigated, along with the advantages of each coil over the others. This study also discusses the use of artificial intelligence (AI) in WPT frameworks and highlights the important aspects of developing an AI model.

This study proposes an advanced hybrid fault‐tolerant control (FTC) architecture for permanent magnet synchronous motors (PMSMs) operating under speed sensor faults (SSFs), integrating model predictive control (MPC), third‐order sliding mode control (TOR‐SMC), and a model reference adaptive system (MRAS). The key innovation lies in the synergistic combination of MPC’s predictive optimization with the robustness of TOR‐SMC and the real‐time adaptive estimation capability of MRAS, enabling reliable operation in the presence of sensor degradation or failure. A residual‐based fault detection mechanism is embedded to monitor discrepancies between actual and estimated rotor speeds, enabling rapid fault identification and seamless transition to observer‐based control. The proposed hybrid control system is designed within a hierarchical architecture, wherein MPC optimizes inverter switching actions, TOR‐SMC ensures robust disturbance rejection and chattering suppression, and MRAS delivers high‐fidelity speed estimation. Simulation studies under various operating scenarios—encompassing step changes in speed, variable torque loads, and fault scenarios—demonstrate that the system achieves a maximum speed estimation error of 1.8%, speed tracking accuracy of 97.6%, and a fault detection time of less than 2.5 ms, which is 41.3% faster than extended Kalman filter (EKF)–based schemes. Quantitatively, the proposed method reduces torque ripples by 32.5%, current overshoot by 35.7%, and transient response time by 27%, while improving overall fault tolerance by 63% compared to conventional FTC approaches. The TOR‐SMC module contributes to a 78% reduction in chattering and ensures stable electromagnetic torque behavior even under dynamic torque disturbances. In parallel, MRAS offers faster convergence (2.5 ms) and smoother transitions compared to SMO and EKF observers, maintaining control integrity despite sensor anomalies. This comprehensive and modular FTC approach addresses a critical vulnerability in PMSM drive systems and is particularly well‐suited for deployment in electric vehicles, aerospace systems, and renewable energy platforms, where high reliability, real‐time responsiveness, and robustness to sensor degradation are paramount. The results confirm the proposed hybrid MPC–TOR‐SMC–MRAS framework as a scalable and high‐performance solution for next‐generation motor control systems under fault‐prone environments.

Residential buildings are a significant source of energy consumption and greenhouse gas emissions, making it crucial to accurately predict their energy demand for reducing their environmental impact. In this study, machine-learning techniques such as linear regression, decision tree classification, logistic regression, and neural networks were applied to forecast the heating and cooling loads of 12 different building types using their area and height attributes. The correlation coefficient was utilized to assign weights to the predictors in linear regression, and the models’ performance was evaluated using metrics such as equations of R2, MAE, and RMSE. The decision tree technique demonstrated the highest accuracy of 98.96% and 93.24% for predicting heating and cooling loads, respectively, among the classification methods. Notably, the cooling load prediction was more accurate than the heating load prediction. The height and area of the roof and floor, along with the relative compactness of the building, were identified as the most influential factors in the heating and cooling loads. These findings have significant implications for optimizing energy efficiency in residential buildings and mitigating their impact on climate change.