This paper proposes a scalable model-free multi-agent deep reinforcement learning (MADRL) framework for voltage regulation in grid-interactive efficient buildings. Unlike traditional methods that rely on reactive power control, the proposed approach utilizes active power adjustment through intelligent demand response (DR) scheduling. The architecture features a decentralized control structure, where customer agents optimize their appliance usage based on dynamic incentives from an aggregator agent. The optimization problem considers various constraints such as user comfort, electricity pricing, voltage deviation penalties, and the presence of distributed photovoltaic (PV) generation. A multi-objective function integrating dynamic price signals, user dissatisfaction, and voltage deviation is formulated. The aggregator leverages voltage-aware incentive signals to nudge consumers toward grid-supportive load behaviors. Simulation investigations are curried out to show that the MADRL framework reduces peak and mean load, improves voltage stability, and preserves user privacy. The paper aims to demonstrate the potential of decentralized, model-free DR systems in modern distribution grids.

As the global transition toward electrification accelerates across the transportation and stationary energy storage sectors, the critical need for accurate end-of-life (EoL) prediction of lithium-ion batteries (LIBs) has become increasingly apparent. Current battery failures impose substantial costs on manufacturers through warranty claims, while creating significant safety risks that threaten both electric vehicle (EV) adoption and grid-scale energy storage deployment. This paper examines the modeling approaches to predict the EoL and the remaining useful life (RUL) of LIBs in EVs. The paper includes data-driven models, physics-based approaches, and hybrid frameworks. Through systematic analysis of recent advances, the paper identifies that hybrid models demonstrate superior performance compared to single-approach methods, effectively addressing the inherent limitations of individual methodologies across diverse operating conditions. Key challenges remain in Battery Management System (BMS) integration complexity, data quality constraints, and real-time computational requirements. The proposed review establishes that next-generation prediction systems and incorporates transfer learning, digital twin technologies, and second-life battery strategies to support sustainable EV adoption and circular economy principles.

Integrating Deep Reinforcement Learning (DRL) into building energy management systems presents a transformative approach to enhancing grid stability and efficiency. Grid-Interactive Efficient Buildings (GEBs), equipped with advanced DRL algorithms, can dynamically optimize their energy consumption and production in response to real-time grid conditions. This paper explores the innovative applications of DRL in GEBs, highlighting its potential to autonomously optimize energy decisions, accommodate the stochastic nature of renewable energy sources, and effectively respond to variable building energy demands. Through a comprehensive analysis, this study not only sheds light on the successes to date but also maps out the significant challenges that must be overcome. By addressing these challenges, DRL for building energy management can fully realize its potential, leading to a more sustainable and efficient energy future.

Land transportation over the past two centuries has experienced astonishing advancement. Up until the 1860s, it took more than six months to get from the East Coast to the West Coast of the United States. Today, it may take only three days by automobile. We are even considering flying cars and there are air-taxi startup companies that have announced going public[1]. Vehicle propulsion electrification is at the core of this modern land vehicle revolution. However, the concept of electric vehicle traction is not new.

Electric and hybrid electric vehicles (EV/HEV) are promising solutions for fossil fuel conservation and pollution reduction for a safe environment and sustainable transportation. The design of these energy-efficient powertrains requires optimization of components, systems, and controls. Controls entail battery management, fuel consumption, driver performance demand emissions, and management strategy. The hardware optimization entails powertrain architecture, transmission type, power electronic converters, and energy storage systems. In this overview, all these factors are addressed and reviewed. Major challenges and future technologies for EV/HEV are also discussed. Published suggestions and recommendations are surveyed and evaluated in this review. The outcomes of detailed studies are presented in tabular form to compare the strengths and weaknesses of various methods. Furthermore, issues in the current research are discussed, and suggestions toward further advancement of the technology are offered. This article analyzes current research and suggests challenges and scope of future research in EV/HEV and can serve as a reference for those working in this field.

Electric traction is one of the most promising technologies that can lead to significant improvements in vehicle performance, energy utilization efficiency, and polluting emissions. Among several technologies, hybrid electric vehicle (HEV) traction is the most promising technology that has the advantages of high performance, high fuel efficiency, low emissions, and long operating range. Moreover, the technologies of all the component hardware are technically and markedly available. At present, almost all the major automotive manufacturers are developing hybrid electric vehicles, and some of them have marketed their productions, such as Toyota and Honda. This paper reviews the present technologies of HEVs in the range of drivetrain configuration, electric motor drives, and energy storages