This paper presents an overview of the status and future prospects of fuel-cell electric vehicles (FC-EVs). As global concerns about emissions escalate, FC-EVs have emerged as a promising substitute for traditional internal combustion engine vehicles. This paper discusses the fundamentals of fuel-cell technology considering the major types of fuel cells that have been researched and delves into the most suitable fuel cells for FC-EV applications, including comparisons with mainstream vehicle technologies. The present state of FC-EVs, ongoing research, and the challenges and opportunities that need to be accounted for are discussed. Furthermore, the comparison between promising proton-exchange membrane fuel cell (PEMFC) and solid oxide fuel cell (SOFC) technologies used in EVs provides valuable insights into their respective strengths and challenges. By synthesizing these aspects, the paper aims to provide a comprehensive understanding and facilitate decision-making for future advancements in sustainable FC-EV transportation, thereby contributing to the realization of a cleaner, greener, and more environmentally friendly future.

This paper presents an overview of the status and prospects of fuel cell electric vehicles (FC-EVs) for grid integration. In recent years, renewable energy has been explored on every front to extend the use of fossil fuels. Advanced technologies involving wind and solar energy, electric vehicles, and vehicle-to-everything (V2X) are becoming more popular for grid support. With recent developments in solid oxide fuel cell electric vehicles (SOFC-EVs), a more flexible fuel option than traditional proton-exchange membrane fuel cell electric vehicles (PEMFC-EVs), the potential for vehicle-to-grid (V2G)’s implementation is promising. Specifically, SOFC-EVs can utilize renewable biofuels or natural gas and, thus, they are not limited to pure hydrogen fuel only. This opens the opportunity for V2G’s implementation by using biofuels or readily piped natural gas at home or at charging stations. This review paper will discuss current V2G technologies and, importantly, compare battery electric vehicles (BEVs) to SOFC-EVs for V2G’s implementation and their impacts.

This review paper presents an overview of fuel cell electrochemical systems that can be used for clean large-scale power generation and energy storage as global energy concerns regarding emissions and greenhouse gases escalate. The fundamental thermochemical and operational principles of fuel cell power generation and electrolyzer technologies are discussed with a focus on high-temperature solid oxide fuel cells (SOFCs) and solid oxide electrolysis cells (SOECs) that are best suited for grid scale energy generation. SOFCs and SOECs share similar promising characteristics and have the potential to revolutionize energy conversion and storage due to improved energy efficiency and reduced carbon emissions. Electrochemical and thermodynamic foundations are presented while exploring energy conversion mechanisms, electric parameters, and efficiency in comparison with conventional power generation systems. Methods of converting hydrocarbon fuels to chemicals that can serve as fuel cell fuels are also presented. Key fuel cell challenges are also discussed, including degradation, thermal cycling, and long-term stability. The latest advancements, including in materials selection research, design, and manufacturing methods, are also presented, as they are essential for unlocking the full potential of these technologies and achieving a sustainable, near zero-emission energy future.

Battery management systems (BMSs) are integral components of electric vehicles (EVs), as they ensure the safe and efficient operation of lithium-ion batteries. State of health (SoH) estimation is one of the core functions of BMSs, providing an assessment of the current condition of a battery, while prognostics aim to predict remaining useful life (RUL) as a function of the battery’s condition. An accurate SoH estimation allows proactive maintenance to prolong battery lifespan. Traditional SoH estimation methods can be broadly divided into experiment-based and model-based approaches. Experiment-based approaches rely on direct physical measurements, while model-driven approaches use physics-based or data-driven models. Although experiment-based methods can offer high accuracy, they are often impractical and costly for real-time applications. With recent advances in artificial intelligence (AI), deep learning models have emerged as powerful alternatives for SoH prediction. This paper offers an in-depth examination of AI-driven SoH prediction technologies, including their historical development, recent advancements, and practical applications, with particular emphasis on the implementation of widely used AI algorithms for SoH prediction. Key technical challenges associated with SoH prediction, such as computational complexity, data availability constraints, interpretability issues, and real-world deployment constraints, are discussed, along with possible solution strategies.