Fuel Cell Electric Vehicles—A Brief Review of Current Topologies and Energy Management Strategies

TLDR

Car manufacturers are shifting toward electric/hybrid and fuel cell vehicles to meet greenhouse gas reduction standards. The paper compares recent fuel cell electric vehicle topologies and identifies new technologies and DC/DC converters for researchers and developers. The study examines fuel cell electric vehicle architectures that integrate fuel cells with batteries or ultracapacitors, and evaluates rule‑based, optimization‑based, and learning‑based energy‑management strategies in terms of efficiency, hydrogen use, and subsystem degradation. The findings suggest that AI‑based control algorithms should be pursued to address the challenges of emerging autonomous vehicle technologies.

Abstract

With the development of technologies in recent decades and the imposition of international standards to reduce greenhouse gas emissions, car manufacturers have turned their attention to new technologies related to electric/hybrid vehicles and electric fuel cell vehicles. This paper focuses on electric fuel cell vehicles, which optimally combine the fuel cell system with hybrid energy storage systems, represented by batteries and ultracapacitors, to meet the dynamic power demand required by the electric motor and auxiliary systems. This paper compares the latest proposed topologies for fuel cell electric vehicles and reveals the new technologies and DC/DC converters involved to generate up-to-date information for researchers and developers interested in this specialized field. From a software point of view, the latest energy management strategies are analyzed and compared with the reference strategies, taking into account performance indicators such as energy efficiency, hydrogen consumption and degradation of the subsystems involved, which is the main challenge for car developers. The advantages and disadvantages of three types of strategies (rule-based strategies, optimization-based strategies and learning-based strategies) are discussed. Thus, future software developers can focus on new control algorithms in the area of artificial intelligence developed to meet the challenges posed by new technologies for autonomous vehicles.