A Review of Nanofluids as Coolants for Thermal Management Systems in Fuel Cell Vehicles

With the development of high-power fuel cell vehicles, heat dissipation requirements have become increasingly stringent. Although conventional cooling techniques improve the heat dissipation capacity by increasing the fan rotating speed or radiator dimensions, high energy consumption and limited engine compartment space prevent their implementation. Moreover, the insufficient heat transfer capacity of existing coolants limits the enhancement of heat dissipation performance. Therefore, exploring novel coolants to replace traditional coolants is important. Nanofluids composed of nanoparticles and base liquids are promising alternatives, effectively improving the heat transfer capacity of the base liquid. However, challenges remain that prevent their use in fuel cell vehicles. These include issues regarding the nanofluid stability and cleaning, erosion and abrasion, thermal conductivity, and electrical conductivity. In this review, we summarize the nanofluid applications in oil-fueled, electric, and fuel cell vehicles. Subsequently, we provide a comprehensive literature review of the challenges and future research directions of nanofluids as coolants in fuel cell vehicles. This review demonstrates the potential of nanofluids as an alternative thermal management system that can facilitate transition toward a low-carbon, energy-secure economy. It will serve as a reference for researchers to focus on new areas that could drive the field forward.

Review of Energy Management Methods for Fuel Cell Vehicles: From the Perspective of Driving Cycle Information

Energy management methods (EMMs) utilizing sensing, communication, and networking technologies appear to be one of the most promising directions for energy saving and environmental protection of fuel cell vehicles (FCVs). In real-world driving situations, EMMs based on driving cycle information are critical for FCVs and have been extensively studied. The collection and processing of driving cycle information is a fundamental and critical work that cannot be separated from sensors, global positioning system (GPS), vehicle-to-vehicle (V2V), vehicle-to-everything (V2X), intelligent transportation system (ITS) and some processing algorithms. However, no reviews have comprehensively summarized the EMMs for FCVs from the perspective of driving cycle information. Motivated by the literature gap, this paper provides a state-of-the-art understanding of EMMs for FCVs from the perspective of driving cycle information, including a detailed description for driving cycle information analysis, and a comprehensive summary of the latest EMMs for FCVs, with a focus on EMMs based on driving pattern recognition (DPR) and driving characteristic prediction (DCP). Based on the above analysis, an in-depth presentation of the highlights and prospects is provided for the realization of high-performance EMMs for FCVs in real-world driving situations. This paper aims at helping the relevant researchers develop suitable and efficient EMMs for FCVs using driving cycle information.