Browse Topic: Fuels and Energy Sources
With the growing energy crisis, people urgently need green energy sources to replace fossil ones. As a zero-emission clean energy source, the proton-exchange membrane fuel cell (PEMFC) has received growing attention from researchers due to its broad practical application. However, the large-scale application of PEMFC is currently impeded by their unsatisfying power output and high cost. PEMFC is composed of multiple components, among which the catalyst layer significantly affects the output power and cost of PEMFC. Drastically reducing the amount of platinum in the catalyst layer can bring great benefits to PEMFC, yet causing the large voltage loss associated with enlarged local oxygen molecule transport. Cutting down the platinum content in the catalyst layer can yield substantial cost savings for PEMFC. Developing an efficient catalyst possessing enhanced oxygen reduction reaction (ORR) catalytic performance is conducive to the commercialization of low-Pt proton exchange membrane
As automotive technology advances, the need for comprehensive environmental awareness becomes increasingly critical for vehicle safety and efficiency. This study introduces a novel integrated wind, weather, and motion sensor designed for moving objects, with a focus on automotive applications. The sensor’s potential to enhance vehicle performance by providing real-time data on local atmospheric conditions is investigated. The research employs a combination of sensor design, vehicle integration, and field-testing methodologies. Findings prove the sensor’s capability to accurately capture dynamic environmental parameters, including wind speed and direction, temperature, and humidity. The integration of this sensor system shows promise in improving vehicle stability, optimizing fuel efficiency through adaptive aerodynamics, and enhancing the performance of autonomous driving systems. Furthermore, the study explores the potential of this technology in contributing to connected vehicle
Drivers present diverse landscapes with their distinct personalities, preferences, and driving habits influenced by many factors. Though drivers' behavior is highly variable, they can exhibit clear patterns that make sorting them into one category or another possible. Discrete segmentation provides an effective way to categorize and address the differences in driving style. The segmentation approach offers many benefits, including simplification, measurement, proven methodology, customization, and safety. Numerous studies have investigated driving style classification using real-world vehicle data. These studies employed various methods to identify and categorize distinct driving patterns, including naturalist differences in driving and field operational tests. This paper presents a novel hybrid approach for segmenting driver behavior based on their driving patterns. We leverage vehicle acceleration data to create granular driver segments by combining event and trip-based methodologies
The deployment of PEM fuel cell systems is becoming an increasingly pivotal aspect of the electrification of the transport sector, particularly in the context of heavy-duty vehicles. One of the principal constraints to market penetration is durability of the fuel cell which hardly meets the expected targets set by the vehicle manufacturers and regulatory bodies. Over the years, researchers and companies have faced the challenge of developing reliable diagnostic and condition monitoring tools to prevent early degradation and efficiency losses of fuel cell stack. The diagnostic tools for fuel cell rely usually on model-based, data driven and hybrid approaches. Most of these are mainly developed for stationary and offline applications, with a lack of suitable methods for real-time and vehicle applications. The work presented is divided into two parts: the first part explores the main degradation conditions for a PEMFC and characteristics, advantages, and application limits of the main
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