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Articles by Bernardo Tormos
Assessing the Compatibility of PAO and POE with Fluoroelastomer (FKM) for Potential EVs Application
3.1. Effects on the Polymer 3.1.1. Mass Change The mass of the FKM specimens was measured before and after immersion in the base fluids to determine the mass change, calculated using Equation (1). For each fluid, six specimens were assessed, and the mean and the standard deviation (SD) were computed. The immersion test results, presented in Table 3, highlight distinct behavioral differences in FKM samples exposed to PAO4 and POE.
Explainable AI Using OBDII Data for Urban Buses Maintenance Management: A Study Case
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
Compatibility Study of Polyamide (PA6) with Lubricant Bases for Electric Vehicle Applications
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
Degradation Effects of Base Oils after Thermal and Electrical Aging for EV Thermal Fluid Applications
1. Introduction The global transition towards decarbonization and sustainability has seen a significant shift towards the electrification of the transport sector. This shift is driven by concerns over climate change and the need to reduce greenhouse gas emissions. Projections show that electric vehicle (EV) sales are growing faster than expected, with forecasts indicating that by 2030, over 30% of new cars sold worldwide will be electric [1].
Machine-Learning-Based Digital Twins for Transient Vehicle Cycles and Their Potential for Predicting Fuel Consumption
This is an early access version, the complete PDF, HTML, and XML versions will be available soon. Open AccessArticle by Eduardo Tomanik 1,*, Antonio J.
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