Performance optimization of M1 light-duty vehicles through dynamic and energetic behavior monitoring
Abstract
This study aimed to develop an integrated system for monitoring and analyzing the dynamic and energy behavior of M1 category light vehicles. The research focused on improving the design of propulsion systems and implementing electric mobility strategies, seeking to optimize vehicle performance and reduce energy consumption and polluting emissions. A descriptive and exploratory research design was used that combined quantitative and qualitative methods. M1 light vehicles, compatible with the CAN protocol, were selected, prioritizing modern electric and hybrid models. Data collection was carried out with advanced technologies, such as the CANmod.gps module and SavvyCAN software, which allowed real-time monitoring of key parameters of vehicle behavior. The results showed that these technologies facilitated the identification of inefficient driving patterns, thus optimizing fleet management. It was shown that better operational control not only improved energy efficiency, but also contributed to emissions reduction. Furthermore, data analysis enabled early detection of operational failures, which in turn improved electric mobility systems. Further exploration of new monitoring methodologies and technologies was recommended, as well as addressing ethical and cybersecurity issues in data management, opening up new opportunities for research in electric mobility and vehicle engineering.
Keywords: Light vehicles, Energy efficiency, Pollutant emissions, Sustainable mobility, Data analysis.
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