Impact of Energy Management Systems on the Efficiency of Electrical Testing Equipment in Automotive Research

Abstract

The increasing demand for sustainable energy solutions has led to a shift toward energy-efficient testing methodologies in industrial and research facilities. This study focuses on optimizing electrical energy management in automotive fuel testing laboratories, ensuring minimal energy wastage and cost-effectiveness. A major research gap exists in improving power consumption efficiency in engine testing setups, particularly in facilities using dynamometers and emission analyzers. The study aims to assess the energy usage patterns in a Kirloskar Eddy Current Dynamometer-based fuel testing lab and propose an energy management strategy using IoT-enabled monitoring systems and renewable energy integration. Using power metering sensors, AI-based predictive analytics, and an automated demand-response system, the study monitors power fluctuations, equipment loads, and peak energy demands. The results indicate that implementing an AI-driven Energy Management System (EMS) reduces energy wastage by 15% and optimizes power factor correction by 10%, improving the efficiency of electrical components. Additionally, renewable energy supplementation (solar-based) provides up to 20% energy savings, reducing reliance on grid power. These findings demonstrate the feasibility of smart electrical load management in industrial testing facilities and highlight the potential of AI and IoT-based automation in reducing energy costs. Future research should explore real-time optimization of energy grids in industrial applications. Major Findings: The study found that DB10 biodiesel blend improved power output by up to 7.2% and reduced CO emissions by up to 25% compared to DB5. Specific fuel consumption decreased by 50% at higher loads, while NOx emissions increased proportionally with biodiesel content and engine speed. AI-based energy management also achieved 15% energy savings and 10% power factor correction.