Energy Consumption Monitoring and Intelligent Analysis Technology under Dynamic Working Conditions of Vehicles

Abstract

This article deeply studies the energy consumption monitoring and intelligent analysis technology of vehicles under dynamic working conditions, aiming to improve the energy management level of vehicles under different driving conditions. The research focuses on the complex impact mechanism of dynamic operating conditions on energy consumption, especially the differences in energy consumption performance under typical operating conditions such as urban driving and high-speed driving. By proposing an energy consumption monitoring technology based on multi-sensor fusion, combined with high-precision real-time monitoring and intelligent analysis models, the effectiveness of the system in terms of real-time performance, accuracy, and environmental adaptability was experimentally verified. The system utilizes big data and artificial intelligence technology to construct an energy consumption prediction model and anomaly detection method. Research shows that the energy-saving rate under urban driving conditions can reach 15%, while the energy-saving rate under high-speed driving is 8%. Through practical application cases, it has been verified that the system can effectively reduce vehicle exhaust emissions and fuel consumption, with significant energy-saving effects, providing feasible solutions and important reference basis for automotive energy management.