AI-Based Carbon Emissions Monitoring for Electric Vehicles: A Technical Review

Abstract

Electric Vehicles (EVs) have been identified as a vital part of global transport emissions reductions. Current methods of calculating an electric vehicle's (EV's) carbon footprint do not allow this data to be gathered and analyzed close to real-time. They cannot capture operational variations across vehicle lifecycles effectively. Artificial intelligence offers new possibilities for continuous emissions monitoring. Machine learning processes data from industrial sensors, vehicle systems, and recycling facilities. This integration creates dynamic carbon footprint calculations that reflect actual conditions. Real-time grid data combined with vehicle energy consumption provides accurate emission profiles. Neural networks automatically identify emission hotspots and unusual patterns. The platform supports interventions targeting manufacturing processes, charging behaviors, and maintenance. Predictive modeling techniques enable forecasts of component failure and recommend interventions to extend component life. Smart Charging shifts electricity demand to periods when the electrical grid emits lower emissions. Route optimization accounts for terrain, traffic, and weather to minimize consumption. The convergence of AI with lifecycle assessment enables evidence-based emission reduction decisions. This advancement transforms environmental monitoring and supports sustainable transportation transitions.