Análise e modelagem das emissões instantâneas de co2 em veículos de combustão interna por meio de dados OBD-II e redes neurais artificiais

Abstract

This study develops a methodology to evaluate and model vehicle carbon dioxide (CO2) emissions using data collected via the OBD-II interface, integrating detailed automotive analysis with artificial neural network techniques. The overall goal is to compare the emission behavior of internal combustion engine vehicles with electric vehicles under real operating conditions. Relevant operational data (speed, throttle position, air flow, etc.) were gathered directly from vehicles through OBD-II and then explored to identify patterns and correlations (such as the strong relationship between engine load and CO2 generation). Predictive models using Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM) neural networks were trained to estimate instantaneous CO2 emissions. The LSTM model achieved the best performance with a mean absolute percentage error (MAPE) of around 5%, significantly outperforming the MLP (MAPE 16%). The comparative analysis indicates that electric vehicles eliminate direct emissions during use, but their total CO2 impact depends on the electrical grid mix and manufacturing emissions. In conclusion, the proposed approach - combining OBD-II data and neural networks - proved effective for real-time emission estimation and can complement standardized lab-based estimates.