Vehicle Emissions Prediction with Physics-Aware AI Models: Preliminary Results

TL;DR

This paper introduces a physics-aware AI model that improves vehicle emissions prediction accuracy using OBD data, addressing the limitations of existing physics models and demonstrating significant accuracy gains in real-world case studies.

Contribution

It develops a novel AI approach that combines physics-based insights with data-driven methods to enhance vehicle emissions prediction accuracy.

Findings

AI model achieves 65% better accuracy than low-order physics models

AI model is 35% more accurate than baseline models

Method validated on real-world OBD data from public transportation

Abstract

Given an on-board diagnostics (OBD) dataset and a physics-based emissions prediction model, this paper aims to develop an accurate and computational-efficient AI (Artificial Intelligence) method that predicts vehicle emissions. The problem is of societal importance because vehicular emissions lead to climate change and impact human health. This problem is challenging because the OBD data does not contain enough parameters needed by high-order physics models. Conversely, related work has shown that low-order physics models have poor predictive accuracy when using available OBD data. This paper uses a divergent window co-occurrence pattern detection method to develop a spatiotemporal variability-aware AI model for predicting emission values from the OBD datasets. We conducted a case study using real-world OBD data from a local public transportation agency. Results show that the proposed AI method has approximately 65% improved predictive accuracy than a non-AI low-order physics model and is approximately 35% more accurate than a baseline model.