Probabilistic Physics-Aware Machine Learning Predictions of Electric Truck Energy Consumption with Field Data

2026-07-21

AI Summary

This study addresses the insufficient accuracy and reliability of energy consumption prediction for electric trucks by proposing a physics-informed modeling framework that integrates first-principles physical knowledge with data-driven techniques. The approach embeds fundamental energy loss mechanisms into machine learning architectures and leverages an ensemble of models—including Bayesian linear regression, neural networks, and gradient-boosted regression trees—to achieve both high-fidelity point predictions and robust uncertainty quantification. Experimental results demonstrate that the proposed method significantly outperforms conventional purely data-driven models in both predictive accuracy and uncertainty estimation, thereby validating the effectiveness and superiority of physics-guided feature modeling for energy consumption forecasting in electric freight transport.

Abstract

In this work, we incorporate first principle physics into the construction of data-driven methods by considering a model that accounts for the different sources of energy losses during vehicle operations. Our results show that Bayesian linear regression based on this physics-aware model can improve the reliability of the expected energy consumption, as compared with standard linear regression. Further, it is shown that more complex machine learning models such as neural networks and gradient boosted regression trees, based on the same physical model, can further improve the accuracy in energy forecasting and significantly outperform standard versions of the same machine learning models. In addition to point predictions of the energy consumption, we develop a framework for estimating the corresponding uncertainty in the form of predicted standard deviation. Our results show that all of the models learn to estimate the uncertainty reasonably well.

Problem

• electric truck
• energy consumption
• uncertainty estimation
• physics-aware modeling
• probabilistic prediction

Innovation

• physics-aware machine learning
• Bayesian linear regression
• energy consumption prediction
• uncertainty quantification
• electric trucks