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

• URL: http://arxiv.org/abs/2607.19054v2
• Date: Thu, 23 Jul 2026 07:57:51 GMT
• Title: Probabilistic Physics-Aware Machine Learning Predictions of Electric Truck Energy Consumption with Field Data
• Abstract summary: 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.<n>Results show that Bayesian linear regression based on this physics-aware model can improve the reliability of the expected energy consumption.<n>More complex machine learning models such as neural networks and gradient boosted regression trees, based on the same physical model, can significantly outperform standard versions of the same machine learning models.
• Score: 9.250157449870558
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• Fuel Consumption Prediction: A Comparative Analysis of Machine Learning Paradigms [0.0]
  This study examines the factors that determine vehicle fuel consumption from the seminal Motor Trend dataset.<n>It identifies the governing physical factors of efficiency through rigorous quantitative analysis.<n>The research finds that intrinsic vehicle efficiency is fundamentally determined by physical design parameters, weight and displacement.
  arXiv  Detail & Related papers  (2026-03-22T03:08:22Z)
• Scientific Knowledge-Guided Machine Learning for Vessel Power Prediction: A Comparative Study [0.0]
  This study introduces a hybrid modeling framework that integrates physics-based knowledge from sea trials with data-driven residual learning.<n>The proposed framework provides a practical and computationally efficient tool for vessel performance monitoring, with applications in weather routing, trim optimization, and energy efficiency planning.
  arXiv  Detail & Related papers  (2026-02-20T18:12:14Z)
• Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling [5.437298646956505]
  Building energy modeling is a key tool for optimizing the performance of building energy systems.<n>Recently, hybrid approaches that combine the strengths of both paradigms have gained attention.
  arXiv  Detail & Related papers  (2025-07-23T14:07:33Z)
• Integrating Physics and Data-Driven Approaches: An Explainable and Uncertainty-Aware Hybrid Model for Wind Turbine Power Prediction [1.1270209626877075]
  The rapid growth of the wind energy sector underscores the urgent need to optimize turbine operations.<n>Traditional empirical and physics-based models offer approximate predictions of power generation based on wind speed.<n>Data-driven machine learning methods present a promising avenue for improving wind turbine modeling.
  arXiv  Detail & Related papers  (2025-02-11T08:16:48Z)
• Can Kans (re)discover predictive models for Direct-Drive Laser Fusion? [11.261403205522694]
  The domain of laser fusion presents a unique and challenging predictive modeling application landscape for machine learning methods. Data-driven approaches have been successful in the past for achieving desired generalization ability and model interpretation that aligns with physics expectations. In this work, we present the use of Kolmogorov-Arnold Networks (KANs) as an alternative to PIL for developing a new type of data-driven predictive model.
  arXiv  Detail & Related papers  (2024-09-13T13:48:06Z)
• Scaling and renormalization in high-dimensional regression [72.59731158970894]
  We present a unifying perspective on recent results on ridge regression.<n>We use the basic tools of random matrix theory and free probability, aimed at readers with backgrounds in physics and deep learning.<n>Our results extend and provide a unifying perspective on earlier models of scaling laws.
  arXiv  Detail & Related papers  (2024-05-01T15:59:00Z)
• Discovering Interpretable Physical Models using Symbolic Regression and Discrete Exterior Calculus [55.2480439325792]
  We propose a framework that combines Symbolic Regression (SR) and Discrete Exterior Calculus (DEC) for the automated discovery of physical models. DEC provides building blocks for the discrete analogue of field theories, which are beyond the state-of-the-art applications of SR to physical problems. We prove the effectiveness of our methodology by re-discovering three models of Continuum Physics from synthetic experimental data.
  arXiv  Detail & Related papers  (2023-10-10T13:23:05Z)
• Prediction of liquid fuel properties using machine learning models with Gaussian processes and probabilistic conditional generative learning [56.67751936864119]
  The present work aims to construct cheap-to-compute machine learning (ML) models to act as closure equations for predicting the physical properties of alternative fuels. Those models can be trained using the database from MD simulations and/or experimental measurements in a data-fusion-fidelity approach. The results show that ML models can predict accurately the fuel properties of a wide range of pressure and temperature conditions.
  arXiv  Detail & Related papers  (2021-10-18T14:43:50Z)
• Hessian-based toolbox for reliable and interpretable machine learning in physics [58.720142291102135]
  We present a toolbox for interpretability and reliability, extrapolation of the model architecture. It provides a notion of the influence of the input data on the prediction at a given test point, an estimation of the uncertainty of the model predictions, and an agnostic score for the model predictions. Our work opens the road to the systematic use of interpretability and reliability methods in ML applied to physics and, more generally, science.
  arXiv  Detail & Related papers  (2021-08-04T16:32:59Z)
• Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future [73.03458424369657]
  In real-time forecasting in public health, data collection is a non-trivial and demanding task. 'Backfill' phenomenon and its effect on model performance has been barely studied in the prior literature. We formulate a novel problem and neural framework Back2Future that aims to refine a given model's predictions in real-time.
  arXiv  Detail & Related papers  (2021-06-08T14:48:20Z)
• Physics-Informed Gaussian Process Regression for Probabilistic States Estimation and Forecasting in Power Grids [67.72249211312723]
  Real-time state estimation and forecasting is critical for efficient operation of power grids. PhI-GPR is presented and used for forecasting and estimating the phase angle, angular speed, and wind mechanical power of a three-generator power grid system. We demonstrate that the proposed PhI-GPR method can accurately forecast and estimate both observed and unobserved states.
  arXiv  Detail & Related papers  (2020-10-09T14:18:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.