Battery Discharge Modeling for Electric Vehicles: A Hybrid Physics-based Residual Learning Approach

TL;DR

This paper introduces a hybrid physics-based residual learning model for EV battery discharge that combines interpretable physics with neural network corrections, achieving high accuracy and physical consistency.

Contribution

It presents a novel hybrid framework that integrates physics-based models with neural networks for improved EV battery discharge prediction.

Findings

Reduces mean absolute percentage error to 0.8%

Outperforms physics-only models in accuracy

Maintains physical interpretability and efficiency

Abstract

The growing integration of electric vehicle (EV) fleets into transportation services and energy systems requires accurate modeling of battery discharge and state-of-charge (SoC) evolution to ensure reliable vehicle operation and grid coordination. Existing approaches face a trade-off between interpretable but simplified physics-based models and data-driven methods that demand large datasets and may lack physical consistency. In this paper, we propose a hybrid physics-based residual learning framework for EV battery discharge modeling. A vehicle dynamics model based on force-balance equations provides an interpretable baseline estimate of energy consumption and SoC evolution, capturing aerodynamic drag, rolling resistance, and regenerative braking. A neural network residual learner then corrects discrepancies caused by complex factors such as traffic conditions and driver behavior. Experimental results on $1,500$ trip scenarios demonstrate that the proposed approach reduces the mean absolute percentage error to approximately $0.8\%$, significantly outperforming physics-only models while preserving physical interpretability and computational efficiency.