Physics-Informed Loss Function for Robust Electric Truck Range Estimation

Accurately estimating the driving range of electric trucks remains a critical challenge due to higher complexity and operational diversity compared to passenger EVs. Factors such as variable payloads, aerodynamic variability from different trailers, and auxiliary power demands significantly impact energy usage. Conventional data-driven models, while effective, frequently struggle to generalize under extreme conditions. This paper introduces a novel Physics-Informed Neural Network (PINN) framework for electric truck range estimation that combines physics-based modeling with data-driven learning. A custom loss function dynamically balances physical constraints and empirical insights using a time-varying parameter. The model prioritizes physics early in training, and then shifts toward data-driven fine-tuning. Experimental results demonstrate that while pure data-driven models achieve higher accuracy under normal conditions, the proposed PINN framework demonstrates superior robustness, particularly with small datasets and in challenging weather and operational scenarios. These findings highlight the potential of PINN - based methods in addressing key challenges in electric truck range estimation.