Physics-informed attention on temporal fusion transformer for multivariate truck range forecasting

Abstract Accurate real-time forecasting of remaining vehicle range remains a challenge, particularly under varying payloads, road gradients, and environmental conditions. Conventional Temporal Fusion Transformers (TFTs) utilize attention mechanisms to dynamically weight historical inputs but may fail to capture explicit physical relationships that are critical for accurate predictions in heavy-duty electric trucks. This paper introduces a novel approach to integrating physical vehicle dynamics directly into the attention mechanism of TFTs. Our method, Physics-Informed Attention for TFT (PIA-TFT), modifies attention calculation by injecting physics-based relevance scores derived from vehicle speed, payload, road gradients, and other physical parameters, improving interpretability and model accuracy under operational conditions. Empirical evaluations conducted with real-world data from electric trucks demonstrate that the PIA-TFT reduces prediction errors compared to standard TFTs by up to 18%. Our approach is a step towards more physically consistent and explainable deep learning architectures for automotive forecasting tasks.