Direct power prediction for energy management of ammonia–hydrogen hybrid heavy-duty vehicles

For heavy-duty commercial vehicles with multi-energy powertrains, the delayed response of key power sources makes predictive energy management difficult. Accurate power demand prediction is therefore essential for improving energy allocation and vehicle efficiency. Conventional methods often rely on vehicle speed forecasting, but multi-step speed prediction can introduce error propagation, which limits their effectiveness under highly dynamic and nonlinear heavy-duty driving conditions. To address this problem, this study develops a driving-cycle dataset for intelligent connected heavy-duty commercial vehicles using a SUMO–MATLAB co-simulation platform. Based on this dataset, a direct power prediction method is proposed using a cross-attention Long Short-Term Memory (LSTM) network. Historical power data are used as the main input channel to describe the vehicle’s intrinsic power evolution, while intelligent transportation information, including Controller Area Network (CAN) data, Vehicle-to-Everything (V2X) data, perception data, and other variables related to the traffic environment and driving behavior, is introduced as an auxiliary channel. The two information streams are fused through cross-attention to improve prediction accuracy. The sparrow search algorithm is further used to optimize the network parameters. Simulation results show that the proposed method reduces the Mean Absolute Error (MAE) by 41.3%. Real-vehicle validation further confirms its effectiveness, with a 74.2% MAE reduction. When applied to the energy management of an ammonia–hydrogen hybrid heavy-duty vehicle, the proposed method reduces fuel consumption by 2.94%.