Research on efficient virtual load spectrum extraction method for heavy-duty commercial vehicle chassis based on novel AM-P-sLSTM model

Virtual load spectrum extraction technology significantly enhances simulation efficiency, yet traditional methods suffer from critical limitations in generation efficiency. This study focuses on virtual load spectrum extraction for a heavy-duty truck chassis. First, physical vehicle tests were conducted based on a refined multi-body dynamics (MBD) model, measuring wheel forces, axle accelerations, damper draw-wire displacements, and frame strains at a proving ground. Second, by integrating weight mechanisms, exponential gating strategies, and patch-independent channel mechanisms into a Long Short-Term Memory (LSTM) framework, this paper creatively proposes an efficient and accurate AM-P-sLSTM model. The model demonstrates superior performance compared to six advanced models across five public datasets. Third, the AM-P-sLSTM was trained using MBD-derived draw-wire sensor displacements and axle-head acceleration simulation signals as inputs, with corresponding simulated wheel-center vertical excitation signals as outputs, to extract virtual load spectra for the heavy truck. Experimental results indicate that while the AM-P-sLSTM, traditional Virtual Iteration method, and other advanced models all achieve load spectrum acquisition, the proposed AM-P-sLSTM exhibits significantly enhanced efficiency without compromising accuracy. Compared to the VI method, it improves efficiency by 35.9% and 45.9% under different cutoff frequencies. This technology is expected to substantially shorten structural fatigue validation cycles for commercial vehicles, providing high-fidelity data foundations for intelligent design and lifecycle health management of heavy equipment, thereby advancing the automotive industry toward data-driven, efficient R&D paradigms.