Stochastic modeling of mission stops and variable cargo weight for heavy-duty trucks

The vehicle's mass is one of the most influential parameters in determining energy consumption. Indeed, the inertial terms are approximately proportional to the instantaneous power required by the prime mover to sustain or accelerate the vehicle. In particular, heavy-duty trucks used in goods distribution are subject to frequent changes in payload, depending on the specific task or sequence of tasks to be executed. In this context, the variability of the cargo weight is clearly reflected in the energy performance, which may exhibit a relatively large spread compared to the nominal operational conditions. The present paper proposes a stochastic model for mission stops and variable cargo weight for heavy-duty trucks. The model is parametrized using log data acquired during real-world road operations and differentiates between multiple working conditions of the vehicle. The model is formally analyzed concerning the probability distribution and expectation of the cargo weight and the mean number of mission stops. The stochastic description is finally integrated with full driver and vehicle models in a virtual simulation environment, and a comparison is performed against log data to validate the proposed formulation. The comparison shows an encouragingly good agreement with the empirical evidence.