A method to build energy-metric-optimal (EMO) classification systems for road transport missions

Paper in proceeding, 2023

Conventionally, the optimal design and selection processes of heavy-duty trucks are supported by the internal classification systems adopted by original equipment manufacturers (OEMs), which classify the energy usage of road vehicles based on the characteristics of the transport mission. These may include, for example, road properties like topography, mean legal speed and curviness, but also weather and traffic conditions. In this context, the definition of classes and thresholds in use by OEMs is however based on heuristics rather than a rigorous scientific approach. As a consequence, there is often no guarantee that vehicles optimally designed with respect to certain combinations of classes will exhibit robustness in energy performance when operated differently from the nominal conditions. This limitation might be overcome by optimally specifying classes and thresholds depending on a suitable energy metric. Therefore, this paper proposes a scientific method to build an energy-metric-optimal (EMO) classification system for road transport missions using statistical models to describe the operating environment. The problem is formulated in a multi-objective optimization form, where the vector-valued function to be minimized collects the total variation of a mean energy function over the considered intervals. The procedure is applied to the stochastic road models of the operating cycle (OC) description, yielding the optimal thresholds for a predefined number of classes. These are finally compared to those in use at Volvo and Scania, respectively.