Multi-criterion optimization of heavy-duty powertrain design for the evaluation of transport efficiency and costs

The commercial vehicle industry faces new challenges, such as the upcoming carbon-dioxide (CO2) targets in Europe and a tightened regulatory program in the U.S. to reduce greenhouse gas emissions (GHG). In addition, increasing costs and globally rising transport volumes are increasing the pressure on vehicle manufacturers to find the optimum fuel and powertrain setup for heavy-duty (HD) vehicles. This paper presents a new approach to optimizing HD truck powertrains with diesel, natural-gas, diesel-electric and natural-gas-electric engines. The contradiction between maximizing the transport efficiency and minimizing the Total Cost of Ownership (TCO) leads to a method of solving multi-objective problems (MOPs) using evolutionary algorithms (EAs). The use of EAs to solve MOPs is mainly motivated by the population-based method of EAs, which allows several elements of the Pareto optimal set to be generated in a single run. The basic algorithm adopted for this purpose is K. Deb's NSGA-II. In order to optimize the vehicle drivetrain, not only are the sizes of the combustion engine, electric motor and the battery allowed to vary, but different component models are also considered, including different gearboxes, operating strategies and battery cell types. Several constraints are applied in the algorithm, such as climbing ability, minimum engine power, and battery charge rates. The designed vehicles are based on real engine maps and are simulated with real-life measured driving cycles, including autobahn and federal road elements. The results were validated with the simulation software AVL Cruise and Dyna4. The objective of the developed methodology is to present the best possible solution with respect to transport efficiency and TCO.