2018-01-1027 Published 0 3 Apr 2018 © 2018 SAE International. All Rights Reserved.Introduction Reducing fuel consumption and emissions is an impor - tant problem in the automotive industry and HEV are essential to solve this. HEV with various electrified powertrain architectures have been proposed, for example, different types of series- and parallel hybrids, which have their own advantages. Optimizing the design of the electrified powertrain is often performed based on multiple performance objectives, for example fuel consumption, component cost, battery size, and drivability. Many of the objectives are contra - dictory meaning that one candidate is never optimal with respect to all objectives, but instead there are different trade-offs between different powertrain candidates [1 ]. When comparing different segments of the transporta - tion industry, for example, public transports or different sizes of trucks for parcel delivery, it is clear that one powertrain may not be optimal in all situations. Selecting electrified powertrain architecture and component sizes is an important task to find the optimal trade-off between fuel consumption and other performance objectives, for each specific applica - tion. The problem of finding the optimal powertrain configu - ration is referred to as design space exploration which is often formulated as a Multi-Objective Optimization Problem (MOOP). Design space exploration is, in general, a non-convex optimization problem where the design space grows exponen - tially with the number of components that are optimized. Fuel consumption can be evaluated by simulating or opti - mizing the specific powertrain for a given set of driving scenarios representing realistic driving missions. Therefore, evaluating the performance of each powertrain can be time-consuming. In these situations, selecting a suitable search algorithm is important. A design space exploration algorithm is proposed which uses Gaussian Processes to select the powertrain candidate, in each iteration, that is most likely to be Pareto-optimal. In this work, the powertrain design of a medium-sized delivery truck is considered, see Figure 1 . The truck has a series hybrid powertrain with an internal combustion engine as range extender where different powertrain architectures are explored. The powertrain is optimized for different driving cycles to analyze how the driving mission affects the selected powertrain. A simulation model of the powertrain is devel - oped and Dynamic Programming is used to compute the optimal control strategies and fuel consumption for each architecture.Abstract Hybrid electric vehicles (HEV) are essential for reducing fuel consumption and emissions. However, when analyzing different segments of the transportation industry, for example, public transportation or different sizes of delivery trucks and how the HEV are used, it is clear that one powertrain may not be optimal in all situations. Choosing a hybrid powertrain architecture and proper component sizes for different applications is an important task to find the optimal trade-off between fuel economy, drivability, and vehicle cost. However, exploring and evaluating all possible architectures and component sizes is a time-consuming task. A search algorithm, using Gaussian Processes, is proposed that simultaneously explores multiple architecture options, to identify the Pareto-optimal solutions. The search algorithm is designed to carefully select the candidate in each iteration which is most likely to be Pareto-optimal, based on the results from previous candidates, to reduce computational time. The powertrain of a medium-sized series plugin hybrid electric delivery truck with a range extender is optimized for different driving missions. Three different powertrain architectures are included in the design space exploration and the fuel economy is evaluated using a simulation model of the powertrain and Dynamic Programming. Results from the analysis show