Design Space Exploration for Powertrain Electrification using Gaussian Processes
Design space exploration of hybrid electric vehicles is an important
multi-objective global optimization problem. One of the main
objectives is to minimize fuel consumption while maintaining
satisfactory driveability performance and vehicle cost. The design
problem often includes multiple design
options, including different driveline architectures and component sizes,
where different candidates have various trade-offs between different, in many cases
contradictory, performance
requirements. Thus, there is no global optimum but a set of Pareto-optimal
solutions to be explored. The objective functions can be expensive to evaluate,
due to time-consuming simulations, which requires careful selection of
which candidates to evaluate. A design space exploration algorithm
is proposed for finding the set of Pareto-optimal solutions when the
design search space includes multiple design options. As a case study,
powertrain optimization is performed for a medium-sized series hybrid electric
delivery truck.
Daniel Jung, Qadeer Ahmed and Giorgio Rizzoni
2018
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