The design and control of hybrid-electric vehicle (HEV) powertrains presents an optimization problem to balance the trade-off between multiple objectives, such as fuel economy, driv-ability, and emissions. However, current design methodologies do not simultaneously incorporate all of these three considerations into both the sizing and control layers of the optimization problem. This paper first demonstrates that the trade-offs between these objectives can be non-trivial in the HEV control problem. This motivates the need for a systematic design procedure that can take all three objectives into account. To address this need, the paper describes the development of a new and efficient design framework called the Hybrid-Vehicle Design Tool (HVDT), which adopts a bi-level optimization strategy. Efficiency is achieved by introducing a neural-network-based meta-model to predict the performance of the optimal control strategy obtained using Dynamic Programming (DP). To demonstrate the HVDT, a small HEV is designed for the UDDS and HWFET driving cycles separately. Results show that the optimized design can reduce fuel consumption, improve emissions and improve driv-ability when compared to the nominal design obtained using first principle design methodologies. Additionally, compared to using DP directly in the bi-level optimization, using the meta-model reduces the simulation from 238 to 16 days (93%) and from 132 to 16 days (88%) for the UDDS and HWFET cycles, respectively, with an acceptable compromise in the accuracy of predicting the performance of DP.

This content is only available via PDF.
You do not currently have access to this content.