Bilevel Predictive Control for HEVs Integrating Energy and Cabin Thermal Comfort
Lulu Guo, Baolin Ma, Xun Gong, Yunfeng Hu, Hong Chen
Abstract
This article proposes an integrated energy and cabin thermal management (TM) strategy for a connected hybrid electric vehicle (HEV) that uses a vehicle speed preview informed by traffic connectivity. The proposed strategy uses a bilevel model predictive control framework over long and short predictive horizons to improve fuel economy while ensuring the vehicle’s traction energy and cabin thermal demands are satisfied. The cabin thermal load planning is developed with sensitivity to the vehicle speed of an air conditioning (AC) system, and the proportion of traction energy supplied between the engine and battery is optimized using the Pontryagin minimum principle (PMP). Under standard and on-road driving cycles, the simulation results indicate that over 18% and 22% reduction in fuel consumption can be saved compared with a baseline strategy and an equivalent consumption minimization strategy (ECMS), respectively. Specifically, the electricity consumption of the AC system is reduced by 13% via thermal load planning of the cabin.