Multi-level integrated energy and emission management for HEVs cold start operation

Low ambient temperatures, cold starts and city drives interrupted by still stand phases represent major challenges for energy and emission management of hybrid-electric vehicles (HEVs). Large time constants of battery and thermal systems, require long horizons to optimize overall system performance and avoid constraint violations, such as battery energy or real-world emission targets. In this work, a local online controller is extended with a coarse global optimization to predict optimal state trajectories. Manageable computational demand of this upper level optimization is achieved by a combination of model approximations, dimension reduction and coarse sampling. An online adaptation mechanism is implemented for the low-level controller to deal with imprecise predictions and system uncertainty. The proposed multi-level control strategy provides performance within 5% of the global optimum for 100 real world driving cycles using a constant parametrization without cycle-specific tuning.