Supervisory Control of Parallel Hybrid Electric Vehicles for Fuel and Emissions Reduction

Past research on Hybrid Electric Vehicles (HEVs) focused primarily on improving their fuel economy. Emission reduction is another important performance attribute that needs to be addressed. When emissions are considered for hybrid vehicles with a gasoline engine, horizon-based optimization methodologies should be used because the lightoff of the three-way catalytic converter heavily depends on the warming-up of catalyst temperature. In this paper, we propose a systematic design method for a cold-start supervisory control algorithm based on the Dynamic Programming (DP) methodology. First, a system-level parallel HEV model is developed to efficiently predict tailpipe emissions as well as fuel economy.The optimal control problem for minimization of cold-start emissions and fuel consumption is then solved via DP. Since DP solution cannot be directly implemented as a real-time controller, more useful control strategies are extracted from DP solution over the entire state space via the comprehensive extraction method. The DP results indicate that the engine on/off, gear-shift, and power-split strategies must be properly adjusted to achieve fast catalyst warm-up with minimal cold-start engine-out emissions. Based on DP results, we proposed a rule-based control algorithm that is easy to implement and achieves nearoptimal fuel economy and emissions performance.