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In Hybrid Electric Vehicles (HEV), the electrical hybridization offers different ways to reduce the fuel consumption: kinetic energy recuperation during vehicle deceleration, possibility of stopping the engine, and intelligent Energy Management System (EMS). Besides, with the future more stringent standards, there is a need to integrate the pollutant emissions in the EMS, since strictly reducing the fuel consumption can increase the emissions. The paper presents an optimal energy management strategy with constraints on pollutant emissions for gasoline-HEV, taking into account the 3-Way Catalyst Converter (3WCC). Based on a complete model of the powertrain, a mixed fuel consumption / pollutant emissions performance index is minimized with the Pontryaguin Minimum Principle (PMP) and two states, the battery State Of Charge and the 3WCC temperature.

Nowadays, (engine) downsizing using turbocharging appears as a major way for reducing fuel consumption. With this aim in view, the air actuators (throttle, Turbo WasteGate) control is needed for an efficient engine torque control especially to reduce pumping losses and to increase efficiency. This work proposes Nonlinear Model Predictive Control (NMPC) of the air actuators for turbocharged SI engines where the predictions are achieved by a neural model. The results obtained from a test bench of a Smart MCC engine show the real time applicability of the proposed method based on on-line linearization and the good control performances (good tracking, no overshoot) for various engine speeds.