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Hybrid electric vehicles offer significant fuel economy benefits, because battery and fuel can be used as complementing energy sources. This paper presents the use of dynamic programming to find the optimal blend of power sources, leading to the lowest fuel consumption and the lowest level of harmful emissions. It is found that the optimal engine behavior differs substantially to an on-line adaptive control system previously designed for the Lotus Evora 414E. When analyzing the trade-off between emission and fuel consumption, CO and HC emissions show a traditional Pareto curve, whereas NOx emissions show a near linear relationship with a high penalty. These global optimization results are not directly applicable for online control, but they can guide the design of a more efficient hybrid control system.

This paper presents a novel approach to augment existing engine calibrations to deliver improved engine performance during a transient, through the application of multi-objective optimization techniques to the calibration of the Variable Valve Timing (VVT) system of a 1.0 litre gasoline engine. Current mature calibration approaches for the VVT system are predominantly based on steady state techniques which fail to consider the engine dynamic behaviour in real world driving, which is heavily transient. In this study the total integrated fuel consumption and engine-out NOx emissions over a 2-minute segment of the transient Worldwide Light-duty Test Cycle are minimised in a constrained multi-objective optimisation framework to achieve an updated calibration for the VVT control. The cycle segment was identified as an area with high NOx emissions.