Oil & Gas Science and Technology - Revue d'IFP Energies nouvelles

The objective of this work is to develop an optimal management strategy to improve the energetic efficiency of a hybrid electric vehicle. The strategy is built based on an extensive experimental study of mobility in order to allow trips recognition and prediction. For this experimental study, a dedicated autonomous acquisition system was developed. On working days, most trips are constrained and can be predicted with a high level of confidence. The database was built to assess the energy and power needed based on a static model for three types of cars. It was found that most trips could be covered by a 10 kWh battery. Regarding the optimization strategy, a novel real time capable energy management approach based on dynamic vehicle model was created using Energetic Macroscopic Representation. This real time capable energy management strategy is done by a combination of cycle prediction based on results obtained during the experimental study. The optimal control strategy for common cycles based on dynamic programming is available in the database. When a common cycle is detected, the pre-determined optimum strategy is applied to the similar upcoming cycle. If the real cycle differs from the reference cycle, the control strategy is adapted using quadratic programming. To assess the performance of the strategy, its resulting fuel consumption is compared to the global optimum calculated using dynamic programming and used as a reference; its optimality factor is above 98%.

This study presents a semi-empirical modeling approach based on an extensive parametric study using a spark-ignition port-injection engine. The experimental results are used to derive engine-out emission models for each regulated pollutant (CO, HC, NO_x) as a function of engine operating parameters. Such parameters include engine speed, intake manifold pressure, equivalence ratio, and spark advance. The proposed models provide accurate predictions over a large range of engine operating conditions. The adequate accuracy and low computational burden of the models are promising in the context of optimal control theory. Dynamic programming is applied in order to find the best operating parameters that define trade-off between fuel consumption and emissions over driving cycles.