Co-Optimization Scheme for the Powertrain and Exhaust Emission Control System of Hybrid Electric Vehicles Using Future Speed Prediction

Abstract

Hybrid electric vehicles (HEVs) have been an effective solution for improved vehicle fuel efficiency and reduced emission pollution. However, the optimization design for HEVs is complicated due to the presence of nonlinear dynamics and complicated integration of the HEV systems. Moreover, there is a trade-off between fuel optimization and emission reduction. In this paper, a co-optimization scheme is proposed to optimize fuel efficiency for HEVs. The proposed optimization scheme uses obtainable future speed prediction as the basis to optimally tune control parameters for the existing powertrain control system. Moreover, the ramp-up time of the catalyst temperature to reach its light-off level in the exhaust emission system is also considered as an additional optimization constraint to reduce emission. At first, the Toyota Prius Hybrid Simulink model which is an integrated model for a powertrain and exhaust emission system is validated using real data from a number of real driving cycle scenarios. Then, to simplify the formulation of the proposed algorithm, the model employed for the optimization for both powertrain and exhaust emission systems is represented by a set of equivalent neural network (NN) models, which are learned using the data generated from the well-validated Toyota Prius Hybrid Simulink model. Using the NN model, a co-optimization algorithm is established that provides an optimal tuning of some fuel-sensitive powertrain control parameters using future speed prediction, leading to a novel co-optimization algorithm, achieving on average a further 9.22% fuel savings for the Toyota Prius Hybrid Simulink model.