Integrated Predictive Powertrain Control for a Multimode Plug-in Hybrid Electric Vehicle

Due to the complexity of a multimode plug-in hybrid electric vehicle (PHEV) powertrain, the energy management strategy of the said powertrain is a prime candidate for the application of optimal control methods. This article presents a predictive control strategy for optimal mode selection and powertrain control for a multimode PHEV capable of real-time control. This method utilizes predictions of future vehicle behavior in order to plan an optimal path of vehicle powertrain modes that minimizes energy consumption. This article also presents the integration of the developed optimal mode control strategy with an optimal powersplit strategy using nonlinear model predictive control to create a real-time integrated predictive powertrain controller (IPPC) responsible for all aspects of multimode PHEV powertrain supervisory control. The IPPC provides a real-time optimal solution to address the major challenge of a multimode HEV powertrain control: an integrated discrete and continuous optimization. Testing in simulation has shown the IPPC to be capable of reducing PHEV energy consumption by 4%-10% across real-world and standard drive cycles. In addition, the presented IPPC was deployed onto a rapid prototyping embedded controller where on-road, real-time testing has shown the IPPC to be capable of providing an energy reduction of 5%, thus confirming the energy savings observed in simulation.

Automated summary

This study presents a predictive control strategy for optimal mode selection and powertrain control of a multimode PHEV, which reduces energy consumption by planning an optimal path of vehicle powertrain modes based on predictions of future vehicle behavior. By integrating optimal control methods, it achieves comprehensive supervision of multimode PHEV powertrain, successfully reducing energy consumption.