Integrated Predictive Powertrain Control for a Multi-Mode Plug-in Hybrid Electric Vehicle
Department of Mechanical Engineering-Engineering Mechanics
Due to the complexity of a multi-mode Plug-in Hybrid Electric Vehicle (PHEV) powertrain, the energy management strategy of said powertrain is a prime candidate for the application of optimal control methods. This paper presents a predictive control strategy for optimal mode selection and powertrain control for a multi-mode 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 paper also presents the integration of the developed optimal mode control strategy with an optimal powersplit strategy using Nonlinear Model Predictive Control (NMPC) to create a real-time Integrated Predictive Powertrain Controller (IPPC) responsible for all aspects of multi-mode PHEV powertrain supervisory control. The IPPC provides a real-time optimal solution to address the major challenge of a multi-mode 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.
IEEE/ASME Transactions on Mechatronics
Integrated Predictive Powertrain Control for a Multi-Mode Plug-in Hybrid Electric Vehicle.
IEEE/ASME Transactions on Mechatronics.
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/14735