Anselma, Pier Giuseppe (Politecnico di Torino), Huo, Yi (McMaster University), Roeleveld, Joel (McMaster University), Belingardi, Giovanni (Politecnico di Torino), Emadi, Ali (McMaster University)

From Off-Line to On-Line Control of a Multimode Power Split Hybrid Electric Vehicle Powertrain

Scheduled for presentation during the Regular Session "Advances Control H.E.V & Applications" (MoBT2), Monday, June 24, 2019, 16:50−17:10, Chenonceau

9th IFAC International Symposium on Advances in Automotive Control, June 23-27, 2019, Orléans, France

This information is tentative and subject to change. Compiled on April 18, 2024

Abstract

On-line optimal control represents a crucial issue in the development of multimode power split hybrid electric vehicles (HEVs). Finding a control strategy that guarantees fuel economy optimality and ease of implementation simultaneously still reveal an open research question. This paper aims at developing an on-line control approach for multimode HEVs based on previously implemented offline control. The two control levels for multimode HEVs are presented: the operating mode selection and the torque split determination. The former is addressed adopting a machine learning approach where artificial neural networks (NNs) are trained in supervised learning using offline control data. The torque split is resolved on-line according to efficiency-based maps extracted offline. Simulation results for a specific multimode HEV design demonstrate the effectiveness of the developed control strategy in minimizing the value of predicted fuel consumption. Furthermore, a sensitivity study is conducted for the NN sizing parameters. The ease of implementation and adaptability suggests the potential application of the developed online control approach in a design methodology for multimode HEVs.