This proposal formulates a real time implementable energy management strategy (EMS) for plug-in hybrid electric vehicles (PHEV). The artificial intelligence (AI) techniques employ for modelling and optimization the PHEV control parameters to minimize vehicle energy consumption. Three different levels of system upgrading will happen: A primary physical model to generate the initial control rules, upgrade the rules using a hybrid rig and finally optimizing the control setting by communication between PHEVs and a central cloud. The control rules generated in the first step will be uploaded on a programable EMS. A wireless communication system implements between the EMS and a Cloud-based model. The system verification will happen using a PHEV hardware-in-the-loop (HiL) system. Finally, in a real driving cycle the online calibration of a PHEV will be done to validate the system for online optimization based on the cycle, traffic and driver pattern recognition.
Associate Professor at Chalmers, Mechanics and Maritime Sciences, Combustion
at Chalmers, Mechanics and Maritime Sciences, Combustion
Funding Chalmers participation during 2018
Areas of Advance