Context and Driver Dependent Hybrid Electrical Vehicle Operation⁎This work was supported in part funded by National Secretariat of Higher Education, Science, Technology and Innovation of Ecuador (SENESCYT), Ghent University Special Research Fund, project MIMOPREC-Starting grant 2018 and Flanders Make-CONACON project nr HBC.2018.0235. Issue 5 (2020)

• Record Type: Journal Article
• Authors: Cajo, Ricardo; Bengea, Sorin; Beck, Rian; Dwek, Nathan; Meskens, Joram; Venkatesan, Sudarsan K.; Plaza, Douglas; Keyser, Robin De; Ionescu, Clara M.
• Is Part Of: IFAC-PapersOnLine. Volume 53:Issue 5(2020)
• Page Start: 768
• Page End: 773
• Publication Date: 2020
• Subjects: Context aware control -- Context estimation -- Machine learning -- Human-in-the-loop control -- Cyber physical systems -- Model predictive control (MPC) -- Self-optimization; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2021.04.171
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: This paper studies the driver and context changes during the operation of a hybrid electric vehicle (HEV) and their influence on fuel consumption. Firstly, a context estimation model to recognize driving styles is developed based on machine learning techniques, for which a realistic scenario with simulation of urban mobility (SUMO) and car modeling platform (IPG Carmaker) integration is designed. Secondly, a novel context-aware control strategy based on model predictive control with extended prediction self-adaptive control (MPC-EPSAC) strategy is proposed. The control objective is to achieve optimal torque-split distribution, while optimizing fuel consumption in the parallel HEV. The simulation results suggest that an improvement in fuel economy can be achieved when the driving style in the control loop is adequately considered.