S may be obtained from corresponding author. Acknowledgments: The authors would prefer to acknowledge all the interviewees who kindly donated their worthwhile time for you to help develop the survey, namely Monica Zajler, Luciano, Edna, Maroia Regina Mendes Nogueira, Ana Rita Avila Nossack, Wilson Gonzaga dos Santos, Joao Sorriso, Adriana, Lucas Muzzi, Ribens do Monte Lima Silva Scatolino, Pedro Goncalves Gomes, Roberta, Joao Paulo, Marcel, Valnei Josde Melo. Conflicts of Interest: The authors declare no conflict of interest.
applied sciencesArticleParallel Hybrid Electric Automobile Modelling and Model Predictive ControlTrieu Minh Vu 1 , Reza Moezzi 1,two, , Jindrich Cyrus 1 , Jaroslav Hlavaand Michal PetruInstitute for Nanomaterials, Sophisticated Technologies and Innovation, Technical University of Liberec, 460 01 Liberec, Czech Republic; [email protected] (T.M.V.); [email protected] (J.C.); [email protected] (M.P.) Faculty of Mechatronics, Informatics and Interdisciplinary Studies, Technical University of Liberec, 460 01 Liberec, Czech Republic; [email protected] Correspondence: [email protected]: This paper presents the modelling and calculations for a hybrid electric car (HEV) in parallel configuration, which includes a primary electrical driving motor (EM), an internal combustion engine (ICE), and also a starter/generator motor. The modelling equations with the HEV involve automobile acceleration and jerk, to ensure that simulations can investigate the car drivability and comfortability with diverse manage parameters. A model predictive manage (MPC) scheme with softened constraints for this HEV is developed. The new MPC with softened constraints shows its superiority over the MPC with difficult constraints since it provides a faster setpoint tracking and smoother clutch engagement. The conversion of some tough constraints into softened constraints can boost the MPC stability and robustness. The MPC with softened constraints can sustain the method stability, though the MPC with hard constraints becomes unstable if some input constraints lead to the violation of output constraints. Search phrases: model predictive manage; parallel hybrid electric car; really hard constraints; softened constraints; quickly clutch engagement; drivability and comfortability; tracking speed and torqueCitation: Vu, T.M.; Moezzi, R.; Cyrus, J.; Hlava, J.; Petru, M. Parallel Hybrid Electric Car Modelling and Model Predictive Handle. Appl. Sci. 2021, 11, 10668. https://doi.org/10.3390/ app112210668 Academic Editor: Andreas Sumper Received: 22 September 2021 Accepted: 9 November 2021 Published: 12 November1. Introduction Controllers for HEVs powertrains and speeds is often integrated model-free or modelbased. Model-free controllers are mainly used with heuristic, fuzzy, neuro, AI, or human virtual and augmented reality. The use of model-free GS-626510 Autophagy approaches might be presented in the subsequent aspect of this study. Model-based controllers might be used having a traditional adaptive PID, H2 , H , or sliding mode. Nonetheless, all standard manage procedures can’t involve the real-time dynamic constraints on the automobile BMS-986094 Autophagy physical limits, the surrounding obstacles, plus the atmosphere (road and weather) circumstances. Thus, a MPC with horizon state and open loop handle prediction topic to dynamic constraints are mainly employed to manage as real-time the HEV speeds and torques. As a consequence of the limit size of this paper, we’ve got reviewed a few of one of the most recent study of MPC applications for HEVs. In this paper.