S may be obtained from corresponding author. Acknowledgments: The authors would prefer to acknowledge all

S may be obtained from corresponding author. Acknowledgments: The authors would prefer to acknowledge all of the interviewees who kindly donated their valuable time for you to assist create 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 1,2, , 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 to get a hybrid electric car (HEV) in parallel configuration, including a main electrical driving motor (EM), an internal combustion engine (ICE), and also a starter/generator motor. The modelling equations of the HEV consist of car acceleration and jerk, so that simulations can investigate the automobile drivability and comfortability with ML-SA1 Purity distinctive handle parameters. A model predictive handle (MPC) scheme with softened constraints for this HEV is developed. The new MPC with softened constraints shows its superiority over the MPC with challenging constraints since it offers a more quickly setpoint tracking and smoother clutch engagement. The conversion of some tough constraints into softened constraints can strengthen the MPC stability and robustness. The MPC with softened constraints can retain the system stability, though the MPC with difficult constraints becomes unstable if some input constraints bring about the violation of output constraints. Keywords and phrases: model predictive control; parallel hybrid electric automobile; really hard constraints; softened constraints; quick clutch engagement; drivability and comfortability; tracking speed and torqueCitation: Vu, T.M.; Moezzi, R.; Cyrus, J.; Hlava, J.; Petru, M. Parallel Hybrid Electric Automobile Modelling and Model Predictive Handle. Appl. Sci. 2021, 11, 10668. https://doi.org/10.3390/ app112210668 Academic Editor: Andreas Sumper Received: 22 September 2021 Icosabutate Icosabutate Biological Activity Accepted: 9 November 2021 Published: 12 November1. Introduction Controllers for HEVs powertrains and speeds may be included model-free or modelbased. Model-free controllers are largely used with heuristic, fuzzy, neuro, AI, or human virtual and augmented reality. The usage of model-free approaches will be presented in the next part of this study. Model-based controllers could be made use of having a traditional adaptive PID, H2 , H , or sliding mode. Nevertheless, all traditional control solutions cannot involve the real-time dynamic constraints in the vehicle physical limits, the surrounding obstacles, and also the environment (road and weather) circumstances. Hence, a MPC with horizon state and open loop control prediction subject to dynamic constraints are mainly employed to handle as real-time the HEV speeds and torques. As a consequence of the limit size of this paper, we’ve reviewed a few of the most recent analysis of MPC applications for HEVs. Within this paper.