Open AccessNeural Network Control of Green Energy Vehicles with Blended Braking Systems
Author(s) -
Valery Vodovozov,
Eduard Petlenkov,
Andrei Aksjonov,
Zoja Raud
Publication year - 2021
Publication title -
renewable energy and power quality journal
Language(s) - English
Resource type - Journals
ISSN - 2172-038X
DOI - 10.24084/repqj19.291
Subject(s) - artificial neural network , dynamic braking , automotive engineering , controller (irrigation) , torque , computer science , process (computing) , energy (signal processing) , control (management) , retarder , control theory (sociology) , control engineering , engineering , artificial intelligence , agronomy , statistics , physics , mathematics , biology , operating system , thermodynamics
A neural network-based control system is offered, which ensures high quality blended braking of the green energy vehicles in both the intensive and the gradual deceleration scenarios, with energy recovery at the changing road pavement. In this study, a neural network controller provides the torque gradient control without a tire model resulting in returning maximal energy to the hybrid energy source during the braking process. To meet the conflicting requirements of different braking modes and road surfaces, an allocation algorithm determines how to distribute the driver’s torque request between the friction and electrical brakes. Simulation demonstrates effectiveness of the proposed braking system. Model states and inputs are used as a guidance to learn a coupled two-layer neural network capable to capture various dynamic behaviours that could not be included in the simplified physics-based model. An experimental part of the research proves the model and simulation validity.