Neural Network-Based Model Reference Control of Braking Electric Vehicles

Valery Vodovozov, Andrei Aksjonov, Eduard Petlenkov and Zoja Raud
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Valery Vodovozov: Department of Electrical Engineering, Tallinn University of Technology, 19086 Tallinn, Estonia
Andrei Aksjonov: Electrical Engineering and Automation, Aalto University, FI-00076 Aalto, Finland
Eduard Petlenkov: Department of Electrical Engineering, Tallinn University of Technology, 19086 Tallinn, Estonia
Zoja Raud: Department of Electrical Engineering, Tallinn University of Technology, 19086 Tallinn, Estonia

Energies, 2021, vol. 14, issue 9, 1-22

Abstract: The problem of energy recovery in braking of an electric vehicle is solved here, which ensures high quality blended deceleration using electrical and friction brakes. A model reference controller is offered, capable to meet the conflicting requirements of intensive and gradual braking scenarios at changing road surfaces. In this study, the neural network controller provides torque gradient control without a tire model, resulting in the return of maximal energy to the hybrid energy storage during braking. The torque allocation algorithm determines how to share the driver’s request between the friction and electrical brakes in such a way as to enable regeneration for all braking modes, except when the battery state of charge and voltage levels are saturated, and a solo friction brake has to be used. The simulation demonstrates the effectiveness of the proposed coupled two-layer neural network capable of capturing various dynamic behaviors that could not be included in the simplified physics-based model. A comparison of the simulation and experimental results demonstrates that the velocity, slip, and torque responses confirm the proper car performance, while the system successfully copes with the strong nonlinearity and instability of the vehicle dynamics.

Keywords: electric vehicle; model reference controller; neural network; energy recovery; braking (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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