Autonomous Electric-Vehicle Control Using Speed Planning Algorithm and Back-Stepping Approach

Sofiane Bacha, Ramzi Saadi (), Mohamed Yacine Ayad, Mohamed Sahraoui, Khaled Laadjal () and Antonio J. Marques Cardoso ()
Additional contact information
Sofiane Bacha: MSE Laboratory, Department of Electrical Engineering, Mohamed Khider University, Biskra 7000, Algeria
Ramzi Saadi: MSE Laboratory, Department of Electrical Engineering, Mohamed Khider University, Biskra 7000, Algeria
Mohamed Yacine Ayad: Industrial Hybrid Vehicle Applications, 75000 Paris, France
Mohamed Sahraoui: MSE Laboratory, Department of Electrical Engineering, Mohamed Khider University, Biskra 7000, Algeria
Khaled Laadjal: CISE—Electromechatronic Systems Research Centre, University of Beira Interior, Calçada Fonte do Lameiro, P-6201-001 Covilhã, Portugal
Antonio J. Marques Cardoso: CISE—Electromechatronic Systems Research Centre, University of Beira Interior, Calçada Fonte do Lameiro, P-6201-001 Covilhã, Portugal

Energies, 2023, vol. 16, issue 5, 1-26

Abstract: Autonomous electric vehicles (AEVs) have garnered increasing attention in recent years as they hold significant promise for transforming the transportation sector. However, despite advances in the field, effective vehicle drive control remains a critical challenge that must be addressed to realize the full potential of AEVs. This study presents a novel approach to AEV drive control for concurrently generating a suitable speed profile and controlling the vehicle drive speed along a planned path that takes into account various driving circumstances that mimic real-world driving. The designed strategy is divided into two parts: The first part presents a proposed speed planning algorithm (SPA) that works on developing an adequate speed profile for vehicle navigation; first, the algorithm uses an approach for identifying sharp curves on the predefined trajectory; secondly, based on the dynamic properties of these curves, it generates an appropriate speed profile to ensure smooth vehicle travel across the entire trajectory with varying velocities. The second part proposes a new back-stepping control technique with a space vector modulation (SVM) strategy to control the speed of an induction motor (IM) as a traction part of the AEV. A load torque observer has been designed to enhance the speed-tracking task, while the system stability has been proven using Lyapunov theory. Through a series of experiments and simulations using MATLAB/Simulink software and the dSPACE 1104 real-time interface, we demonstrate the effectiveness of the SPA combined with the back-stepping control technique and highlight its potential to advance the field of AEV technology. Our findings have important implications for the design and implementation of AEVs and provide a foundation for future research in this exciting area of study.

Keywords: speed planning; curve identification; autonomous electric vehicle; induction motor; back-stepping control; space vector modulation (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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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