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Optimal Predictive Torque Distribution Control System to Enhance Stability and Energy Efficiency in Electric Vehicles

Author

Listed:
  • Arash Mousaei

    (Department of Computer and Electrical Engineering, University of Tabriz, Tabriz 51666-16471, Iran)

  • Yahya Naderi

    (Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XQ, UK)

Abstract

This article presents a novel approach to address the critical issues of stable rotation and energy efficiency in electric vehicles (EVs). To achieve these objectives, we propose a comprehensive control system that leverages the power of optimization through optimal predictive control methods. The central idea revolves around minimizing the predicted tracking error for future time steps by intelligently determining control inputs. In this innovative approach, we emphasize the dynamic adjustment of weight coefficients and optimization of wheel torque to strike a delicate balance between energy consumption and enhanced vehicle stability. The result is an adept controller that not only ensures vehicle stability but also significantly reduces energy consumption. Given the inherent limitations of electric motors, especially in terms of torque during vehicle operation, and the growing importance of energy conservation, our method tailors weight coefficients to generate optimal wheel torque. This ensures that the electric motors operate within their power range, thereby minimizing energy consumption and extending the overall efficiency of EVs. The combination of stable rotation and energy efficiency offered by this control system represents a promising step forward in the realm of electric vehicles, making them more sustainable and environmentally friendly while maintaining the high standards of performance and safety that consumers expect.

Suggested Citation

  • Arash Mousaei & Yahya Naderi, 2023. "Optimal Predictive Torque Distribution Control System to Enhance Stability and Energy Efficiency in Electric Vehicles," Sustainability, MDPI, vol. 15(20), pages 1-19, October.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:20:p:15155-:d:1265263
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