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A Computationally Efficient Robust Model Predictive Control Framework for Ecological Adaptive Cruise Control Strategy of Electric Vehicles

Sheng Yu, Xiao Pan, Anastasis Georgiou, Boli Chen, Imad M. Jaimoukha, Simos A. Evangelou

TL;DR

This work addresses a car-following task, where the feedback linearisation method is combined with a robust model predictive control scheme to safely, optimally and efficiently control a connected electric vehicle.

Abstract

The recent advancement in vehicular networking technology provides novel solutions for designing intelligent and sustainable vehicle motion controllers. This work addresses a car-following task, where the feedback linearisation method is combined with a robust model predictive control (RMPC) scheme to safely, optimally and efficiently control a connected electric vehicle. In particular, the nonlinear dynamics are linearised through a feedback linearisation method to maintain an efficient computational speed and to guarantee global optimality. At the same time, the inevitable model mismatch is dealt with by the RMPC design. The control objective of the RMPC is to optimise the electric energy efficiency of the ego vehicle with consideration of a bounded model mismatch disturbance subject to satisfaction of physical and safety constraints. Numerical results first verify the validity and robustness through a comparison between the proposed RMPC and a nominal MPC. Further investigation into the performance of the proposed method reveals a higher energy efficiency and passenger comfort level as compared to a recently proposed benchmark method using the space-domain modelling approach.

A Computationally Efficient Robust Model Predictive Control Framework for Ecological Adaptive Cruise Control Strategy of Electric Vehicles

TL;DR

This work addresses a car-following task, where the feedback linearisation method is combined with a robust model predictive control scheme to safely, optimally and efficiently control a connected electric vehicle.

Abstract

The recent advancement in vehicular networking technology provides novel solutions for designing intelligent and sustainable vehicle motion controllers. This work addresses a car-following task, where the feedback linearisation method is combined with a robust model predictive control (RMPC) scheme to safely, optimally and efficiently control a connected electric vehicle. In particular, the nonlinear dynamics are linearised through a feedback linearisation method to maintain an efficient computational speed and to guarantee global optimality. At the same time, the inevitable model mismatch is dealt with by the RMPC design. The control objective of the RMPC is to optimise the electric energy efficiency of the ego vehicle with consideration of a bounded model mismatch disturbance subject to satisfaction of physical and safety constraints. Numerical results first verify the validity and robustness through a comparison between the proposed RMPC and a nominal MPC. Further investigation into the performance of the proposed method reveals a higher energy efficiency and passenger comfort level as compared to a recently proposed benchmark method using the space-domain modelling approach.
Paper Structure (10 sections, 39 equations, 5 figures, 2 tables)

This paper contains 10 sections, 39 equations, 5 figures, 2 tables.

Table of Contents

  1. INTRODUCTION
  2. System and Control Problem Definition
  3. Car-following Model
  4. Model Convexification
  5. Model Predictive Control
  6. Robust Model Predictive Controller
  7. Simulation Results
  8. Simulation Setup
  9. Numerical Examples
  10. Conclusion and Future Work

Figures (5)

  • Figure 1: Scheme of the car-following scenario with ego vehicle equipped with Vehicular ad hoc networks (VANET) communication. The ego vehicle can obtain the anticipated velocity trajectory of the leading vehicle either through direct V2V communication (the leading vehicle is connected) or relying on road side units to measure and transmit the leading vehicle data by V2I communication (the leading vehicle is not connected).
  • Figure 2: Driving cycle of WLTP-M Phase.
  • Figure 3: Comparison on the inter-vehicular distance between nominal and robust MPCs
  • Figure 4: Comparison on battery energy consumption between robust time-domain and space-domain schemes.
  • Figure :