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Co-optimization of Vehicle Dynamics and Powertrain Management for Connected and Automated Electric Vehicles

Zongtan Li, Yunli Shao

TL;DR

The paper investigates energy-efficient eco-driving for connected and automated electric vehicles by jointly optimizing vehicle speed and dual-motor torque distribution using V2X-predicted traffic. It develops a control-oriented model set and an MPC framework capable of real-time computation, validated on real-world traffic data from Chattanooga, TN. Key findings show a 12.80–24.52% reduction in vehicle power consumption, with dual-motor torque optimization contributing 3.16–4.40% of the gains, and demonstrated robustness to prediction uncertainties. The work highlights practical pathways for deployment in CAVs and BEVs, and suggests extending the model to include battery thermal management and lab-based validation for further gains.

Abstract

Connected and automated vehicles (CAVs) represent the future of transportation, utilizing detailed traffic information to enhance control and decision-making. Eco-driving of CAVs has the potential to significantly improve energy efficiency, and the benefits are maximized when both vehicle speed and powertrain operation are optimized. In this paper, we studied the co-optimization of vehicle speed and powertrain management for energy savings in a dual-motor electric vehicle. Control-oriented vehicle dynamics and electric powertrain models were developed to transform the problem into an optimal control problem specifically designed to facilitate real-time computation. Simulation validation was conducted using real-world data calibrated traffic simulation scenarios in Chattanooga, TN. Evaluation results demonstrated a 12.80-24.52% reduction in the vehicle's power consumption under ideal predicted traffic conditions, while maintaining benefits with various prediction uncertainties, such as Gaussian process uncertainties on acceleration and time-shift effects on predicted speed. The energy savings of the proposed eco-driving strategy are achieved through effective speed control and optimized torque allocation. The proposed model can be extended to various CAV and electric vehicle applications, with potential adaptability to diverse traffic scenarios.

Co-optimization of Vehicle Dynamics and Powertrain Management for Connected and Automated Electric Vehicles

TL;DR

The paper investigates energy-efficient eco-driving for connected and automated electric vehicles by jointly optimizing vehicle speed and dual-motor torque distribution using V2X-predicted traffic. It develops a control-oriented model set and an MPC framework capable of real-time computation, validated on real-world traffic data from Chattanooga, TN. Key findings show a 12.80–24.52% reduction in vehicle power consumption, with dual-motor torque optimization contributing 3.16–4.40% of the gains, and demonstrated robustness to prediction uncertainties. The work highlights practical pathways for deployment in CAVs and BEVs, and suggests extending the model to include battery thermal management and lab-based validation for further gains.

Abstract

Connected and automated vehicles (CAVs) represent the future of transportation, utilizing detailed traffic information to enhance control and decision-making. Eco-driving of CAVs has the potential to significantly improve energy efficiency, and the benefits are maximized when both vehicle speed and powertrain operation are optimized. In this paper, we studied the co-optimization of vehicle speed and powertrain management for energy savings in a dual-motor electric vehicle. Control-oriented vehicle dynamics and electric powertrain models were developed to transform the problem into an optimal control problem specifically designed to facilitate real-time computation. Simulation validation was conducted using real-world data calibrated traffic simulation scenarios in Chattanooga, TN. Evaluation results demonstrated a 12.80-24.52% reduction in the vehicle's power consumption under ideal predicted traffic conditions, while maintaining benefits with various prediction uncertainties, such as Gaussian process uncertainties on acceleration and time-shift effects on predicted speed. The energy savings of the proposed eco-driving strategy are achieved through effective speed control and optimized torque allocation. The proposed model can be extended to various CAV and electric vehicle applications, with potential adaptability to diverse traffic scenarios.

Paper Structure

This paper contains 27 sections, 30 equations, 7 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Electric Powertrain Modeling
  3. Powertrain Model
  4. Vehicle Power Consumption Model
  5. Battery Model
  6. Optimal Control Problem Formulation
  7. State-space Model
  8. Constraints
  9. Traffic Dynamics Constraints
  10. Car Following Constrains
  11. Signal Constraint
  12. Speed Limit Constraints
  13. Vehicle Dynamics Constraints
  14. Motor Speed Constraint
  15. SOC Constraints
  16. ...and 12 more sections

Figures (7)

  • Figure 1: Control diagram of the proposed framework
  • Figure 2: Traffic simulation scenario in VISSIM.
  • Figure 3: Optimized trajectory of the ego vehicle and preceding vehicle with traffic light status.
  • Figure 4: State and powertrain graphs with the preceding vehicle id 11084.
  • Figure 5: Efficiency map and operating points for front and rear motors
  • ...and 2 more figures