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

TL;DR

This paper presents a real-time co-optimization strategy for vehicle speed and powertrain management in connected electric vehicles, achieving significant energy savings validated through realistic traffic simulations.

Contribution

It introduces a control-oriented model and optimal control framework for simultaneous vehicle and powertrain optimization in CAVs, demonstrating substantial energy efficiency improvements.

Findings

12.80-24.52% reduction in power consumption

Effective speed control and torque allocation improve energy savings

Model adaptable to various traffic scenarios

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.