Energy-Aware Lane Planning for Connected Electric Vehicles in Urban Traffic: Design and Vehicle-in-the-Loop Validation

TL;DR

Problem: energy-efficient urban driving for CAVs requires considering lane-change decisions, not just speed control. Approach: an energy-aware lane selection framework combines short-horizon optimal control with a graph-based long-horizon energy-cost approximation and a data-driven BEV energy model, all leveraging V2I/SPaT data. Validation: vehicle-in-the-loop experiments with Hyundai IONIQ5 show substantial energy reductions: up to 39% motion-energy reduction versus a human driver and 24% total energy reduction versus lane-keeping eco-driving baseline (7% vs that baseline). Significance: demonstrates the potential of connectivity-enabled lane planning to improve urban EV sustainability, while acknowledging travel-time costs and the need for broader generalization.

Abstract

Urban driving with connected and automated vehicles (CAVs) offers potential for energy savings, yet most eco-driving strategies focus solely on longitudinal speed control within a single lane. This neglects the significant impact of lateral decisions, such as lane changes, on overall energy efficiency, especially in environments with traffic signals and heterogeneous traffic flow. To address this gap, we propose a novel energy-aware motion planning framework that jointly optimizes longitudinal speed and lateral lane-change decisions using vehicle-to-infrastructure (V2I) communication. Our approach estimates long-term energy costs using a graph-based approximation and solves short-horizon optimal control problems under traffic constraints. Using a data-driven energy model calibrated to an actual battery electric vehicle, we demonstrate with vehicle-in-the-loop experiments that our method reduces motion energy consumption by up to 24 percent compared to a human driver, highlighting the potential of connectivity-enabled planning for sustainable urban autonomy.

Figures (7)

• Figure 1: Vehicle-in-the-loop control architecture arpae-lc
• Figure 2: Lane selection problem in urban road
• Figure 3: Comparison of quadratic energy consumption model and measured data
• Figure 4: Illustration of a graph for navigation through multiple traffic lights. The graph approximates the pass or nonpass decision for a fixed lane and velocity
• Figure 5: The virtual CARLA simulator map, the satellite image of the actual testing site, and the physical test vehicle