Eco-Driving Control for Electric Vehicles with Multi-Speed Transmission: Optimizing Vehicle Speed and Powertrain Operation in Dynamic Environments

TL;DR

This work tackles the challenge of maximizing energy efficiency for electric vehicles equipped with multi-speed transmissions in dynamic traffic. It introduces a real-time co-optimization framework that simultaneously optimizes ego-vehicle speed and powertrain operation within a model-predictive control (MPC) setting, incorporating a detailed powertrain and battery model and safety constraints derived from V2X SPaT data and traffic prediction. The approach employs problem simplifications including Big-M linearization, McCormick relaxation, and SOS2 piecewise-linear approximations to achieve real-time solvability, with a prediction horizon of 10 seconds and updates every second. Validation across SUMO simulations and real-world road tests shows substantial energy savings (up to 12% in simulations and 11.36% in experiments) for multi-speed EVs compared with single-speed baselines, underscoring the practical benefits for range and efficiency in connected traffic environments. The work highlights the feasibility of integrating multi-speed transmissions into real-time eco-driving, while pointing to future work on incorporating battery thermal dynamics for a more holistic powertrain management approach.

Abstract

This article presents an eco-driving algorithm for electric vehicles featuring multi-speed transmissions. The proposed controller is formulated as a co-optimization problem, simultaneously optimizing both vehicle longitudinal speed and powertrain operation to maximize energy efficiency. Constraints derived from a connected vehicle based traffic prediction algorithm are used to ensure traffic safety and smooth traffic flow in dynamic environments with multiple signalized intersections and mixed traffic. By simplifying the complex, nonlinear mixed integer problem, the proposed controller achieves computational efficiency, enabling real-time implementation. To evaluate its performance, traffic scenarios from both Simulation of Urban MObility (SUMO) and real-world road tests are employed. The results demonstrate a notable reduction in energy consumption by up to 11.36\% over an \SI{18}{\km} drive.

Figures (8)

• Figure 1: Traffic prediction framework used in this work. The red car represents the ego vehicle that operates using the proposed algorithm. Yellow vehicles indicate CVs, while gray vehicles represent non-CVs. Using the real-time traffic information obtained through communications and sensors, the algorithm predicts the longitudinal movement of the vehicle directly ahead.
• Figure 2: Residual heat map for motor power approximation.
• Figure 3: Schematic of the eco-driving co-optimization algorithm.
• Figure 4: The comparison of the eco-driving co-optimization results for EVs equipped with three-speed and single-speed transmissions. Blue lines represent the states of the vehicle with three-speed transmission, while orange lines show those of the vehicle with single-speed transmission. In the spacing subplot, the red and black dashed lines correspond to the following distance constraints outlined in \ref{['eq:ev-eco-car-following-constraint']}, representing the ego vehicle with three-speed and single-speed transmissions, respectively.
• Figure 5: Comparison of motor operating points, where blue points indicate the operating conditions for the vehicle with a three-speed transmission, and orange points represent those for the single-speed transmission vehicle.