Vehicle Speed Profile Optimization for Fuel Efficient Eco-Driving via Koopman Linear Predictor and Model Predictive Control

Advancements in Vehicle-to-Everything (V2X) technologies have opened new avenues for enhancing fuel efficiency in long-haul trucks through ecological (eco-) driving initiatives. These initiatives are commonly executed via a Model Predictive Control (MPC) framework, relying on lookahead information like road grade and speed limits. However, the performance of this framework relies on a precise model of the vehicle's longitudinal dynamics, which can be challenging due to the inherent non-linearities in the vehicle's behavior. Nonlinear MPC (NLMPC) is often impractical for real-time, embedded implementations of eco-driving programs. To address this challenge, we employ the principles of Koopman operator theory and Extended Dynamic Mode Decomposition (EDMD) to construct a linear dynamical system in an augmented state space. This linear model, known as the Koopman Linear Predictor (KLP), approximates the vehicle's longitudinal dynamics, providing a valuable tool for designing optimal speed profiles and trajectories that minimize fuel consumption. Numerical simulation results show the computational advantages of the proposed Koopman MPC (KMPC) framework over NLMPC. Moreover, this approach proves to be particularly effective in executing eco-driving programs over long routes.