Eco-Driving Assistance System based on Variable Sampling Model Predictive Control

This paper presents an eco-driving assistance system based on model predictive control (MPC) with variable sampling within the prediction horizon, such as quadratic point sampling (QPS) using a quadratic time-warping function and dynamic point sampling (DPS) where the Douglas-Peucker algorithm is used to dynamically select relevant sampling points, and provides comparison to uniform point sampling (UPS). The controllers aim to track the energy-optimal velocity profile derived from dynamic programming (DP), which incorporates long-term knowledge of speed limits and road slope, while maintaining a safe distance from a preceding vehicle in a car-following scenario, taking into account short-term safety-relevant knowledge. Variable sampling is used to consider the short- and long-term objectives simultaneously, promoting energy efficiency and safety and providing real-time capability. The controllers were evaluated in MATLAB simulations using the model of a Renault ZOE Q90, a compact battery electric vehicle (BEV), with scenarios both including and excluding knowledge of the preceding vehicle’s velocity trajectory. The results show that variable sampling strategies (QPS and DPS) offer greater efficiency than uniform point sampling, especially in complex traffic scenarios, leading to improved energy savings while maintaining safety.