Predictive Adaptive Cruise Control for Heavy-duty Vehicle based on Cloud Control System

Abstract

With the concept of cloud control system (CCS) for intelligent and connected vehicles proposed and developed, the vehicles and traffic safety and economy have a comprehensive improvement. However, the current research mostly stays in vehicle-to-vehicle or vehicle-to-infrastructure, which requires an on-board calculator with large computational and storage capacity, and the planning range is short. To solve these problems, a predictive adaptive cruise control (PACC) algorithm based on CCS is proposed. Hierarchical control architecture of PACC between vehicle and cloud is constructed, coordinating free cruising mode with car-following mode. For the free cruising mode, an ergodic optimization method placed in the cloud is designed. A multi-objective optimization algorithm based on model predictive control is designed for the car-following mode, which can plan the vehicle speed in advance. Finally, the simulation results show that the PACC algorithm can achieve 8.16% fuel saving with consuming less time for free cruising, and for car-following, it can achieve 3.23% fuel saving. More importantly, the results also show that the CCS has significant potential and feasibility for intelligent and connected vehicles application.