Cooperative Estimation of Road Grade Based on Multidata Fusion for Vehicle Platoon with Optimal Energy Consumption 2020-01-0586
The platooning of automated vehicles possesses the significant potential of reducing energy consumption in the Intelligent Transportation System (ITS). Moreover, with the rapid development of eco-driving technology, vehicle platoon can further enhance the fuel efficiency by optimizing the efficiency of the powertrain. Since road grade takes great account effectting energy consumption of vehicle, the estimation of the road grade with high accuracy is the key factor for connected vehicle platoon to optimize energy consumption using vehicle-to-vehicle (V2V) communication. Commonly the road grade is quantified by single consumer grade global positioning system (GPS) with the geodetic height data which is rough in meter-level, increasing the difficulty to precisely estimate the road grade. This paper presents a novel cooperative estimation method Extended Kalman Filter (EKF) to obtain the accurate information of slopes by multidata fusion of GPS, Inertial Measurement Unit (IMU) using vehicle platoon communication, i.e. the following vehicle fuses the data which was measured by the on-board sensors and delivered by the proceding vehicle. Considering the accurate road grade information, the fuel consumption optimazition of the vehicle platoon was conducted based on distributed model predictive control (DMPC) with favorable car following ferformance. According to simulation results, it was found that the accuracy of road grade was improved to a great extent compared with data fusion of GPS and IMU in a single vehicle. Relying on the more precise road grade information, the powertrain optimizition could be carried out more effectively, resulting in improved energy economy of the connected vehicle platoon. Hence, the high accuracy cooperative estimation of road grade for vehicle platoon is the foundation of intelligent eco-driving technology and makes a great significance in the ITS application.