Cooperative Estimation of Road Grade Based on Multidata Fusion for Vehicle Platoon with Optimal Energy Consumption 2020-01-0586

The platooning of connected automated vehicles (CAV) possesses the significant potential of reducing energy consumption in the Intelligent Transportation System (ITS). Moreover, with the rapid development of eco-driving technology, vehicle platooning can further enhance the fuel efficiency by optimizing the efficiency of the powertrain. Since road grade is a main factor that affects the energy consumption of a vehicle, the estimation of the road grade with high accuracy is the key factor for a 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 and in the meter-level, increasing the difficulty of precisely estimating the road grade. This paper presents a novel estimation method called Cooperative Extended Kalman Filter (CEKF) to obtain the accurate information of slopes by multidata fusion of GPS and on-aboard sensors using vehicle platoon communication, i.e. the following vehicle fuses the data which was measured by the on-board sensors and delivered by the preceding vehicle. Considering the accurate road grade information, the fuel consumption optimization of the vehicle platoon was conducted based on distributed model predictive control (DMPC) with favorable car following performance. According to simulation results, it was found that the accuracy of road grade was improved to a great extent compared with data fusion in a single vehicle. Relying on the more precise road grade information, the powertrain optimization 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 ITS applications.