Mohamed Maged, Dalia M. Mahfouz, Omar M. Shehata, E. I. Morgan
{"title":"Behavioral Assessment of an Optimized Multi-Vehicle Platoon Formation Control for Efficient Fuel Consumption","authors":"Mohamed Maged, Dalia M. Mahfouz, Omar M. Shehata, E. I. Morgan","doi":"10.1109/NILES50944.2020.9257911","DOIUrl":null,"url":null,"abstract":"Over the past few decades, climate change, air pollution and road safety have been classified as vital problems affecting the globe adversely in terms of transportation. To solve these problems, Intelligent Transportation Systems (ITS) are investigated. One of the important ITS applications is vehicle platooning, which is contemplated to enhance road organization and reduce the overall fuel consumption. In this study a cooperative optimal algorithm is adopted to coordinate several vehicles to form platoons that minimize the total fuel cost by maximizing distance vehicles are in platoon, through the adjustment of the vehicles’ speeds. The algorithm is based on pairwise coordination by which the coordination decision is made between each two vehicles or sub-platoons to form a platoon based on fuel-saving potential. The optimization problem outputs the desired optimal speed profiles for each vehicle offline. These speed profiles are then sent to a cruise controller to control each vehicle’s dynamics to reach the desired optimal speeds. A nonlinear vehicle dynamic model including the powertrain dynamics is investigated. A hierarchical speed control approach is used, having an optimal Model predictive Control (MPC) as the upper level controller and a linear Proportional-Integral-Derivative (PID) as the lower level control approach used to manage the vehicles’ velocities. The coordination algorithm and the controller are tested on a scenario of four scattered vehicles moving on a flat road, having same destination point. The simulation scenario is conducted to test the coordination algorithm and demonstrate the performance of the controller in terms of velocity tracking, realistic control effort and reduced fuel consumption. Results show that the optimization and control objectives are achieved successfully.","PeriodicalId":253090,"journal":{"name":"2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NILES50944.2020.9257911","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Over the past few decades, climate change, air pollution and road safety have been classified as vital problems affecting the globe adversely in terms of transportation. To solve these problems, Intelligent Transportation Systems (ITS) are investigated. One of the important ITS applications is vehicle platooning, which is contemplated to enhance road organization and reduce the overall fuel consumption. In this study a cooperative optimal algorithm is adopted to coordinate several vehicles to form platoons that minimize the total fuel cost by maximizing distance vehicles are in platoon, through the adjustment of the vehicles’ speeds. The algorithm is based on pairwise coordination by which the coordination decision is made between each two vehicles or sub-platoons to form a platoon based on fuel-saving potential. The optimization problem outputs the desired optimal speed profiles for each vehicle offline. These speed profiles are then sent to a cruise controller to control each vehicle’s dynamics to reach the desired optimal speeds. A nonlinear vehicle dynamic model including the powertrain dynamics is investigated. A hierarchical speed control approach is used, having an optimal Model predictive Control (MPC) as the upper level controller and a linear Proportional-Integral-Derivative (PID) as the lower level control approach used to manage the vehicles’ velocities. The coordination algorithm and the controller are tested on a scenario of four scattered vehicles moving on a flat road, having same destination point. The simulation scenario is conducted to test the coordination algorithm and demonstrate the performance of the controller in terms of velocity tracking, realistic control effort and reduced fuel consumption. Results show that the optimization and control objectives are achieved successfully.