{"title":"Optimal parameter selection of a Model Predictive Control algorithm for energy efficient driving of heavy duty vehicles","authors":"Michael Henzler, M. Buchholz, K. Dietmayer","doi":"10.1109/IVS.2015.7225773","DOIUrl":null,"url":null,"abstract":"This paper presents an improved approach to the problem of energy efficient driving of heavy duty vehicles. The proposed model for a map-based Model Predictive Control (MPC) leads to an underlying Quadratic Programming (QP) optimization problem, allowing computationally efficient and robust solutions. A parameter estimation procedure is developed for a vehicle- and optimization-independent parametrization of the tradeoff between saving energy and keeping a desired vehicle velocity. Extensive simulations on a highway scenario for different optimization parameters give further insight to optimization properties, which can be utilized to enhance control performance. Compared to previous literature, we demonstrate a significant improvement of the computation time to under one-fifth of a millisecond, while maintaining (or even increasing) the fuel consumption reduction, which is 8.1 percent with the proposed approach compared to a standard cruise controller, without a decrease in the average cruising speed.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE Intelligent Vehicles Symposium (IV)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IVS.2015.7225773","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 12
Abstract
This paper presents an improved approach to the problem of energy efficient driving of heavy duty vehicles. The proposed model for a map-based Model Predictive Control (MPC) leads to an underlying Quadratic Programming (QP) optimization problem, allowing computationally efficient and robust solutions. A parameter estimation procedure is developed for a vehicle- and optimization-independent parametrization of the tradeoff between saving energy and keeping a desired vehicle velocity. Extensive simulations on a highway scenario for different optimization parameters give further insight to optimization properties, which can be utilized to enhance control performance. Compared to previous literature, we demonstrate a significant improvement of the computation time to under one-fifth of a millisecond, while maintaining (or even increasing) the fuel consumption reduction, which is 8.1 percent with the proposed approach compared to a standard cruise controller, without a decrease in the average cruising speed.