Quantifying Performance of a Connected Vehicle by Optimal Control∗. Issue 15 (2015)

• Record Type: Journal Article
• Authors: Park, Hyeongjun; Gupta, Rohit; Dai, Edward; McCallum, James; Pietron, Gregory; Shelton, Matthew; Kolmanovsky, Ilya
• Is Part Of: IFAC-PapersOnLine. Volume 48:Issue 15(2015)
• Page Start: 328
• Page End: 334
• Publication Date: 2015
• Subjects: Powertrain control -- optimal control -- integrated powertrain control -- acceleration performance optimization -- connected vehicles -- trajectory optimization; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2015.10.047
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: In this paper we exploit trajectory optimization techniques to quantify acceleration performance improvements in an advanced powertrain that are achievable if a preview of a launch event is available about 1 sec in advance through vehicle to traffic light communications. The vehicle is at idle immediately before acceleration. Based on the preview information, the idle speed is increased immediately before launch. A dynamic optimization approach is employed to optimize the idle speed set-point and trajectories of clutch and engine actuators after start of the acceleration. The optimization is applied to a high complexity model of powertrain. To perform the optimization, the trajectories being optimized are first transcribed using Gaussian kernel functions and then a mesh adaptive direct search (MADS) algorithm is applied. The achievable average acceleration with and without preview (i.e., without raising pre-launch engine speed) and for different sets of actuator trajectories being optimized under drivability constraints are compared thereby quantifying the benefits of vehicle-to-infrastructure connectivity.