文章基本信息

标题：Forecasting ECMS for Hybrid Electric Vehicles
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作者：Jean Kuchly ; Dominique Nelson-Gruel ; Alain Charlet 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：14154-14160
DOI：10.1016/j.ifacol.2020.12.1027
语种：English
出版社：Elsevier
摘要：AbstractThis paper aims to propose a real-time suitable method to tackle the problem of energy and pollutant management of Hybrid Electric Vehicles. Methods proposed in the literature often limit the underlying optimal control problem to single-instant optimizations (Paganelli, 2002) due to the difficulty of taking future into account and to onboard limited computational resources. The point of the present paper is to propose an online oriented method based on a long-term vehicle speed prediction, using cartographic information such as speed limitation, road curvature, traffic and road signs. Pontryagin Maximum Principle applied on this speed prediction signal allows to convert the optimal control problem into a root-finding problem. This problem is solved using a Pegasus algorithm initialized by a black-box method trained offline, allowing high computational efficiency. The results are near-optimal and significantly better than classical methods: in the real-driving trip used in this paper, forecasting-ECMS showed a consumption 1.1% better and NOxemissions 4.4% better than a SOC-feedback adaptive-ECMS.
关键词：KeywordsHybrid Electric VehiclesEnergy ManagementPollutants ManagementReal-Time