Communication‐based predictive energy management strategy for a hybrid powertrain. (25th September 2021)

• Record Type: Journal Article
• Authors: Deng, Junpeng; Adelberger, Daniel; del Re, Luigi
• Other Names: Incremona Gian Paolo guestEditor.; Zanon Mario guestEditor.
• Is Part Of: Optimal control applications and methods. Volume 43:Number 1(2022)
• Page Start: 86
• Page End: 105
• Publication Date: 2021-09-25
• Subjects: automotive control -- HEV -- powertrain optimization -- V2V -- V2X; Control theory -- Periodicals; Mathematical optimization -- Periodicals; 629.8312
• DOI: 10.1002/oca.2795
• Languages: English
• ISSNs: 0143-2087

Abstract

Abstract: Predictive information can significantly improve energy efficiency in a hybrid powertrain, especially for a long drive. Ideally the prediction of a sufficiently long horizon can bring the maximum benefit, however during this horizon the traffic can change, making it impossible to continue the chosen optimal strategy. Due to the limited vision of vehicles' sensors, it is difficult to acquire information far away. Against this background, this article proposes a predictive optimal control strategy in the connected environment. It combines real‐world vehicle‐to‐everything (V2X) information and cloud communication to allow for swift reaction and loss mitigation. More specifically, V2X information is used to detect impending changes on the route and the cloud is used to map these changes onto new constraints on the operation of the HEV. Instead of using "static" V2X information directly, a more realistic prediction method considering the dynamic of traffic is developed. Besides, an update strategy is adopted to timely cope with uncertainties. The performance of the approach is shown by a case study based on real driving cycles in Austria. The results show that the proposed structure is able to improve the performance (mainly fuel efficiency) up to 6 . 9 % .