Effective optimal control strategy for hybrid electric vehicle with continuously variable transmission. (March 2019)

• Record Type: Journal Article
• Authors: Li, Hangyang; Hu, Xiaolan; Fu, Bing; Wang, Jiande; Zhang, Feitie; Zhou, Yunshan
• Is Part Of: Advances in mechanical engineering. Volume 11:Number 3(2019)
• Publication Date: 2019-03
• Subjects: Continuously variable transmission -- hybrid electric vehicle -- dynamic programming -- drivability -- penalty function; Mechanical engineering -- Periodicals; 621.05
• DOI: 10.1177/1687814018824811
• Languages: English
• ISSNs: 1687-8132

Abstract

Hybrid electric vehicles equipped with continuously variable transmission show dramatic improvements in fuel economy and driving performance because they can continuously adjust the operating points of the power source. This article proposes an optimal control strategy for continuously variable transmission–based hybrid electric vehicles with a pre-transmission parallel configuration. To explore the fuel-saving potential of the given configuration, a 'control-oriented' quasi-static vehicle model is built, and dynamic programming is adopted to determine the optimal torque split factor and continuously variable transmission speed ratio. However, a single-criterion cost function will lead to undesirable drivability problems. To tackle this problem, the main factors affecting the driving performance of a continuously variable transmission–based hybrid electric vehicle are studied. On that basis, a multicriterion cost function is proposed by introducing drivability constraints. By varying the weighting factors, the trade-off between fuel economy and drivability can be evaluated under a predetermined driving cycle. To validate the effectiveness of the proposed method, simulation experiments are performed under four different driving cycles, and the results indicate that the proposed method greatly enhanced the drivability without significantly increasing fuel consumption. Compared to a single-criterion cost function, the use of multiple criteria is more representative of real-world driving behaviour and thus provides better reference solutions to evaluate suboptimal online controllers.