Multi-objective optimization strategy of adaptive cruise control considering regenerative energy. (December 2019)

• Record Type: Journal Article
• Authors: Wu, Di; Zhu, Bo; Tan, Dongkui; Zhang, Nong; Gu, Jiaxin
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 233:Number 14(2019)
• Page Start: 3630
• Page End: 3645
• Publication Date: 2019-12
• Subjects: Adaptive cruise control -- variable time headway -- time-varying weights -- adaptive model predictive control -- regenerative energy; Mechanical engineering -- Congresses; Transportation engineering -- Congresses; 629.2
• DOI: 10.1177/0954407019830200
• Languages: English
• ISSNs: 0954-4070

Abstract

A multi-objective optimization strategy considering regenerative braking was proposed. Taking into account the impact of relative velocity, driver style, and road adhesion conditions, a variable time headway strategy was first proposed. Then, a multi-objective optimization strategy in adaptive cruise system was designed under the model predictive control framework. The incremental adaptive model predictive control with time-varying weights was constructed to be used as the upper controller, and slack variable was used to process the constraints. The constraints of regenerative braking were analyzed, and a new brake force distribution strategy based on multi-source information fusion was proposed to further optimize the economy. On the AMESim & Simulink co-simulation platform, a battery electric vehicle model was built and the proposed strategy was simulated. The results showed that, comparing to the constant-weight strategy, the proposed strategy had better robustness, which could rapidly and timely adjust the control target and guarantee the safety, comfort, economy, and following. The multi-source information braking force distribution strategy can guarantee several goals of the system while improving the economy. It can regenerate more braking energy, and the braking regenerative energy contribution increased by 5.68%.