Cooperative control strategy for plug-in hybrid electric vehicles based on a hierarchical framework with fast calculation. (1st April 2020)

• Record Type: Journal Article
• Authors: Zhang, Yuanjian; Guo, Chong; Li, Guang; Liu, Yonggang; Chen, Zheng
• Is Part Of: Journal of cleaner production. Volume 251(2020)
• Publication Date: 2020-04-01
• Subjects: Cooperative control strategy -- Hierarchical framework -- Iterative dynamic programming (IDP) -- Model predictive control (MPC) -- Plug-in hybrid electric vehicles (PHEVs); Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2019.119627
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: Developing optimal control strategies with capability of real-time implementation for plug-in hybrid electric vehicles (PHEVs) has drawn explosive attention. In this study, a novel hierarchical control framework is proposed for PHEVs to achieve the instantaneous vehicle-environment cooperative control. The mobile edge computation units (MECUs) and the on-board vehicle control units (VCUs) are included as the distributed controllers, which enable vehicle-environment cooperative control and reduce the computation intensity on the vehicle by transferring partial work from VCUs to MECUs. On this basis, a novel cooperative control strategy is designed to successively achieve the energy management planned by the iterative dynamic programming (IDP) in MECUs and the energy utilization management achieved by the model predictive control (MPC) algorithm in the VCU. The performance of raised control strategy is validated by simulation analysis, highlighting that the cooperative control strategy can achieve superior performance in real-time application that is close to the global optimization results solved offline. Highlights: A fast calculation control strategy is raised for plug-in hybrid electric vehicles . The hierarchical framework is built to uphold the implementation of strategy . Iterative dynamic programming attains fast global optimization . Model predictive control completes global optimal control policy tracking.