Hierarchical predictive control for electric vehicles with hybrid energy storage system under vehicle-following scenarios. (15th July 2022)

• Record Type: Journal Article
• Authors: Wu, Yue; Huang, Zhiwu; Hofmann, Heath; Liu, Yongjie; Huang, Jiahao; Hu, Xiaosong; Peng, Jun; Song, Ziyou
• Is Part Of: Energy. Volume 251(2022)
• Publication Date: 2022-07-15
• Subjects: Hybrid energy storage system -- Model predictive control -- Vehicle-following -- Energy management -- Economy analysis -- V2V/V2I communication; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2022.123774
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: For electric vehicles with hybrid energy storage system, driving economy depends not only on novel energy management strategies but also on load power demand. In order to optimize the power demand and energy management simultaneously, this paper proposes a hierarchical model predictive control framework for electric vehicles with a Li-ion battery/supercapacitor hybrid energy storage system under vehicle-following scenarios. In the vehicle-following level, based on vehicle-to-vehicle and vehicle-to-infrastructure communications, the following vehicle can acquire the real-time velocity and position of the preceding vehicle, optimize the motor electricity consumption, and ensure driving safety through velocity planning. Such cost-effective power demand is further allocated in the energy management level, in order to minimize battery degradation and power losses. Urban, suburban, and highway driving conditions are tested to evaluate the effectiveness and robustness of the proposed method. Determination of prediction horizon and detailed comparison with existing methods are investigated. Simulation results show that compared with optimizing energy management alone under a classical car-following model, the proposed method can reduce the total operation cost by 4.69–14.55% and yield results closer to offline dynamic programming, which provides the globally optimal results. Highlights: Upper-level MPC aims to optimize the electricity cost and ensure safety. Lower-level MPC tends to minimize the battery degradation and power loss. Detailed parameter selection and comparison under different driving conditions. More economy gained from simultaneous optimization of load power/energy management. Battery degradation cost is still 1.97–2.59 times higher than electricity cost.