Xu, Fuguo (Tokyo City University), Shen, Tielong (Sophia University), Nonaka, Kenichiro (Tokyo City University)

Decentralized Optimal Energy Efficiency Improvement Strategy for Large-Scale Connected HEVs

Scheduled for presentation during the Regular Session "Onboard Energy Management in Electrified Powertrains " (WeAT3), Wednesday, August 31, 2022, 11:20−11:40, Ballroom

10th IFAC International Symposium on Advances in Automotive Control, August 28-31, 2022, Columbus, Ohio, USA

This information is tentative and subject to change. Compiled on April 19, 2024

Abstract

In this paper, the energy efficiency improvement optimization strategy is explored for large-scale hybrid electric vehicles (HEVs) in a connected environment. Both reducing vehicle speed fluctuation and increasing high efficiency working conditions of HEV powertrain are beneficial for fuel economy improvement. A hierarchical optimization strategy is designed in this paper, where the speed consensus problem is considered in the upper layer and an energy management problem is considered in the lower layer. To deal with optimization of large-scale HEVs, mean field game (MFG) is employed for speed consensus. Meanwhile, model predictive MFG-based control scheme is developed with consideration of distribution predication error caused by the uncertainties of road and traffic. With connection of vehicle to everything (V2X), the real-time distribution can be calculated in the big data center and sent back to individual HEV for model predictive MFG-based controller. Simulations are conducted to show the effectiveness of the proposed strategy.