Research on an Improved equivalent fuel consumption minimization strategy Based on Ant Colony Algorithm - Jing, Peiyang; Wang, Xingcheng; Cai, Mingyu; Sheng, Yang (Chinese Automation Congress (Online), 2019)
In order to further improve fuel economy and control system stability of hybrid electric vehicle (HEV), the ant colony algorithm (ACO) is used to optimize the control strategy. Firstly, the logic threshold control strategy is combined with the traditional equivalent fuel consumption minimization strategy (ECMS). On this basis, the ant colony algorithm is used to optimize the charge and discharge equivalent factors of the improved energy management strategy. This research mainly seeks optimization under UDDS conditions, and finally simulates on the ADVISOR platform. In the final simulation results, the improved equivalent fuel consumption minimization strategy based on ant colony algorithm (ACO-ECMS) has higher fuel economy and emission control than the traditional equivalent fuel consumption minimization strategy.