Research on an Improved equivalent fuel consumption minimization strategy Based on Ant Colony Algorithm

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 str...

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Bibliographic Details
Published inChinese Automation Congress (Online) pp. 2318 - 2323
Main Authors Jing, Peiyang, Wang, Xingcheng, Cai, Mingyu, Sheng, Yang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2019
Subjects
ACO-ECMS
Batteries
control strategy
Discharges (electric)
Electronic countermeasures
Engines
Fuels
HEV
State of charge
Torque
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ISSN2688-0938
DOI10.1109/CAC48633.2019.8996647

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Summary: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.
ISSN:2688-0938
DOI:10.1109/CAC48633.2019.8996647