文章基本信息

标题：An Artificial Potential Function For Battery Life Optimization In Car-following System ⁎
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作者：Bo Liu ; Yingze Yang ; Hongtao Liao 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：14236-14241
DOI：10.1016/j.ifacol.2020.12.1156
语种：English
出版社：Elsevier
摘要：AbstractFor a pure electric car-following system, if the auto-following vehicle acts in an aggressive following manner, battery life fades evidently, since overcharging or over-discharging damage the cell irreversibly. In this regard, this paper proposed an artificial potential function for battery life extension. First, the electric vehicle physical model and an empirical lithium-ion battery model have established form real-world data measurement. The physical layer models car-following dynamics and the battery model describes the energy consumption. Second, with the perceptive of the battery life in a loss-minimal, optimize manner, the controller mathematically computes the optimal acceleration/deceleration value with the Lagrange multipliers method. Then using the Matlab curve fitting tool toolbox to fusion optimal acceleration data with potential function, thus the acceleration consistent rule is realized through the consistency of an artificial potential function. Finally, the control strategy is validated through a simulation test in Matlab/Simulink, and the results show that the proposed control strategy extends battery life while keeping good tracking ability.
关键词：Keywordscar-following control strategybattery life optimizationartificial potential function