Enhancement of an Adaptive HEV Operating Strategy Using Machine Learning Algorithms

被引：0

作者：

Schudeleit, Mark ^{[1
]}

Zhang, Meng ^{[2
]}

Qi, Xiaofei ^{[1
]}

Kuecuekay, Ferit ^{[1
]}

Rausch, Andreas ^{[2
]}

机构：

[1] Tech Univ Carolo Wilhelmina Braunschweig, Inst Automot Engn, Braunschweig, Germany

[2] Tech Univ Clausthal, Inst Informat, Clausthal Zellerfeld, Germany

来源：

LEVERAGING APPLICATIONS OF FORMAL METHODS, VERIFICATION AND VALIDATION: DISCUSSION, DISSEMINATION, APPLICATIONS, ISOLA 2016, PT II | 2016年 / 9953卷

关键词：

D O I：

10.1007/978-3-319-47169-3_53

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

For vehicle manufacturers as well as for many drivers saving fuel has been a popular issue. In order to maximize the potential of the consumption-savings, optimization of operating strategy of vehicles, particularly of HEV (HEV: hybrid electric vehicle.), becomes an increasingly important task. To enhance the current rule-based operating strategy of HEV, an adaptive heuristic operating strategy has been developed, which identifies driving patterns and chooses the best parameter set for the control strategy from a database. However, this strategy does not fit to the driving behavior of individual drivers. Therefore, a further knowledge-based approach that independently optimizes the operating strategy has been developed using of multigene symbolic regression by utilizing supervised machine learning. The investigation showed that a knowledge-based approach is able to save about 18.3 % CO2 and fuel compared to a basic heuristic strategy.

引用

页码：688 / 702

页数：15

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