期刊
71ST CONFERENCE OF THE ITALIAN THERMAL MACHINES ENGINEERING ASSOCIATION (ATI 2016)
卷 101, 期 -, 页码 685-692出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.egypro.2016.11.087
关键词
Machine learning; rule-based controller; genetic algorithm; dynamic programming
Two different machine-learning techniques have been assessed and applied to define rule-based control strategies for a parallel hybrid midsize sport utility vehicle equipped with a diesel engine. Both methods include two phases: a clustering algorithm and a rule definition. In the first method, a homemade clustering algorithm is preliminarily run to generate the set of clusters, while the rules are identified by minimizing an objective function. In the second method, a genetic algorithm provides the optimal size of the clusters, while the associated rules are extracted from the results obtained with a benchmark optimizer The controllers were tested over NEDC, 1015, AMDC and WLTP. (C) 2016 The Authors. Published by Elsevier Ltd.
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