Predictive Model of Adaptive Cruise Control Speed to Enhance Engine Operating Conditions
نویسندگان
چکیده
This article presents a novel methodology to predict the optimal adaptive cruise control set speed profile (ACCSSP) by optimizing engine operating conditions (EOC) considering vehicle level vectors (VLV) (body parameter, environment, driver behaviour) as affecting parameters. paper investigates criteria develop predictive model of ACCSSP in real-time. We developed deep learning (DL) using NARX method point (EOP) mapping VLV. used real-world field data obtained from Cadillac test vehicles driven activating ACC feature for developing DL model. realistic assumptions estimate VLV future time steps range allowable values and applied them at input generate multiple sets EOP’s. imposed defined EOC on these EOPs, top three modes speeds satisfying all requirements are derived each second. Thus, eligible estimated second, an additional criterion is unique steps. A performance comparison between predicted constant ACCSSP’s indicates that outperforms ACCSSP.
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ژورنال
عنوان ژورنال: Vehicles
سال: 2021
ISSN: ['2624-8921']
DOI: https://doi.org/10.3390/vehicles3040044