Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Predictive Model of ACC Speed to Enhance Engine Operating Conditions

Version 1 : Received: 16 October 2021 / Approved: 18 October 2021 / Online: 18 October 2021 (14:48:11 CEST)

A peer-reviewed article of this Preprint also exists.

Kolachalama, S.; Malik, H. Predictive Model of Adaptive Cruise Control Speed to Enhance Engine Operating Conditions. Vehicles 2021, 3, 749-763. Kolachalama, S.; Malik, H. Predictive Model of Adaptive Cruise Control Speed to Enhance Engine Operating Conditions. Vehicles 2021, 3, 749-763.

Journal reference: Vehicles 2021, 3, 44
DOI: 10.3390/vehicles3040044

Abstract

The ACC feature when activated augments the engine performance in real-time. This article presents a novel methodology to predict the optimal adaptive cruise control set speed profile (ACCSSP) by considering all the effecting parameters. This paper investigates engine operating conditions (EOC) criteria to develop a predictive model of ACCSSP in real-time. We developed a deep learning (DL) model using the NARX method to predict engine operating point (EOP) mapping the vehicle-level vectors (VLV). We used real-world field data obtained from Cadillac test vehicles driven by activating the ACC feature for developing the DL model. We used a realistic set of assumptions to estimate the VLV for the future time steps for the range of allowable speed values and applied them at the input of the developed DL model to generate multiple sets of EOP’s. We imposed the defined EOC criteria on these EOPs, and the top three modes of speeds satisfying all the requirements are derived for each second. Thus three eligible speed values are estimated for each second, and an additional criterion is defined to generate a unique ACCSSP for future time steps. Performance comparison between predicted and constant ACCSSPs indicates that the predictive model outperforms constant ACCSSP.

Keywords

Adaptive Cruise Control; Driver behvaiour; Deep learning; Engine Operating Point; NARX

Subject

ENGINEERING, Automotive Engineering

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