预测驾驶员行为向量以实时提高发动机性能的智能车辆驾驶模式

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE DataCentric Engineering Pub Date : 2022-04-07 DOI:10.1017/dce.2022.15
Srikanth Kolachalama, Hafiz Abid Mahmood Malik
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引用次数: 2

摘要

摘要本文提出了一种新的驱动模式——“智能汽车驱动模式”(IVDM),以增强汽车发动机的实时性能。该驾驶模式预测驾驶员行为向量(DBV),对汽车发动机性能进行优化,并以发动机工作点(EOP)和暖通空调系统(HVAC)为指标定义了汽车发动机最优性能指标。通过将车辆水平向量(VLV)与EOP和HVAC参数进行映射,建立深度学习模型,并利用训练函数预测DBV的未来状态,以反映增强后的汽车发动机性能。通过经验估计DBV允许范围内VLV的未来状态进行迭代分析,并将其输入到DL模型中预测性能向量。将定义的车辆发动机性能指标应用于预测向量,因此最优DBV是IVDM的瞬时输出。分析和验证技术是根据密歇根州沃伦市通用汽车公司的现场数据开发的。最后,通过分析瞬时发动机效率(IEE)和瞬时发动机图(IEM)的平滑度量,对所提概念进行了量化。
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Intelligent vehicle drive mode which predicts the driver behavior vector to augment the engine performance in real-time
Abstract In this article, a novel drive mode, “intelligent vehicle drive mode” (IVDM), was proposed, which augments the vehicle engine performance in real-time. This drive mode predicts the driver behavior vector (DBV), which optimizes the vehicle engine performance, and the metric of optimal vehicle engine performance was defined using the elements of engine operating point (EOP) and heating ventilation and air conditioning system (HVAC). Deep learning (DL) models were developed by mapping the vehicle level vectors (VLV) with EOP and HVAC parameters, and the trained functions were utilized to predict the future states of DBV reflecting augmented vehicle engine performance. The iterative analysis was performed by empirically estimating the future states of VLV in the allowable range of DBV and was fed into the DL model to predict the performance vectors. The defined vehicle engine performance metric was applied to the predicted vectors, and thus optimal DBV is the instantaneous output of the IVDM. The analytical and validation techniques were developed using field data obtained from General Motors Inc., Warren, Michigan. Finally, the proposed concept was quantified by analyzing the instantaneous engine efficiency (IEE) and smoothness measure of the instantaneous engine map (IEM).
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来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
期刊最新文献
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