基于人工智能的高性能汽车尾气污染物排放虚拟传感技术

IF 1.1 Q3 TRANSPORTATION SCIENCE & TECHNOLOGY
Emanuele Giovannardi, A. Brusa, Boris Petrone, N. Cavina, Roberto Tonelli, Ioannis Kitsopanidis
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引用次数: 0

摘要

本科学出版物介绍了人工智能(AI)技术作为虚拟传感器在高性能汽车尾气一氧化碳、氮氧化物和碳氢化合物排放中的应用。该研究旨在解决实际工业应用中面临的关键挑战,包括信号对齐和信号动态管理。为解决这些问题,提出了一个全面的预处理管道,并采用轻梯度提升机(LightGBM)模型来估计实际驾驶周期中的排放量。研究比较了两种建模方法:一种涉及独特的 "直接模型",另一种使用 "两阶段模型",即利用发动机和后处理的不同模型。研究结果表明,直接模型在简单性和准确性之间取得了最佳平衡。此外,该研究还调查了两种传感器设置:一种是标准配置,另一种是优化配置,即在主催化剂之后的排气管路中增加一个 lambda 探头。结果表明,引入第三个 lambda 探头后,氮氧化物和一氧化碳的估算性能显著提高,而碳氢化合物的结果则相对保持不变。此外,人工智能模型还在两种不同的电子控制单元(ECU)软件校准上进行了测试,结果均非常出色。这表明机器学习模型对控制软件的变化具有鲁棒性,可用于在虚拟环境中优化软件标定,从而减少对大量实验测试的依赖。此外,人工智能模型的性能证明了与实时实施的兼容性。总之,这项工作证明了人工智能技术在工业环境下准确估算发动机尾气排放的可行性和效率。这项研究展示了人工智能在排放估算和优化过程中的潜力,为创新工业实践提供了一条大有可为的途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AI-Based Virtual Sensing of Gaseous Pollutant Emissions at the Tailpipe of a High-Performance Vehicle
This scientific publication presents the application of artificial intelligence (AI) techniques as a virtual sensor for tailpipe emissions of CO, NOx, and HC in a high-performance vehicle. The study aims to address critical challenges faced in real industrial applications, including signal alignment and signal dynamics management. A comprehensive pre-processing pipeline is proposed to tackle these issues, and a light gradient-boosting machine (LightGBM) model is employed to estimate emissions during real driving cycles. The research compares two modeling approaches: one involving a unique “direct model” and another using a “two-stage model” which leverages distinct models for the engine and the aftertreatment. The findings suggest that the direct model strikes the best balance between simplicity and accuracy. Furthermore, the study investigates two sensor setups: a standard configuration and an optimized one, which incorporates an additional lambda probe in the exhaust line after the main catalyst. The results indicate a significant enhancement in performance for NOx and CO estimations with the introduction of the third lambda probe, while HC results remain relatively unchanged. Additionally, the AI model is tested on two different electronic control unit (ECU) software calibrations, yielding excellent results in both cases. This suggests that machine learning models are robust to control software variation and can be used to optimize software calibrations in a virtual environment, reducing the reliance on extensive experimental testing. Moreover, the AI model’s performance demonstrates compatibility with real-time implementation. In conclusion, this work establishes the viability and efficiency of AI techniques in accurately estimating tailpipe emissions from an engine in an industrial context. The study showcases the potential for AI to contribute to emission estimation and optimization processes, offering a promising pathway for an innovative industrial practice.
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来源期刊
SAE International Journal of Engines
SAE International Journal of Engines TRANSPORTATION SCIENCE & TECHNOLOGY-
CiteScore
2.70
自引率
8.30%
发文量
38
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