Affiliation:
1. University of Bologna, Italy
2. Ferrari, Italy
Abstract
<div>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.</div>
Subject
Fuel Technology,Automotive Engineering,General Earth and Planetary Sciences,General Environmental Science