Enhancing Lambda Measurement in Hydrogen-Fueled SI Engines through Virtual Sensor Implementation

Federico Ricci, Massimiliano Avana and Francesco Mariani ()
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Federico Ricci: Department of Engineering, University of Perugia, Via G. Duranti 93, 06125 Perugia, Italy
Massimiliano Avana: Department of Engineering, University of Perugia, Via G. Duranti 93, 06125 Perugia, Italy
Francesco Mariani: Department of Engineering, University of Perugia, Via G. Duranti 93, 06125 Perugia, Italy

Energies, 2024, vol. 17, issue 16, 1-17

Abstract: The automotive industry is increasingly challenged to develop cleaner, more efficient solutions to comply with stringent emission standards. Hydrogen (H 2 )-powered internal combustion engines (ICEs) offer a promising alternative, with the potential to reduce carbon-based emissions and improve efficiency. However, hydrogen combustion presents two main challenges related to the calibration process: emissions control and measurement of the air excess coefficient (λ). Traditional lambda sensors struggle with hydrogen’s combustion dynamics, leading to potential inefficiencies and increased pollutant emissions. Consequently, the determination of engine performance could also be compromised. This study explores the feasibility of using machine learning (ML) to replace physical lambda sensors with virtual ones in hydrogen-fueled ICEs. The research was conducted on a single-cylinder spark-ignition (SI) engine, collecting data across a range of air excess coefficients from 1.6 to 3.0. An advanced hybrid model combining long short-term memory (LSTM) networks and convolutional neural networks (CNNs) was developed and fine-tuned to accurately predict the air–fuel ratio; its predictive performance was compared to that obtained with the backpropagation (BP) architecture. The optimal configuration was identified through iterative experimentation, focusing on the neuron count, number of hidden layers, and input variables. The results demonstrate that the LSTM + 1DCNN model successfully converged without overfitting; it also showed better prediction ability in terms of accuracy and robustness when compared with the backpropagation approach.

Keywords: hydrogen fuel; SI engine; ultra-lean combustion; machine learning; virtual sensor (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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