DOAJ Open Access 2023

Machine-Learning-Based Digital Twins for Transient Vehicle Cycles and Their Potential for Predicting Fuel Consumption

Eduardo Tomanik Antonio J. Jimenez-Reyes Victor Tomanik Bernardo Tormos

Abstrak

Transient car emission tests generate huge amount of test data, but their results are usually evaluated only using their “accumulated” cycle values according to the homologation limits. In this work, two machine learning models were developed and applied to a truck RDE test and two light-duty vehicle chassis emission tests. Different from the conventional approach, the engine parameters and fuel consumption were acquired from the Engine Control Unit, not from the test measurement equipment. Instantaneous engine values were used as input in machine-learning-based digital twins. This novel approach allows for much less costly vehicle tests and optimizations. The paper’s novel approach and developed digital twins model were able to predict both instantaneous and accumulated fuel consumption with good accuracy, and also for tests cycles different to the one used to train the model.

Topik & Kata Kunci

Mechanical engineering and machinery Machine design and drawing Motor vehicles. Aeronautics. Astronautics

Penulis (4)

Eduardo Tomanik

Antonio J. Jimenez-Reyes

Victor Tomanik

Bernardo Tormos

Format Sitasi

APA MLA BibTeX

Tomanik, E., Jimenez-Reyes, A.J., Tomanik, V., Tormos, B. (2023). Machine-Learning-Based Digital Twins for Transient Vehicle Cycles and Their Potential for Predicting Fuel Consumption. https://doi.org/10.3390/vehicles5020032

Akses Cepat

PDF tidak tersedia langsung

Cek di sumber asli →

Lihat di Sumber doi.org/10.3390/vehicles5020032

Informasi Jurnal

Tahun Terbit: 2023
Sumber Database: DOAJ
DOI: 10.3390/vehicles5020032
Akses: Open Access ✓