Integrating machine learning and model predictive control for automotive applications: A review and future directions
In this review paper, the integration of Machine Learning (ML) and Model Predictive Control
(MPC) in Automotive Control System (ACS) applications are discussed. ACS can be divided …
(MPC) in Automotive Control System (ACS) applications are discussed. ACS can be divided …
Deep learning based model predictive control for compression ignition engines
Abstract Machine learning (ML) and a nonlinear model predictive controller (NMPC) are
used in this paper to minimize the emissions and fuel consumption of a compression ignition …
used in this paper to minimize the emissions and fuel consumption of a compression ignition …
Laminar flame speed modeling for low carbon fuels using methods of machine learning
S Shahpouri, A Norouzi, C Hayduk, A Fandakov… - Fuel, 2023 - Elsevier
Abstract Artificial Neural Network (ANN) and Support Vector Machine (SVM) methods are
designed to accurately predict Laminar Flame Speed (LFS) over the entire engine operating …
designed to accurately predict Laminar Flame Speed (LFS) over the entire engine operating …
Application of the Gaussian process regression method based on a combined kernel function in engine performance prediction
X Shi, D Jiang, W Qian, Y Liang - ACS omega, 2022 - ACS Publications
At present, regression modeling methods fail to achieve higher simulation accuracy, which
limits the application of simulation technology in more fields such as virtual calibration and …
limits the application of simulation technology in more fields such as virtual calibration and …
The prediction of spark-ignition engine performance and emissions based on the SVR algorithm
Y Zhang, Q Wang, X Chen, Y Yan, R Yang, Z Liu, J Fu - Processes, 2022 - mdpi.com
Engine development needs to reduce costs and time. As the current main development
methods, 1D simulation has the limitations of low accuracy, and 3D simulation is a long, time …
methods, 1D simulation has the limitations of low accuracy, and 3D simulation is a long, time …
Hybrid emission and combustion modeling of hydrogen fueled engines
Zero carbon fuels can be used to reduce CO 2 emissions from internal combustion engines.
Hydrogen is an important zero-carbon fuel that can be used as the primary fuel for spark …
Hydrogen is an important zero-carbon fuel that can be used as the primary fuel for spark …
Safe deep reinforcement learning in diesel engine emission control
A deep reinforcement learning application is investigated to control the emissions of a
compression ignition diesel engine. The main purpose of this study is to reduce the engine …
compression ignition diesel engine. The main purpose of this study is to reduce the engine …
Machine learning integrated with model predictive control for imitative optimal control of compression ignition engines
The high thermal efciency and reliability of the compression-ignition engine makes it the first
choice for many applications. For this to continue, a reduction of the pollutant emissions is …
choice for many applications. For this to continue, a reduction of the pollutant emissions is …
Beam offset detection in laser stake welding of Tee Joints using machine learning and spectrometer measurements
Laser beam welding offers high productivity and relatively low heat input and is one key
enabler for efficient manufacturing of sandwich constructions. However, the process is …
enabler for efficient manufacturing of sandwich constructions. However, the process is …
Artificial intelligence strategies for the development of robust virtual sensors: an industrial case for transient particle emissions in a high-performance engine
L Pulga, C Forte, A Siliato, E Giovannardi… - … International Journal of …, 2023 - sae.org
The use of data-driven algorithms for the integration or substitution of current production
sensors is becoming a consolidated trend in research and development in the automotive …
sensors is becoming a consolidated trend in research and development in the automotive …