Data-driven algorithms for engine friction estimation

2007 ◽  
Vol 221 (7) ◽  
pp. 901-909 ◽  
Author(s):  
A A Stotsky
Keyword(s):  
Engine Speed ◽  
Control Unit ◽  
Spark Ignition ◽  
Friction Torque ◽  
Data Driven ◽  
Periodic Signal ◽  
Computationally Efficient ◽  
Engine Torque ◽  
Look Up Table ◽  
Engine Friction

Errors in the estimation of friction torque in modern spark ignition automotive engines necessitate the development of real-time algorithms for adaptation of the friction torque. Friction torque in the engine control unit is presented as a look-up table with two input variables (the engine speed and indicated engine torque). The algorithms proposed in this paper estimate the engine friction torque via the crankshaft speed fluctuations at the fuel cut-off state and at idle. A computationally efficient filtering algorithm for reconstruction of the first harmonic of a periodic signal is used to recover an amplitude which corresponds to engine events from the noise-contaminated engine speed measurements at the fuel cut-off state. The values of the friction torque at the nodes of the look-up table are updated, when new measured data of the friction torque are available. New data-driven algorithms which are based on a stepwise regression method are developed for adaptation of look-up tables. The algorithms are verified by using a spark ignition six-cylinder prototype engine.

scholarly journals Computationally efficient filtering algorithms for engine torque estimation

2005 ◽  
Vol 219 (9) ◽  
pp. 1099-1107 ◽  
Author(s):  
A Stotsky
Keyword(s):  
Engine Speed ◽  
Spark Ignition ◽  
Estimation Problem ◽  
Quality Information ◽  
Periodic Signal ◽  
Computationally Efficient ◽  
Filtering Algorithms ◽  
Engine Torque ◽  
Filtering Algorithm ◽  
Torque Estimation

A new computationally efficient filtering algorithm for the reconstruction of the first harmonic of a periodic signal is presented. The algorithm allows the recovery of the combustion quality information from the engine speed measurements that are noise contaminated. The algorithm is verified by using a spark ignition V8 engine in the torque estimation problem.

scholarly journals A Control-Oriented Engine Torque Online Estimation Approach for Gasoline Engines Based on In-Cycle Crankshaft Speed Dynamics

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4683
Author(s):  
Qiang Tong ◽  
Hui Xie ◽  
Kang Song ◽  
Dong Zou
Keyword(s):  
Real Time ◽  
Engine Speed ◽  
Estimation Error ◽  
Control Unit ◽  
Friction Torque ◽  
Adaptive Observer ◽  
Load Torque ◽  
Engine Torque ◽  
Brake Torque ◽  
Torque Estimation

Engine brake torque is a key feedback variable for the optimal torque split control of an engine–motor hybrid powertrain system. Due to the limitations in available sensors, however, engine torque is difficult to measure directly. For torque estimation, the unknown external load torque and the overlap of the expansion stroke between cylinders introduce a great disturbance to engine speed dynamics. This makes the conventional cycle average engine speed-based estimation approach unusable. In this article, an in-cycle crankshaft speed-based indicated torque estimation approach is proposed for a four-cylinder engine. First, a unique crankshaft angle window is selected for load torque estimation without the influence of combustion torque. Then, an in-cycle angle-domain crankshaft speed dynamic model is developed for engine indicated torque estimation. To account for the effects of model inaccuracy and unknown external disturbances, a “total disturbance” term is introduced. The total disturbance is then estimated by an adaptive observer using the engine’s historical operating data. Finally, a real-time correction method for the friction torque is proposed in the fuel cut-off scenario. Combining the aforementioned torque estimators, the brake torque can be obtained. The proposed algorithm is implemented in an in-house developed multi-core engine control unit (ECU). Experimental validation results on an engine test bench show that the algorithm’s execution time is about 3.2 ms, and the estimation error of the brake torque is within 5%. Therefore, the proposed method is a promising way to accurately estimate engine torque in real-time.

Misfire Detection of Spark Ignition Engines Using a New Technique Based on Mean Output Power

2015 ◽  
Vol 137 (9) ◽  
Author(s):  
M. Boudaghi ◽  
M. Shahbakhti ◽  
S. A. Jazayeri
Keyword(s):  
Steady State ◽  
Output Power ◽  
Engine Speed ◽  
Control Unit ◽  
Spark Ignition ◽  
New Technique ◽  
Si Engine ◽  
Engine Model ◽  
Wide Range ◽  
Misfire Detection

Control and detection of misfire are an essential part of on-board diagnosis (OBD) of modern spark ignition (SI) engines. This study proposes a novel model-based technique for misfire detection for a multicylinder SI engine. The new technique uses a dynamic engine model to determine mean output power, which is then used to calculate a new parameter for misfire detection. The new parameter directly relates to combustion period and is sensitive to engine speed fluctuations caused by misfire. The new technique requires only measured engine speed data and is computationally viable for use in a typical engine control unit (ECU). The new technique is evaluated experimentally on a four-cylinder 1.6-l SI engine. Three types of misfire are studied including single, continuous, and multiple-event. The steady-state and transient experiments were done for a wide range of engine speeds and engine loads, using a vehicle chassis dynamometer and on-road vehicle testing. The validation results show that the new technique is able to detect all three types of misfire with up to 94% accuracy during steady-state conditions. The new technique is augmented with a compensation factor to improve the accuracy of the technique for transient operations. The resulting technique is shown to be capable of detecting misfire during both transient and steady-state engine conditions.

scholarly journals Measurement of Impact of Air Filter Cleanness on a Change of Engine Speed Characteristics

2017 ◽  
Vol 5 (1) ◽  
pp. 21-23
Author(s):  
František Synák ◽  
◽  
Vladimír Rievaj
Keyword(s):  
Engine Speed ◽  
Spark Ignition ◽  
Spark Ignition Engine ◽  
Air Filter ◽  
Engine Torque ◽  
The Impact

The paper is focused on the impact of clogged air filter on a change of speed characteristics of spark-ignition engine 1.4 MPI, 16V, 74 kW. The clogged air filter can cause deterioration in engine charging. Less air means the possibility of burning smaller amount of fuel, and thus less energy brought to the engine. This should cause a change in the size of engine torque and its power.

Spark ignition engine torque management

2004 ◽  
Author(s):  
G.A. Ingram ◽  
M.A. Franchek ◽  
V. Balakrishnan ◽  
G. Surnilla
Keyword(s):  
Spark Ignition ◽  
Spark Ignition Engine ◽  
Engine Torque

Comparison of Random Forest and Neural Network in Modelling the Performance and Emissions of a Natural Gas Spark Ignition Engine

2021 ◽  
pp. 1-20
Author(s):  
Jinlong Liu ◽  
Qiao Huang ◽  
Christopher Ulishney ◽  
Cosmin E. Dumitrescu
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Natural Gas ◽  
Internal Combustion Engines ◽  
Engine Performance ◽  
Spark Ignition ◽  
Spark Ignition Engine ◽  
Data Driven ◽  
Ann Model ◽  
Mean Square Errors

Abstract Machine learning (ML) models can accelerate the development of efficient internal combustion engines. This study assessed the feasibility of data-driven methods towards predicting the performance of a diesel engine modified to natural gas spark ignition, based on a limited number of experiments. As the best ML technique cannot be chosen a priori, the applicability of different ML algorithms for such an engine application was evaluated. Specifically, the performance of two widely used ML algorithms, the random forest (RF) and the artificial neural network (ANN), in forecasting engine responses related to in-cylinder combustion phenomena was compared. The results indicated that both algorithms with spark timing, mixture equivalence ratio, and engine speed as model inputs produced acceptable results with respect to predicting engine performance, combustion phasing, and engine-out emissions. Despite requiring more effort in hyperparameter optimization, the ANN model performed better than the RF model, especially for engine emissions, as evidenced by the larger R-squared, smaller root-mean-square errors, and more realistic predictions of the effects of key engine control variables on the engine performance. However, in applications where the combustion behavior knowledge is limited, it is recommended to use a RF model to quickly determine the appropriate number of model inputs. Consequently, using the RF model to define the model structure and then employing the ANN model to improve the model's predictive capability can help to rapidly build data-driven engine combustion models.

scholarly journals Data-driven studies of magnetic two-dimensional materials

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Trevor David Rhone ◽  
Wei Chen ◽  
Shaan Desai ◽  
Steven B. Torrisi ◽  
Daniel T. Larson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Dft Calculations ◽  
Density Functional ◽  
Magnetic Coupling ◽  
Magnetic Ordering ◽  
Magnetic Behavior ◽  
Data Driven ◽  
Two Dimensional ◽  
Computationally Efficient ◽  
Data Driven Approach

Abstract We use a data-driven approach to study the magnetic and thermodynamic properties of van der Waals (vdW) layered materials. We investigate monolayers of the form $$\hbox {A}_2\hbox {B}_2\hbox {X}_6$$ A 2 B 2 X 6 , based on the known material $$\hbox {Cr}_2\hbox {Ge}_2\hbox {Te}_6$$ Cr 2 Ge 2 Te 6 , using density functional theory (DFT) calculations and machine learning methods to determine their magnetic properties, such as magnetic order and magnetic moment. We also examine formation energies and use them as a proxy for chemical stability. We show that machine learning tools, combined with DFT calculations, can provide a computationally efficient means to predict properties of such two-dimensional (2D) magnetic materials. Our data analytics approach provides insights into the microscopic origins of magnetic ordering in these systems. For instance, we find that the X site strongly affects the magnetic coupling between neighboring A sites, which drives the magnetic ordering. Our approach opens new ways for rapid discovery of chemically stable vdW materials that exhibit magnetic behavior.

Friction Torque and Leakage Based Data-Driven Approach for Rotary Seal Prognostics in Manufacturing Industry

2019 ◽  
Author(s):  
Madhumitha Ramachandran ◽  
Zahed Siddique
Keyword(s):  
Manufacturing Industry ◽  
Friction Torque ◽  
Operating Conditions ◽  
Oil Field ◽  
Data Driven ◽  
Sensor Technology ◽  
Physical Parameters ◽  
Direct Estimation ◽  
Rotary Seal ◽  
Wide Range

Abstract Rotary seals are found in many manufacturing equipment and machines used for various applications under a wide range of operating conditions. Rotary seal failure can be catastrophic and can lead to costly downtime and large expenses; so it is extremely important to assess the degradation of rotary seal to avoid fatal breakdown of machineries. Physics-based rotary seal prognostics require direct estimation of different physical parameters to assess the degradation of seals. Data-driven prognostics utilizing sensor technology and computational capabilities can aid in the in-direct estimation of rotary seals’ running condition unlike the physics-based approach. An important aspect of data-driven prognostics is to collect appropriate data in order to reduce the cost and time associated with the data collection, storage and computation. Seals in machineries operate in harsh conditions, especially in the oil field, seals are exposed to harsh environment and aggressive fluids which gradually reduces the elastic modulus and hardness of seals, resulting in lower friction torque and excessive leakage. Therefore, in this study we implement a data-driven prognostics approach which utilizes friction torque and leakage signals along with Multilayer Perceptron as a classifier to compare the performance of the two metrics in classifying the running condition of rotary seals. Friction torque was found to have a better performance than leakage in terms of differentiating the running condition of rotary seals throughout its service life. Although this approach was designed for seals in oil and gas industry, this approach can be implemented in any manufacturing industry with similar applications.

Sign in / Sign up

Export Citation Format

Share Document