Connected Vehicle Based Distributed Meta-Learning for Online Adaptive Engine/Powertrain Fuel Consumption Modeling

Xin, Ma; Shahbakhti, Mahdi; Chigan, Chunxiao

doi:10.1109/tvt.2020.3002491

Cited by 10 publications

(5 citation statements)

References 42 publications

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“…This paper builds upon our prior experience for the design and implementation of model-based ICE controllers [2,4,5,7,21,22,46,. In this review paper, we searched for relevant papers in the field of MPC and ICE using the Scopus, Web of Science, IEEE Xplore, ScienceDirect, Springer, SAE, ASME, Wiley, Taylor & Francis, arXiv, and SAGE databases.…”

Section: Scope Of the Papermentioning

confidence: 99%

“…Internal Combustion Engines (ICEs) are widely used for small power applications, such as lawnmower and string trimmers, to large applications, such as power generation and commercial transportation, such as ships, locomotives, and heavy-duty trucks [1][2][3][4][5][6][7][8][9][10]. Due to widespread and broad application, ICEs contribute more than 20% of total GHG (greenhouse gas) emissions in the world [11].…”

Section: Introduction 1progress In Engine Controlmentioning

confidence: 99%

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Model Predictive Control of Internal Combustion Engines: A Review and Future Directions

et al. 2021

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An internal combustion engine (ICE) is a highly nonlinear dynamic and complex engineering system whose operation is constrained by operational limits, including emissions, noise, peak in-cylinder pressure, combustion stability, and actuator constraints. To optimize today’s ICEs, seven to ten control actuators and 10–20 feedback sensors are often used, depending on the engine applications and target emission regulations. This requires extensive engine experimentation to calibrate the engine control module (ECM), which is both cumbersome and costly. Despite these efforts, optimal operation, particularly during engine transients and to meet real driving emission (RDE) targets for broad engine speed and load conditions, has still not been obtained. Methods of model predictive control (MPC) have shown promising results for real-time multi-objective optimal control of constrained multi-variable nonlinear systems, including ICEs. This paper reviews the application of MPC for ICEs and analyzes the recent developments in MPC that can be utilized in ECMs. ICE control and calibration can be enhanced by taking advantage of the recent developments in the field of Artificial Intelligence (AI) in applying Machine Learning (ML) to large-scale engine data. Recent developments in the field of ML-MPC are investigated, and promising methods for ICE control applications are identified in this paper.

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Section: Scope Of the Papermentioning

confidence: 99%

Section: Introduction 1progress In Engine Controlmentioning

confidence: 99%

Model Predictive Control of Internal Combustion Engines: A Review and Future Directions

et al. 2021

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“…A method for dynamic fuel consumption prediction was proposed and tested with dynamic data both from shorter time intervals and longer time intervals [14]. This study [15] thus proposed an adaptive dynamic fuel consumption model. Based on distributed meta-regression algorithms.…”

Section: Introductionmentioning

confidence: 99%

A novel data-driven method for fuel-consumption prediction based on fast converged kernel extreme learning machine

Wang

2023

Meas. Sci. Technol.

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An accurate fuel consumption prediction is significant to fuel-economy-oriented optimization, which can improve vehicle fuel economy. Based on the data-driven prediction framework, this paper proposes a Real-time Fuel Consumption Prediction Model (RFCPM) using Kernel Extreme Learning Machine (KELM) which is optimized by Fast Converged Grey Wolf Algorithm (FCGWA). A new steering function of FCGWA and a new activation function for KELM are presented to ensure the fast converge speed and higher accuracy performance. First, the characteristic variables of RFCPM are selected by reference to the model-based fuel consumption prediction method. Second, a KELM is adopted to predict fuel consumption. Third, FCGWA is adopted to select the best parameters of KELM using k-fold cross-validation method. Finally, the best model is chosen through real-vehicle test. Test results are compared with the original ELM and the wildly used WNN. The experiment shows that the proposed method significantly outperforms the original ELM and WNN in terms of both prediction accuracy and training time.

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“…Internal combustion engines (ICEs) have been widely employed in a wide range of applications, such as power production and commercial transportation (heavy-duty trucks, locomotives, and ships). [1][2][3] ICEs have a few advantages; however, the harmful pollutants from the fuel combustion process inside the cylinder are substantial sources of air pollution and pose severe carcinogenic dangers to human health. 4,5 Typical air pollutants, such as carbon dioxide, soot, sulfur, and oxides of nitrogen, have adverse effects on environmental and human health consequences.…”

Section: Introductionmentioning

confidence: 99%

Determination of engine performance and harmful pollutants of a spark-ignition engine fueled with higher-order alcohol/gasoline blends by engine simulation

Ghanati

Doğan

Yeşilyurt

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part E:

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In this study, the performance and exhaust emissions of a spark-ignition (SI) engine were simulated using AVL program, and the outcomes were compared with the results coming from experiments. The simulated engine was operated at a constant speed (1600 rpm) and various engine powers with gasoline (G100), and it blends with different higher-order alcohols such as 1-hexanol (HEX) and 1-heptanol (HP) as new fuel combinations. The proportions of tests fuel combinations were G100, G100 + HEX (5, 10, 15, and 20%) and G100 + HP (5, 10, 15, and 20%). The experimental study showed that the highest brake-specific fuel consumption was calculated to be 0.625 kg/kWh using HP20 fuel at 1 kW of engine power, while it was found to be 0.598 kg/kWh in the numerical study. The experimental research indicated that the lowest CO emission was emitted to be 0.28% in HEX20 fuel at 5 kW of engine power. Under the same condition, it was found 0.26% in the simulation study. The highest NOx emission was measured to be 1349.8 ppm in HEX20 fuel at 5 kW of the engine power. Meanwhile, 1318.3 ppm was found in the simulation. When the simulation outcomes were compared with the experimental study results, the simulation results were in valid. The difference in brake-specific fuel consumption results between experimental and numerical research ascended as the engine power jumped up. Furthermore, reductions were observed in the amount of difference in the results related to emissions between experimental and simulation studies at higher engine powers.

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Connected Vehicle Based Distributed Meta-Learning for Online Adaptive Engine/Powertrain Fuel Consumption Modeling

Cited by 10 publications

References 42 publications

Model Predictive Control of Internal Combustion Engines: A Review and Future Directions

Model Predictive Control of Internal Combustion Engines: A Review and Future Directions

A novel data-driven method for fuel-consumption prediction based on fast converged kernel extreme learning machine

Determination of engine performance and harmful pollutants of a spark-ignition engine fueled with higher-order alcohol/gasoline blends by engine simulation

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