The Experts below are selected from a list of 27909 Experts worldwide ranked by ideXlab platform
Chris Brace - One of the best experts on this subject based on the ideXlab platform.
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Vehicle Engine Torque Estimation via Unknown Input Observer and Adaptive Parameter Estimation
IEEE Transactions on Vehicular Technology, 2018Co-Authors: Jing Na, Anthony Siming Chen, Guido Herrmann, Richard Burke, Chris BraceAbstract:This paper presents two Torque estimation methods for vehicle Engines: unknown input observer (UIO) and adaptive parameter estimation. We first propose a novel yet simple unknown input observer based on the crankshaft rotation dynamics only. For this purpose, an invariant manifold is derived by defining auxiliary variables in terms of first-order low-pass filters, where only one constant (filter coefficient) needs to be tuned. These filtered variables are used to calculate the estimated Torque. Robustness of this UIO against sensor noise is studied and compared to two other estimators. On the other hand, since the Engine Torque dynamics can be formulated as a parameterized form with unknown time-varying parameters, we further present several adaptive laws for time-varying parameter estimation. The parameter estimation errors are derived to drive these adaptive laws and time-varying adaptive gains are introduced. The two proposed estimators only use the measured air mass flow rate and Engine speed, and thus allow for improved computational efficiency. Both estimators are verified via a dynamic Engine simulator built in a commercial software GT-Power, and also practically tested via experimental data collected in a dynamometer test-rig. Both simulations and practical tests show very encouraging results with small estimation errors even in the presence of sensor noise.
Zissimos P. Mourelatos - One of the best experts on this subject based on the ideXlab platform.
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Optimization of Engine Torque Management Under Uncertainty for Vehicle Driveline Clunk Using Time-Dependent Metamodels
Journal of Mechanical Design, 2009Co-Authors: Daniel N. Wehrwein, Zissimos P. MourelatosAbstract:Quality and performance are two important customer requirements in vehicle design. Driveline clunk negatively affects the perceived quality and must be minimized. This can be achieved using Engine Torque management, which is part of Engine calibration. During a tip-in event, the Engine Torque rate of rise is limited until all the driveline lash is taken up. The Engine Torque rate of rise can negatively affect the vehicle throttle response, which determines performance. The Engine Torque management must be therefore balanced against throttle response. In practice, the Engine Torque rate of rise is calibrated manually. This article describes an analytical methodology for calibrating the Engine Torque considering uncertainty, in order to minimize clunk, while still meeting throttle response constraints. A set of predetermined Engine Torque profiles are considered, which span the practical range of interest. The transmission turbine speed is calculated for each profile using a bond graph vehicle model. Clunk is quantified by the magnitude of the turbine speed spike. Using the Engine Torque profiles and the corresponding turbine speed responses, a time-dependent metamodel is created using principal component analysis and kriging. The metamodel predicts the turbine speed response due to any Engine Torque profile and is used in deterministic and reliability-based optimizations to minimize clunk. Compared with commonly used production calibration, the clunk disturbance is reduced substantially without greatly affecting the vehicle throttle response.
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Optimization of Engine Torque Management Under Uncertainty for Vehicle Driveline Clunk Using Time-Dependent Metamodels
Volume 1: 34th Design Automation Conference Parts A and B, 2008Co-Authors: Daniel N. Wehrwein, Zissimos P. MourelatosAbstract:Quality and performance are two important customer requirements in vehicle design. Driveline clunk negatively affects the perceived quality and must be therefore, minimized. This is usually achieved using Engine Torque management, which is part of Engine calibration. During a tip-in event, the Engine Torque rate of rise is limited until all the driveline lash is taken up. However, the Engine Torque rise, and its rate can negatively affect the vehicle throttle response which determines performance. Therefore, the Engine Torque management must be balanced against throttle response. In practice, the Engine Torque rate of rise is calibrated manually. This paper describes an analytical methodology for calibrating the Engine Torque, considering uncertainty, in order to minimize the clunk disturbance, while still meeting throttle response constraints. A set of predetermined Engine Torque profiles which span the practical range of interest, are used and the transmission turbine speed is calculated for each profile using a bond-graph vehicle model. The turbine speed quantifies the clunk disturbance. Using the Engine Torque profiles and the corresponding turbine speed responses, a time-dependent metamodel is created using principal component analysis and Kriging. The metamodel predicts the turbine speed response due to any Engine Torque profile and is used in a deterministic and reliability-based optimization which minimizes the clunk disturbance while still meeting the throttle response target. Compared with commonly used production calibration, the clunk disturbance is reduced substantially without negatively affecting the vehicle throttle response.Copyright © 2008 by ASME
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Optimal Engine Torque management for reducing driveline clunk using time–dependent metamodels
SAE Technical Paper Series, 2007Co-Authors: Zissimos P. Mourelatos, Daniel N. WehrweinAbstract:Driveline clunk negatively affects the perceived vehicle quality and must be minimised. This is usually achieved using Engine Torque management which must be balanced against throttle response. In practice, the Engine Torque rate of rise is calibrated manually. This paper describes a methodology for calibrating the Engine Torque to minimise the clunk disturbance, while still meeting throttle response constraints. Using a set of Engine Torque profiles and the corresponding turbine speed responses, a time-dependent metamodel is created using principal component analysis and kriging. The metamodel predicts the turbine speed response due to any Engine Torque profile and is used in a subsequent optimisation to minimise a clunk disturbance measure while still meeting the throttle response target. The optimal Engine Torque profile and corresponding turbine speed were successfully validated experimentally. We reduced the clunk disturbance by 33% while improving the throttle response by 11%.
Hao Ying - One of the best experts on this subject based on the ideXlab platform.
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Direct Torque Feedback for Accurate Engine Torque Delivery and Improved Powertrain Performance
Journal of Engineering for Gas Turbines and Power, 2016Co-Authors: Anwar Alkeilani, Le Yi Wang, Hao YingAbstract:At the present time, both control and estimation accuracies of Engine Torque are causes for under-achieving optimal drivability and performance in today’s production vehicles. The major focus in this area has been to enhance Torque estimation and control accuracies using existing open-loop Torque control and estimation structures. Such an approach does not guarantee optimum Torque tracking accuracy and optimum estimation accuracy due to air flow and efficiencies estimations errors. Furthermore, current approach overlooks the fast Torque path tracking which does not have any related feedback. Recently, explicit Torque feedback control has been proposed in the literature using either estimated or measured Torques as feedback to control the Torque using the slow Torque path only. We propose the usage of a surface acoustic wave (SAW) Torque sensor to measure the Engine brake Torque and feedback the signal to control the Torque using both the fast and slow Torque paths utilizing an inner-outer loop control structure. The fast Torque path feedback is coordinated with the slow Torque path by a novel method using the potential Torque that is adapted to the sensor reading. The Torque sensor signal enables a fast and explicit Torque feedback control that can correct Torque estimation errors and improve drivability, emission control, and fuel economy. Control-oriented Engine models for the 3.6L Engine are developed. Computer simulations are performed to investigate the advantages and limitations of the proposed control strategy, versus the existing strategies. The findings include an improvement of 14% in gain margin and 60% in phase margin when the Torque feedback is applied to the cruise control Torque request at the simulated operating point. This study demonstrates that the direct Torque feedback is a powerful technology with promising results for improved powertrain performance and fuel economy.Copyright © 2015 by ASME
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Direct Torque Feedback for Accurate Engine Torque Delivery and Improved Powertrain Performance
Volume 2: Emissions Control Systems; Instrumentation Controls and Hybrids; Numerical Simulation; Engine Design and Mechanical Development, 2015Co-Authors: Anwar Alkeilani, Le Yi Wang, Hao YingAbstract:At the present time, both control and estimation accuracies of Engine Torque are causes for under-achieving optimal drivability and performance in today’s production vehicles. The major focus in this area has been to enhance Torque estimation and control accuracies using existing open-loop Torque control and estimation structures. Such an approach does not guarantee optimum Torque tracking accuracy and optimum estimation accuracy due to air flow and efficiencies estimations errors. Furthermore, current approach overlooks the fast Torque path tracking which does not have any related feedback. Recently, explicit Torque feedback control has been proposed in the literature using either estimated or measured Torques as feedback to control the Torque using the slow Torque path only. We propose the usage of a surface acoustic wave (SAW) Torque sensor to measure the Engine brake Torque and feedback the signal to control the Torque using both the fast and slow Torque paths utilizing an inner-outer loop control structure. The fast Torque path feedback is coordinated with the slow Torque path by a novel method using the potential Torque that is adapted to the sensor reading. The Torque sensor signal enables a fast and explicit Torque feedback control that can correct Torque estimation errors and improve drivability, emission control, and fuel economy. Control-oriented Engine models for the 3.6L Engine are developed. Computer simulations are performed to investigate the advantages and limitations of the proposed control strategy, versus the existing strategies. The findings include an improvement of 14% in gain margin and 60% in phase margin when the Torque feedback is applied to the cruise control Torque request at the simulated operating point. This study demonstrates that the direct Torque feedback is a powerful technology with promising results for improved powertrain performance and fuel economy.
Jing Na - One of the best experts on this subject based on the ideXlab platform.
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Vehicle Engine Torque Estimation via Unknown Input Observer and Adaptive Parameter Estimation
IEEE Transactions on Vehicular Technology, 2018Co-Authors: Jing Na, Anthony Siming Chen, Guido Herrmann, Richard Burke, Chris BraceAbstract:This paper presents two Torque estimation methods for vehicle Engines: unknown input observer (UIO) and adaptive parameter estimation. We first propose a novel yet simple unknown input observer based on the crankshaft rotation dynamics only. For this purpose, an invariant manifold is derived by defining auxiliary variables in terms of first-order low-pass filters, where only one constant (filter coefficient) needs to be tuned. These filtered variables are used to calculate the estimated Torque. Robustness of this UIO against sensor noise is studied and compared to two other estimators. On the other hand, since the Engine Torque dynamics can be formulated as a parameterized form with unknown time-varying parameters, we further present several adaptive laws for time-varying parameter estimation. The parameter estimation errors are derived to drive these adaptive laws and time-varying adaptive gains are introduced. The two proposed estimators only use the measured air mass flow rate and Engine speed, and thus allow for improved computational efficiency. Both estimators are verified via a dynamic Engine simulator built in a commercial software GT-Power, and also practically tested via experimental data collected in a dynamometer test-rig. Both simulations and practical tests show very encouraging results with small estimation errors even in the presence of sensor noise.
Guido Herrmann - One of the best experts on this subject based on the ideXlab platform.
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Vehicle Engine Torque Estimation via Unknown Input Observer and Adaptive Parameter Estimation
IEEE Transactions on Vehicular Technology, 2018Co-Authors: Jing Na, Anthony Siming Chen, Guido Herrmann, Richard Burke, Chris BraceAbstract:This paper presents two Torque estimation methods for vehicle Engines: unknown input observer (UIO) and adaptive parameter estimation. We first propose a novel yet simple unknown input observer based on the crankshaft rotation dynamics only. For this purpose, an invariant manifold is derived by defining auxiliary variables in terms of first-order low-pass filters, where only one constant (filter coefficient) needs to be tuned. These filtered variables are used to calculate the estimated Torque. Robustness of this UIO against sensor noise is studied and compared to two other estimators. On the other hand, since the Engine Torque dynamics can be formulated as a parameterized form with unknown time-varying parameters, we further present several adaptive laws for time-varying parameter estimation. The parameter estimation errors are derived to drive these adaptive laws and time-varying adaptive gains are introduced. The two proposed estimators only use the measured air mass flow rate and Engine speed, and thus allow for improved computational efficiency. Both estimators are verified via a dynamic Engine simulator built in a commercial software GT-Power, and also practically tested via experimental data collected in a dynamometer test-rig. Both simulations and practical tests show very encouraging results with small estimation errors even in the presence of sensor noise.
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Engine Torque estimation with integrated unknown input observer and adaptive parameter estimator
IFAC-PapersOnLine, 2017Co-Authors: Thomas C. Harding, Clement Rames, Huang Yu Teh, Toby Mill, Anthony Siming Chen, Guido HerrmannAbstract:Abstract This paper presents an integrated estimation scheme for the effective Engine Torque in automotive systems. This leads to a cascaded estimation structure, composed of an adaptive parameter estimator for the augmented wheel dynamics and the longitudinal motion, and an unknown input observer for the Engine crankshaft dynamics. The adaptive parameter estimator has the ability to track time-varying parameters and can therefore provide an estimate of the driving Torque for the wheels. Then this estimated Torque is transmitted to the Engine as the load Torque through the drivetrain, and is used to design the unknown input observer. The standard models of driveline and tyre friction are modified for ease of parameter estimation. Only the Engine crankshaft velocity, the wheel rotational velocity, and the vehicle longitudinal speed are needed. The convergence of these estimators is analyzed. Simulations based on a dynamic simulator built with commercial vehicular simulation software, IPG CarMaker, and Matlab/Simulink show promising results.