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Vehicle Engine Torque Estimation via Unknown Input Observer and Adaptive Parameter Estimation

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Vehicle Engine Torque Estimation via Unknown Input Observer and Adaptive Parameter Estimation. / Na, Jing; Chen, Anthony Siming; Herrmann, Guido; Burke, Richard; Brace, Chris.

In: IEEE Transactions on Vehicular Technology, Vol. 67, No. 1, 8010342, 01.01.2018, p. 409-422.

Research output: Contribution to journalArticle

Harvard

Na, J, Chen, AS, Herrmann, G, Burke, R & Brace, C 2018, 'Vehicle Engine Torque Estimation via Unknown Input Observer and Adaptive Parameter Estimation', IEEE Transactions on Vehicular Technology, vol. 67, no. 1, 8010342, pp. 409-422. https://doi.org/10.1109/TVT.2017.2737440

APA

Na, J., Chen, A. S., Herrmann, G., Burke, R., & Brace, C. (2018). Vehicle Engine Torque Estimation via Unknown Input Observer and Adaptive Parameter Estimation. IEEE Transactions on Vehicular Technology, 67(1), 409-422. [8010342]. https://doi.org/10.1109/TVT.2017.2737440

Vancouver

Na J, Chen AS, Herrmann G, Burke R, Brace C. Vehicle Engine Torque Estimation via Unknown Input Observer and Adaptive Parameter Estimation. IEEE Transactions on Vehicular Technology. 2018 Jan 1;67(1):409-422. 8010342. https://doi.org/10.1109/TVT.2017.2737440

Author

Na, Jing ; Chen, Anthony Siming ; Herrmann, Guido ; Burke, Richard ; Brace, Chris. / Vehicle Engine Torque Estimation via Unknown Input Observer and Adaptive Parameter Estimation. In: IEEE Transactions on Vehicular Technology. 2018 ; Vol. 67, No. 1. pp. 409-422.

Bibtex

@article{91d2b7ed0c594900a85fb529cea6e434,
title = "Vehicle Engine Torque Estimation via Unknown Input Observer and Adaptive Parameter Estimation",
abstract = "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.",
keywords = "Engine torque estimation, Mean value engine model, Time-varying parameter estimation, Unknown input observer",
author = "Jing Na and Chen, {Anthony Siming} and Guido Herrmann and Richard Burke and Chris Brace",
year = "2018",
month = "1",
day = "1",
doi = "10.1109/TVT.2017.2737440",
language = "English",
volume = "67",
pages = "409--422",
journal = "IEEE Transactions on Vehicular Technology",
issn = "0018-9545",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
number = "1",

}

RIS - suitable for import to EndNote

TY - JOUR

T1 - Vehicle Engine Torque Estimation via Unknown Input Observer and Adaptive Parameter Estimation

AU - Na, Jing

AU - Chen, Anthony Siming

AU - Herrmann, Guido

AU - Burke, Richard

AU - Brace, Chris

PY - 2018/1/1

Y1 - 2018/1/1

N2 - 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.

AB - 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.

KW - Engine torque estimation

KW - Mean value engine model

KW - Time-varying parameter estimation

KW - Unknown input observer

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DO - 10.1109/TVT.2017.2737440

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