Evaluation of algorithms for temperature estimation in a crankcase

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Abstract

Ongoing research in emission reduction requires accurate load detection for combustion engines with a limited number of sensors. Therefore, fast estimation of load temperature is essential. Temperature measurements are influenced by the thermal properties of the sensor itself as well as its position and mounting method. In this paper, the transient thermal behavior of the engine's crankcase and the temperature sensor for load detection is modeled as a low-pass transfer function. The unknown parameters of the transfer function are identified from experimental measurements. Maximum likelihood estimation and Kalman filtering are used to estimate the original temperature from disturbed measurements. Estimator performance is evaluated via simulations of randomized test scenarios in a Monte Carlo fashion. Influences of the model errors, the measurement noise and the estimation window-time are investigated.

LanguageEnglish
Title of host publicationI2MTC 2018 - 2018 IEEE International Instrumentation and Measurement Technology Conference
Subtitle of host publicationDiscovering New Horizons in Instrumentation and Measurement, Proceedings
PublisherInstitute of Electrical and Electronics Engineers
Pages1-6
Number of pages6
ISBN (Electronic)9781538622223
DOIs
StatusPublished - 10 Jul 2018
Event2018 IEEE International Instrumentation and Measurement Technology Conference - Houston, United States
Duration: 14 May 201817 May 2018

Conference

Conference2018 IEEE International Instrumentation and Measurement Technology Conference
Abbreviated titleI2MTC
CountryUnited States
CityHouston
Period14/05/1817/05/18

Fingerprint

Crankcases
transfer functions
Transfer functions
engines
evaluation
Engines
Maximum likelihood estimation
sensors
Sensors
temperature sensors
Temperature sensors
mounting
noise measurement
Mountings
estimators
Temperature measurement
Temperature
temperature
temperature measurement
Thermodynamic properties

Keywords

  • estimation
  • temperature

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Instrumentation

Cite this

Rath, M., Piecha, P., & Neumayer, M. (2018). Evaluation of algorithms for temperature estimation in a crankcase. In I2MTC 2018 - 2018 IEEE International Instrumentation and Measurement Technology Conference: Discovering New Horizons in Instrumentation and Measurement, Proceedings (pp. 1-6). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/I2MTC.2018.8409663

Evaluation of algorithms for temperature estimation in a crankcase. / Rath, Matthias; Piecha, Pascal; Neumayer, Markus.

I2MTC 2018 - 2018 IEEE International Instrumentation and Measurement Technology Conference: Discovering New Horizons in Instrumentation and Measurement, Proceedings. Institute of Electrical and Electronics Engineers, 2018. p. 1-6.

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Rath, M, Piecha, P & Neumayer, M 2018, Evaluation of algorithms for temperature estimation in a crankcase. in I2MTC 2018 - 2018 IEEE International Instrumentation and Measurement Technology Conference: Discovering New Horizons in Instrumentation and Measurement, Proceedings. Institute of Electrical and Electronics Engineers, pp. 1-6, 2018 IEEE International Instrumentation and Measurement Technology Conference, Houston, United States, 14/05/18. https://doi.org/10.1109/I2MTC.2018.8409663
Rath M, Piecha P, Neumayer M. Evaluation of algorithms for temperature estimation in a crankcase. In I2MTC 2018 - 2018 IEEE International Instrumentation and Measurement Technology Conference: Discovering New Horizons in Instrumentation and Measurement, Proceedings. Institute of Electrical and Electronics Engineers. 2018. p. 1-6 https://doi.org/10.1109/I2MTC.2018.8409663
Rath, Matthias ; Piecha, Pascal ; Neumayer, Markus. / Evaluation of algorithms for temperature estimation in a crankcase. I2MTC 2018 - 2018 IEEE International Instrumentation and Measurement Technology Conference: Discovering New Horizons in Instrumentation and Measurement, Proceedings. Institute of Electrical and Electronics Engineers, 2018. pp. 1-6
@inproceedings{fb8d3acb3a1146419c5514c44a3fd4c9,
title = "Evaluation of algorithms for temperature estimation in a crankcase",
abstract = "Ongoing research in emission reduction requires accurate load detection for combustion engines with a limited number of sensors. Therefore, fast estimation of load temperature is essential. Temperature measurements are influenced by the thermal properties of the sensor itself as well as its position and mounting method. In this paper, the transient thermal behavior of the engine's crankcase and the temperature sensor for load detection is modeled as a low-pass transfer function. The unknown parameters of the transfer function are identified from experimental measurements. Maximum likelihood estimation and Kalman filtering are used to estimate the original temperature from disturbed measurements. Estimator performance is evaluated via simulations of randomized test scenarios in a Monte Carlo fashion. Influences of the model errors, the measurement noise and the estimation window-time are investigated.",
keywords = "estimation, temperature",
author = "Matthias Rath and Pascal Piecha and Markus Neumayer",
year = "2018",
month = "7",
day = "10",
doi = "10.1109/I2MTC.2018.8409663",
language = "English",
pages = "1--6",
booktitle = "I2MTC 2018 - 2018 IEEE International Instrumentation and Measurement Technology Conference",
publisher = "Institute of Electrical and Electronics Engineers",
address = "United States",

}

TY - GEN

T1 - Evaluation of algorithms for temperature estimation in a crankcase

AU - Rath, Matthias

AU - Piecha, Pascal

AU - Neumayer, Markus

PY - 2018/7/10

Y1 - 2018/7/10

N2 - Ongoing research in emission reduction requires accurate load detection for combustion engines with a limited number of sensors. Therefore, fast estimation of load temperature is essential. Temperature measurements are influenced by the thermal properties of the sensor itself as well as its position and mounting method. In this paper, the transient thermal behavior of the engine's crankcase and the temperature sensor for load detection is modeled as a low-pass transfer function. The unknown parameters of the transfer function are identified from experimental measurements. Maximum likelihood estimation and Kalman filtering are used to estimate the original temperature from disturbed measurements. Estimator performance is evaluated via simulations of randomized test scenarios in a Monte Carlo fashion. Influences of the model errors, the measurement noise and the estimation window-time are investigated.

AB - Ongoing research in emission reduction requires accurate load detection for combustion engines with a limited number of sensors. Therefore, fast estimation of load temperature is essential. Temperature measurements are influenced by the thermal properties of the sensor itself as well as its position and mounting method. In this paper, the transient thermal behavior of the engine's crankcase and the temperature sensor for load detection is modeled as a low-pass transfer function. The unknown parameters of the transfer function are identified from experimental measurements. Maximum likelihood estimation and Kalman filtering are used to estimate the original temperature from disturbed measurements. Estimator performance is evaluated via simulations of randomized test scenarios in a Monte Carlo fashion. Influences of the model errors, the measurement noise and the estimation window-time are investigated.

KW - estimation

KW - temperature

UR - http://www.scopus.com/inward/record.url?scp=85050767783&partnerID=8YFLogxK

U2 - 10.1109/I2MTC.2018.8409663

DO - 10.1109/I2MTC.2018.8409663

M3 - Conference contribution

SP - 1

EP - 6

BT - I2MTC 2018 - 2018 IEEE International Instrumentation and Measurement Technology Conference

PB - Institute of Electrical and Electronics Engineers

ER -