Sensor Range Sensitivity of Predictive Energy Management in Plug-In Hybrid Vehicles

Martin Hofstetter, Martin Ackerl, Mario Hirz, Harald Kraus, Paul Karoshi, Jürgen Fabian

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

Abstract

The fuel saving potential of passenger plug-in hybrid vehicles (PHEVs) is presented as a function of sensor prediction range. The route is assumed to be given, while the upcoming vehicle speed is uncertain and requires prediction. The proposed prediction method uses up-to-date sensor information inside the prediction horizon and rough estimations through road inclination and speed limits beyond that prediction horizon. The entire route is thus considered in the optimization process. A deterministic Dynamic Programming algorithm then uses the speed- and acceleration-related power demand estimation to find the optimal torque-split control signals for the hybrid powertrain - the vehicle speed is not affected. The research results show that achieving significant fuel saving potential is relatively insensitive to the prediction range. Even relatively short prediction ranges within radar sensor range enable fuel economy very close to the global-optimal solution. The simulation results are compared with a simple charge-depleting strategy using the same amount of electrical energy to show the fuel saving potential.

Original languageEnglish
Title of host publication2015 IEEE Conference on Control and Applications, CCA 2015 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers
Pages1925-1932
Number of pages8
ISBN (Electronic)9781479977871
DOIs
Publication statusPublished - 4 Nov 2015
EventIEEE Conference on Control and Applications, CCA 2015 - Sydney, Australia
Duration: 21 Sep 201523 Sep 2015

Conference

ConferenceIEEE Conference on Control and Applications, CCA 2015
CountryAustralia
CitySydney
Period21/09/1523/09/15

Fingerprint

Plug-in hybrid vehicles
Energy management
Sensors
Hybrid powertrains
Torque control
Fuel economy
Dynamic programming
Radar

ASJC Scopus subject areas

  • Control and Systems Engineering

Fields of Expertise

  • Mobility & Production

Cite this

Hofstetter, M., Ackerl, M., Hirz, M., Kraus, H., Karoshi, P., & Fabian, J. (2015). Sensor Range Sensitivity of Predictive Energy Management in Plug-In Hybrid Vehicles. In 2015 IEEE Conference on Control and Applications, CCA 2015 - Proceedings (pp. 1925-1932). [7320891] Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/CCA.2015.7320891

Sensor Range Sensitivity of Predictive Energy Management in Plug-In Hybrid Vehicles. / Hofstetter, Martin; Ackerl, Martin; Hirz, Mario; Kraus, Harald; Karoshi, Paul; Fabian, Jürgen.

2015 IEEE Conference on Control and Applications, CCA 2015 - Proceedings. Institute of Electrical and Electronics Engineers, 2015. p. 1925-1932 7320891.

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

Hofstetter, M, Ackerl, M, Hirz, M, Kraus, H, Karoshi, P & Fabian, J 2015, Sensor Range Sensitivity of Predictive Energy Management in Plug-In Hybrid Vehicles. in 2015 IEEE Conference on Control and Applications, CCA 2015 - Proceedings., 7320891, Institute of Electrical and Electronics Engineers, pp. 1925-1932, IEEE Conference on Control and Applications, CCA 2015, Sydney, Australia, 21/09/15. https://doi.org/10.1109/CCA.2015.7320891
Hofstetter M, Ackerl M, Hirz M, Kraus H, Karoshi P, Fabian J. Sensor Range Sensitivity of Predictive Energy Management in Plug-In Hybrid Vehicles. In 2015 IEEE Conference on Control and Applications, CCA 2015 - Proceedings. Institute of Electrical and Electronics Engineers. 2015. p. 1925-1932. 7320891 https://doi.org/10.1109/CCA.2015.7320891
Hofstetter, Martin ; Ackerl, Martin ; Hirz, Mario ; Kraus, Harald ; Karoshi, Paul ; Fabian, Jürgen. / Sensor Range Sensitivity of Predictive Energy Management in Plug-In Hybrid Vehicles. 2015 IEEE Conference on Control and Applications, CCA 2015 - Proceedings. Institute of Electrical and Electronics Engineers, 2015. pp. 1925-1932
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