Model-Based Angular Position Sensorless Drives of Main Electric Oil Pumps for e-Axles in HEV and BEV

Chinchul Choi () and Jongbeom Kim
Additional contact information
Chinchul Choi: Department of Control and Instrumentation Engineering, Changwon National University, Changwon 51140, Republic of Korea
Jongbeom Kim: R&D Center, Myunghwa Ind. Co., Ltd., Seoul 08505, Republic of Korea

Energies, 2024, vol. 17, issue 19, 1-14

Abstract: This paper describes an approach in improving the performance of the position sensorless control of electric oil pumps with a permanent magnet synchronous motor. Electric oil pumps are widely applied for the lubricating and cooling of e-Axles in HEV and BEV which operate from −40 to 130 °C. The accuracy of the estimation obtained from the sensorless control based on the motor model depends on the accuracy of motor parameters and input values. At a lower speed and lower temperature region, the parameter variation and input measurement errors have gained greater influence over the accuracy of the estimation. This paper describes how to overcome this weakness of the sensorless drive via applying a robust position estimator with electrical parameter adaptation and compensation of a phase voltage measurement error. Experimental results with various types of pumps show the effectiveness of the proposed method.

Keywords: permanent magnet synchronous motor; parameter estimation; motor control; sensorless; electric vehicle (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/19/4962/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/19/4962/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:17:y:2024:i:19:p:4962-:d:1492076

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().