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Development of Electric Oil Pump Controller in Hybrid Vehicle Based on PMSM (Permanent Magnet Synchronous Motor) and the AUTOSAR Platform
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 02, 2019 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Traditional hybrid vehicles operated two types of oil pump, mechanical for combustion engine and electrical for electric motor, to supply oil pressure for automatic transmission in each power source. New hybrid vehicles has only one electrical pump to reduce system volume and to improve fuel efficiency. Purposes of this study are to make standardization of firmware development process for oil pump unit (OPU) and electric oil pump (EOP) and to apply permanent magnet synchronous motor (PMSM) instead of brushless DC (BLDC) motor. In old-fashioned non-operating system (OS) firmware, it was convenient to calculate control timing for motor control, however the firmware was deeply dependent to specific microcontroller Unit (MCU). In other words, much of efforts and time are needed if new MCU were applied. In this study, new standard development process for MCU firmware can be built up based on automotive open system architecture (AUTOSAR) platform by developing specific motor complex device driver (CDD). In existing system, the OPU supplied with high-voltage from battery drives the BLDC motor driven with six-step block commutation method through the hall sensor inputs. In new system of this study, sensorless vector control algorithm for PMSM is applied to improve motor performance and sensorless algorithm helps to reduce failure risk caused by position sensor.
|Technical Paper||Investigation of the Hybrid Operating Modes Regarding Efficiency, Emissions and Comfort for the Parallel-Series Hybrid Powertrain Concept DE-REX|
CitationKim, H., Ryu, S., Yoo, H., Kim, H. et al., "Development of Electric Oil Pump Controller in Hybrid Vehicle Based on PMSM (Permanent Magnet Synchronous Motor) and the AUTOSAR Platform," SAE Technical Paper 2019-01-0765, 2019, https://doi.org/10.4271/2019-01-0765.
Data Sets - Support Documents
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