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The Body Vibration Reduction Technology of Hybrid Electric Vehicle on Engine Re-start Situation
Technical Paper
2019-01-2303
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Hybridization and electrification of powertrains have been promoted to improve fuel efficiency and CO2 emissions. Along with hybridization, it is possible to reduce an engine usage frequency and improve the fuel efficiency in traveling. Especially in a hybrid electric vehicle (HEV), an operation state change from motor assist mode to engine firing mode. As a result, stay time in eigenvalue of the powertrain is shortened, and vibration of the vehicle body at the engine re-start situation is able to be reduced as compared with conventional engine-driven vehicle.
However, because HEV is good comfortability, a driver feels vibration easily. Therefore, the HEV system also has the issue of engine re-start vibration. Therefore, the purpose of this study is to reduce the body vibration at the engine re-start situation of the HEV.
In this study, the following model-based development (MBD) process was applied:
- -Construction of multi body dynamics model by using functional mockup interface (FMI)
- -Simulation of the impact on the body vibration by changing the valve timing at the engine re-start situation
It has been clarified that the vehicle body vibration can be reduced with controlling of the intake valve closing timing (IVC). Finally, it is possible to quantitatively choice the intake valve closing timing that acceptable the body vibration.
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Authors
- Kenichiro Ogata - Honda R&D Co.,Ltd. Automobile R&D Center
- Satoshi Watanabe - Honda R&D Co.,Ltd. Automobile R&D Center
- Kanako Shimojo Keiji Tojo - Honda R&D Co.,Ltd. Automobile R&D Center
- Tatsuya Ide - Honda R&D Co.,Ltd. Automobile R&D Center
- Shoji Kusunoki - Honda R&D Co.,Ltd. Automobile R&D Center
Citation
Ogata, K., Watanabe, S., Tojo, K., Ide, T. et al., "The Body Vibration Reduction Technology of Hybrid Electric Vehicle on Engine Re-start Situation," SAE Technical Paper 2019-01-2303, 2019, https://doi.org/10.4271/2019-01-2303.Data Sets - Support Documents
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