LAPSE:2018.0954
Published Article
LAPSE:2018.0954
Optimization of Shift Schedule for Hybrid Electric Vehicle with Automated Manual Transmission
Wenchen Shen, Huilong Yu, Yuhui Hu, Junqiang Xi
November 27, 2018
Currently, most hybrid electric vehicles (HEVs) equipped with automated mechanical transmission (AMT) are implemented with the conventional two-parameter gear shift schedule based on engineering experience. However, this approach cannot take full advantage of hybrid drives. In other words, the powertrain of an HEV is not able to work at the best fuel-economy points during the whole driving profile. To solve this problem, an optimization method of gear shift schedule for HEVs is proposed based on Dynamic Programming (DP) and a corresponding solving algorithm is also put forward. A gear shift schedule that can be employed in real-vehicle is extracted from the obtained optimal gear shift points by DP approach and is optimized based on analysis of the engineering experience in a typical Chinese urban driving cycle. Compared with the conventional two-parameter gear shift schedule in both simulation and real vehicle experiments, the extracted gear shift schedule is proved to clearly improve the fuel economy of the HEV.
Keywords
automated mechanical transmission (AMT), dynamic programming (DP), gear shift schedule, hybrid electric vehicle (HEV)
Suggested Citation
Shen W, Yu H, Hu Y, Xi J. Optimization of Shift Schedule for Hybrid Electric Vehicle with Automated Manual Transmission. (2018). LAPSE:2018.0954
Author Affiliations
Shen W: School of Mechanical Engineering, Beijing Institute of Technology, 5 South Zhongguancun Street, Haidian District, Beijing 100081, China
Yu H: Department of Mechanical Engineering, Politecnico di Milano, via G. La Masa 1, Milano 20156, Italy
Hu Y: School of Mechanical Engineering, Beijing Institute of Technology, 5 South Zhongguancun Street, Haidian District, Beijing 100081, China
Xi J: School of Mechanical Engineering, Beijing Institute of Technology, 5 South Zhongguancun Street, Haidian District, Beijing 100081, China
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Journal Name
Energies
Volume
9
Issue
3
Article Number
E220
Year
2016
Publication Date
2016-03-19
Published Version
ISSN
1996-1073
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PII: en9030220, Publication Type: Journal Article
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LAPSE:2018.0954
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doi:10.3390/en9030220
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Nov 27, 2018
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Nov 27, 2018
 
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Calvin Tsay
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