This dissertation proposes an Intelligent Energy Management Agent (IEMA) for parallel hybrid vehicles. A key concept adopted in the development of an IEMA is based on
the premise that driving environment would affect fuel consumption and pollutant emissions, as well as the operating modes of the vehicle and the driver behavior do. IEMA incorporates a driving
situation identification component whose role is to assess the driving environment, the driving style of the driver, and the operating mode (and trend) of the vehicle using long and short
term statistical features of the drive cycle.
This information is subsequently used by the torque distribution and charge sustenance components of IEMA to determine the power
split strategy, which is shown to lead to improved fuel economy and reduced emissions.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/271 |
| Date | 30 September 2004 |
| Creators | Won, Jong-Seob |
| Contributors | Langari, Reza |
| Publisher | Texas A&M University |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation, text |
| Format | 671951 bytes, 153199 bytes, electronic, application/pdf, text/plain, born digital |
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