This work focuses on the development and testing of new driving data pattern recognition intelligent system techniques to support driver adaptive, real-time optimal power control and energy management of hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs). A novel, intelligent energy management approach that combines vehicle operation data acquisition, driving data clustering and pattern recognition, cluster prototype based power control and energy optimization, and real-time driving pattern recognition and optimal energy management has been introduced. The method integrates advanced machine learning techniques and global optimization methods form the driver adaptive optimal power control and energy management. Fuzzy C-Means clustering algorithm is used to identify the representative vehicle operation patterns from collected driving data. Dynamic Programming (DA) based off-line optimization is conducted to obtain the optimal control parameters for each of the identified driving patterns. Artificial Neural Networks (ANN) are trained to associate each of the identified operation patterns with the optimal energy management plan to support real-time optimal control. Implementation and advantages of the new method are demonstrated using the 2012 California household travel survey data, and driver-specific data collected from the city of Victoria, BC Canada. / Graduate
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/11052 |
| Date | 19 August 2019 |
| Creators | Munthikodu, Sreejith |
| Contributors | Dong, Zuomin |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
| Rights | Available to the World Wide Web |
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