This thesis (1) reports a new Dynamic Programming (DP) approach, and (2) reports a Real Time Control strategy to optimize the energy management of a Hybrid Electric Vehicle(HEV). Increasing environmental concerns and rise in fuel prices in recent years has escalated interest in fuel efficient vehicles from government, consumers and car manufacturers. Due to this, Hybrid electric vehicles (HEV) have gained popularity in recent years. HEV’s have two degrees of freedom for energy flow controls, and hence the performance of a HEV is strongly dependent on the control of the power split between thermal and electrical power sources. In this thesis backward-looking and forward-looking control strategies for two HEV architectures namely series and parallel HEV are developed.
The new DP approach, in which the state variable is not discretized, is first introduced and a theoretical base is established. We then prove that the proposed DP produces globally optimal solution for a class of discrete systems. Then it is applied to optimize the fuel economy of HEV's. Simulations for the parallel and series HEV are then performed for multiple drive cycles and the improved fuel economy obtained by the new DP is compared to existing DP approaches. The results are then studied in detail and further improvements are suggested.
A new Real Time Control Strategy (RTCS) based on the concept of preview control for online implementation is also developed in this thesis. It is then compared to an existing Equivalent Cost Minimization Strategy (ECMS) which does not require data to be known apriori. The improved fuel economy results of the RTCS for the series and parallel HEV are obtained for standard drive cycles and compared with the ECMS results
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/51887 |
| Date | 22 May 2014 |
| Creators | Ramaswamy, Nikhil |
| Contributors | Sadegh, Nader |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
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