Although there are different HEV configurations, they are all based on same kinds of components. After introducing the main components HEVs use, we build up a model which can illustrate the basic idea of HEVs. The analysis of the model helps us to reveal the essential problem of HEV power control. The performance of a HEV depends not only on the individual components but also on how the components are coordinated. The power control system must determine operating points for the components during driving to save energy. The proposed power control approach is based on model predictive control and trying to solve the nature problem of HEV power control by an optimization concept, which makes the approach applicable for all kinds of HEVs. A number of different simulations have been executed to prove the feasibility of the approach. By changing some operational weights, the power control system can achieve different performances. / Another key concept adopted in the power control system is based on the premise that future driving load would affect fuel consumption, as well as the operating modes of the vehicle and the driver behavior do. The proposed power control approach incorporates a driving load forecasting algorithm whose role is to assess the driving environment, the driving style of the driver, and the trend of the vehicle using long and short term statistical features of the past drive cycle. This future driving load information is subsequently used to change the operational weights of the power control approach, such as engine efficiency, battery State of Charge (SOC), engine speed, etc. By this way, the power control approach leads to improved the vehicle's overall performance. / One of the major crises that the world is facing today is the problems of energy. With the beneficial effect on the environment and high energy transformation efficiency in hybrid electric vehicle technology, automobile manufacturers have begun to look more seriously into vehicles with alternative power sources. Aimed at solving the more and more serious problems of energy, HEV has been one of the best practical applications for transportation with high fuel economy. / This dissertation proposes a new power control approach for all kinds of hybrid electric vehicles (HEVs). / To obtain better performance, we use particle swarm optimization (PSO) to find optimal weights for different drive loads. Then, by integrating MPC controller and load forecasting algorithm, a realtime HEV power control system, model predictive power control with load forecasting system (MPC-LF), is developed. Experimental results prove the feasibility of the control system. / Wang, Zhancheng. / Adviser: Xu Yangsheng. / Source: Dissertation Abstracts International, Volume: 70-06, Section: B, page: 3631. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 132-140). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.
| Identifer | oai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_344314 |
| Date | January 2008 |
| Contributors | Wang, Zhancheng., Chinese University of Hong Kong Graduate School. Division of Automation and Computer-Aided Engineering. |
| Source Sets | The Chinese University of Hong Kong |
| Language | English, Chinese |
| Detected Language | English |
| Type | Text, theses |
| Format | electronic resource, microform, microfiche, 1 online resource (xvi, 140 leaves : ill.) |
| Rights | Use of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/) |
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