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The Research Of Energy Management Strategy And SOC Estimation For Hybrid Electric Vehicles

Posted on:2010-09-18Degree:MasterType:Thesis
Country:ChinaCandidate:Q KongFull Text:PDF
GTID:2132360278472455Subject:Power electronics and electric drive
Abstract/Summary:PDF Full Text Request
Since invented, vehicles bring people not only facility, but also challenges, such as shortage of energy sources, depravation of environment and so on. New type vehicles are needed for sustainable traffic, so it has been the chief task for present auto industry to develop vehicles with low fuel consumption and emissions. Under the background mentioned above, hybrid electric vehicle (HEV), which incorporates the advantages of both the conventional fuel vehicle and the electric vehicle, is regarded as the focal point of attention and main flow of commercialization for its high fuel economy and low emissions.The complexity of hybrid drive system determines the complicacy of energy management strategy (EMS). With the prerequisite of satisfying the driving requirements, EMS of HEV controls the magnitude and flow direction of energy between each component, so EMS is significant for rational use of carrying energy and realization of energy saving and environment protection. Battery state-of-charge (SOC) is the main warranty to establish EMS and to use batteries rationally, so it is necessary to estimate SOC exactly. But SOC estimation is regarded as a difficult problem due to the nonlinearity of batteries. In this paper, EMS and SOC estimation of HEV are researched, and the main contents are described as follows.Firstly, the development background, present situation and foreground of HEV are introduced. The present situation of EMS and SOC estimation are also introduced. After that, the main methods, present problem and improvement of EMS and SOC estimation are analysed.Secondly, the principle of energy management strategy for parallel HEV is discussed and several main kinds of energy management strategy are analysed. Then diagonal recurrent neural network (DRNN) based EMS for parallel HEV is designed. The design process is described detailedly and simulation is performed based on electric vehicle simulator ADVISOR. The simulation results demonstrate that DRNN based strategy can reduce fuel consumption and has higher fuel economy than the logic threshold strategy. At the same time, comparing with the equivalent fuel consumption minimization strategy, the strategy has simpler calculation and faster response, so it is suitable for real-time application.Thirdly, the battery SOC estimation is researched. The present SOC estimation methods are analysed. Neural network and Kalman filter (KF) are used to gauge battery SOC separately. Then based on the foundation of the two methods, neural network and KF are combined to estimate SOC. The simulation results indicate that the integrated method can estimate SOC exactly. Furthermore, the method has batter robustness than neural network based method and smaller calculation amount than Kalman filter based method.Finally, smart battery monitor DS2438 based SOC estimation experimental system is designed. The system can survey temperature, voltage and current of each battery, estimate SOC and display results. The hardware circuit and software flow are described in detail.
Keywords/Search Tags:hybrid electric vehicle, energy management strategy, diagonal recurrent neural network, Kalman filter, SOC estimation
PDF Full Text Request
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