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Optimization Research On Fuzzy Control Strategy For A Plug-In Hybrid Vehicle Based On SA-PSO Algorithm

Posted on:2018-05-30Degree:MasterType:Thesis
Country:ChinaCandidate:P LiFull Text:PDF
GTID:2322330512491284Subject:Control engineering
Abstract/Summary:PDF Full Text Request
The energy shortage and environmental pollution of the world become more and more serious,which requires the traditional automotive industry to transform to achieve the sustainable development.During the exploration of transition,developing the new energy vehicles with low fuel consumption and emission has become the primary task of the automotive vehicle industry.And the Plug-In Hybrid Electric Vehicle(PHEV)has been one of the most promising vehicles for its excellent performance of energy saving and environmental protection.As a major control issue of PHEV,energy management has been the key of achieving low fuel consumption and low emissions.Energy management is distributing the energy flow between the engine and the battery to satisfy the requirements of vehicle dynamic performance and achieve a good fuel economy.And this paper will research the energy management from both driving and braking.An appropriate power system is the basis of energy management.Thus,the powertrain of the PHEV is first analyzed in this paper,and then the size and parameters of engine,motor and power battery are selected according to the vehicle parameters and dynamic performance.Simulations are also taken to verify the selection results.A fuzzy rule-based energy management strategy is designed in this paper.And the battery SOC,speed and engine efficiency is selected as the control rules.During traveling,PHEV may frequently change work modes to get better fuel economy.Hence,the engine needs to start and stop correspondingly,which can cause bad behaviors like shock of mechanical transmission system and bad ride experience.So the engine start/stop control strategy is added to the energy management strategy,which can significantly reduce the engine starting and stopping frequency and make the drivers feel more comfortable.At present,most energy management strategies focus on optimizing the energy flow in the drive process.In fact,much energy is caused in the braking process for urban cycles.If fully recovered,the brake energy will make a significant contribution to improve the fuel economy.However,the recovery of brake energy is always contradictory to the brake performance and must be recovered with the assurance of braking safe.Therefore,this paper analyzes the regenerative braking system and designs the fuzzy regenerative braking strategy.The simulation results show that the regenerative braking strategy can significantly improve the fuel economy of PHEV and has a certain advantages over other regenerative braking strategies.The fuzzy logic strategy should be based on vast manual or engineering experience,which may keep the PHEV from getting the best fuel economy.Therefore,the SA-PSO algorithm is introduced to optimize the engine fuzzy controller in this paper.To improve the adaptability of the strategy under different driving cycles,two methods are carried out:(1)Taking the hybrid driving cycle as the simulation cycle.Firstly,compose the HWFET,LA92,Ja1015,NEDC,MANHATTAN and NYCC driving cycles to a hybrid driving cycle,and then optimize the fuzzy rule-based energy management strategy by SA-PSO algorithm under the hybrid driving cycle.(2)Taking condition recognition technology to the energy management.Divide the hybrid driving cycle of method(1)into three driving cycles,namely low speed driving cycle,middle speed driving cycle and high speed driving cycle,and then optimize the fuzzy energy management under each driving cycle by SA-PSO algorithm.Finally,through the online condition recognition technology,select the appropriate control parameters.Moreover,simulations under other driving cycles are taken to verify the effects of the proposed energy management strategies.And the results indicate that the two energy management strategies are both effective in improving the fuel economy under different driving cycles.
Keywords/Search Tags:Plug-In hybrid electric vehicle, Energy management strategy, Fuzzy rule-based, Regenerative braking, SA-PSO algorithm, Condition recognition
PDF Full Text Request
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