Research On Intelligent Energy Management Strategies Of Hybrid Electric Vehicles

The traditional automobile industry has brought many energy and environmental problems in the process of rapid development.The development of energy-saving,pollution-free and high-efficiency vehicles is the theme of my country's sustainable development.The effective use of supercapacitors has obvious advantages in terms of specific power,efficiency and cycle life,and allows it to work in conjunction with power batteries to make up for the current defects of pure electric vehicles with single-power electric vehicles in terms of power performance and driving range.In this paper,the lithium iron phosphate power battery and the super capacitor are matched to form a composite energy storage system,and a suitable vehicle control strategy is provided for the composite power system.The thesis is mainly divided into three parts.The first part is to design the transmission scheme,1.Analyze the main components of the composite power system,including lithium iron phosphate battery,super capacitor and bidirectional DC / DC converter,determine the configuration of the composite power system;2.Take a small The car is used as a reference model to match its parameters.Under the prerequisites of meeting the power and economy of the vehicle,the model of the composite power system is designed and built.3.Based on AVL-Cruise software to build a composite power supply pure electric vehicle energy system simulation model,based on the control strategy principle Matlab / Simulink software to build an overall control strategy model.The second part is based on the characteristics of the composite power supply system in the paper,based on the development of four power supply working modes of the composite power supply,and designing three different control strategies for comparison.1.Design the logic threshold control strategy according to the working mode,and use the required power and super capacitor as the threshold to control;2.Combine fuzzy mathematics,fuzzy theory and other mathematical principles to formulate the fuzzy logic controller according to the working mode,the controller uses battery,super capacitor SOC,The power demand of the system is used as three inputs,and the scale factor K is used as the output.According to expert opinions,fuzzy control rules are formulated to distribute power flow to the supercapacitor and lithium iron phosphate battery;3.Optimize the fuzzy logic controller to increase driving intent control Strategy modification optimizes the fuzzy logic controller.The driving intent recognition work is also implemented using fuzzy logic strategy,with driver speed,brake pedal opening and acceleration as inputs and driving intention as output.The third part analyzes the simulation results.(1)Under the fuzzy logic control strategy,compared with the single-power lithium battery pure electric vehicle,the composite power system with super capacitors can improve the charging and discharging efficiency of the power battery and reduce the power battery.The number of discharge cycles and discharge current.Under the NEDC cycle of 1180 s,the charge state of the power battery decreased from 0.9 to 0.69 in the composite power system,which was an increase of 1.5% compared with the single power system.In terms of peak current of the power battery,the composite power system was lower than the single power system.20 A.(2)Comparing the results obtained by driving intention recognition with the results of fuzzy logic operation,it can be seen that the driving intention recognition strategy can maximize the energy utilization of the power supply system and increase the driving range of pure electric vehicles.The SOC of the battery drops from 0.9 to 0.724.Compared with the fuzzy control of single battery,the SOC of the battery is increased by 2.5%.Compared with the fuzzy control of composite power supply,this method increases the SOC of the lithium battery by 1.2%.In terms of aspect,the vehicle's power consumption per 100 kilometers after the optimization of driving intent recognition is slightly reduced compared with the fuzzy control strategy,and the power consumption is reduced by 1.7%.