Energy Management Strategy Of Plug-in Hybrid Electric Vehicle Based On The Recognition Of Driving Intention And Working Condition

The plug-in hybrid electric vehicles (PHEV) are new energy vehicles, which havethe advantages of the pure electric vehicles and hybrid electric vehicles. They not onlycan charge the energy storage device through external power grid, which can reduce thedependency on the fuel and the cost of the vehicle; but also can ensure the car’s drivingrange. So they got the attention of the car companies, scientific research institutions andgovernments. Improvements of PHEV energy consumption economy stongly depend ontheir supervisory energy management strategy, but the existing energy managementstrategies have failed to consider or better adapt to working condision and drivingintention. Theerfore, the research of energy management strategy for PHEV based onthe recognition of driving intention and working condition has important practicalsignificance for further improve the vehicle energy consumption economy.This thesis takes the single motor plug-in hybrid electric vehicle as the object ofstudy. In order to improve energy consumption economic, the energy managementstrategy based on the recognition of driving intention and driving condition has beenstudied. The main works are summarized as follows.(1) The relationship of the ISG motor peak power, engine maximum power and thevehicle dynamic performance is researched. The relationship of the battery capacity andpure electric travel distance is analyzed. The final drive ratio is optimized by using ofgenetic algorithm for dynamic constraints and the objective function is minimum cost ofenergy consumption. Then the parameters matching of power transmission system arecompleted. Finally the simulation model of the vehicle is set up and dynamic and fueleconomy performance is analyzed, which verifies the rationality of power transsionsystem matching and laid the foundation for the development of energy managementstrategy.(2) In order to make full use of low const energy from power grid, the target valueof the battery state of charge (SOC) between the charging of depleting (CD) mode andthe charging of sustaining (CS) mode, the upper limit and lower limit value of CS modeare obtained by using of genetic algorithm. The CVT speed ratio, the engine torquegraph and the ISG motor torque grahp are obtained by using of instantaneousoptimization algorithm. The real-time optimization energy management strategy of thecombining the logic threshold and instantaneous optimization algorithm is put forward and evaluated about oil-saving effect. In order to solve the real-time optimization ofenergy management strategy can not get energy economy globally optimal problem，theoptimal energy management strategy of hybrid mode is set up by using minimumprinciple to solve the extremum of the objective function. The influence of energyeconomy is analyzed by the mileage, the initial battery SOC and the energy price.(3) In order to solve the problem of reliance on driving cycles about globaloptimization of energy management stategy based on the minimum principle, the energymanagement strategy based on the working condition recognition is presented. Thischapter also selectes six kinds of driving cycles including urban congestion, citysuburban and highway condition as standard conditions; adopts composite uniform splitstandard conditions and calculate the characteristic parameters; takes advantage of theextreme learning machine to identify the condition. The optimization parameterdatabase of a number of standard working conditions is obtained by using the minimumprinciple. The PHEV energy consumption optimization control based on the workingcondition recognition is realized by combining the results of working conditionrecognition with the updating of real-time control strategy parameters.(4) When the PHEV is cold start, the traffic data of the first condition is unknown.If the control parameters of the energy management strategies based on the workingcondition recognition are improper selected, the PHEV energy consumption economywill greatly decerased. Taking into the travel data is impacted by driving intention, thecontrol stategy parameters database is set up by combine the driving intentionrecognition based on fuzzy controller with real-time optimization energy managementstrategy, which implements the PHEV energy consumption optimization control basedon driving intention recognition. In order to fully tap the PHEV saving potential, theenergy savings of energy management strategies based on driving intention and workingcondition is comparative analyzed, which take the energy management strategies basedon the driving recognition as the initialization parameters of global optimization energymanagement strategy. Therefore, the comprehensive energy management strategy basedon the recognition of driving intention and working condition is proposed.(5) The comprehensive test bench for plug-in hybrid electric vehicles powertrain isbuilt. The vehicle control procedures and the test bench control software for PHEV aredeveloped based on Matlab/Simulink/Stateflow and D2P platform. Then the dataacquisition and calibration control system are set up by ATI-VISION. Finally, benchtests and road tests are conducted, and the testing results validate the correctness of the PHEV simulation model is established, and also partly demonstrate the effectiveness ofthe energy management strategy in this work.