Study On Series-parallel Hybrid Electric Vehicle’s Multimode Energy Control Strategy Based On Driving Condition Recognition

Energy control strategy is one of the core technology of Hybrid electric vehicle (HEV),and has been a research hot spot all the time. But the existent research didn’t deal with thethree following problems very well caused high fuel consumption and emission of HEVrelatively. Firstly, the existent strategy didn’t take the reaction of dynamic variation ofdriving conditions (the carload, road slope) to the energy control strategy into account.Secondly, because the control strategy based on the power driving mode, as the requirementof power is too high, it will cause HEV worse fuel consumption and emission. Thirdly, thecomponents’ character of control strategy optimization model ignores the influence ofvehicle condition varies. In view of this, this dissertation did study on series-parallel HEV’smultimode energy control strategy based on driving condition recognition.In the research of identification model of driver’s intention based on driving conditionrecognition, Firstly, taking a serial-parallel HEV as research object, based on vehiclelongitudinal dynamics and making use of vehicle speed, acceleration speed etc which werecollected by real vehicle tests, the carload and road slope solving model was built by leastsquare method. Applying particle swarm optimization method, the solving theory of carloadand road slope was studied. Eighteen groups of identification experiments of real vehicledynamical carload and road slope were collected to verify the theory, which turns out to befeasible. Secondly, in order to analyze driver’s intentions,180drivers were investigated,and the conclusion of ‘driver’s operation on accelerate pedal is much more like therequirement of vehicle’s speed’ was obtained. Since then, taking advantage of comfortdegree and fatigue theory in ergonomics, the nonlinear model called ‘acceleratorpedal-speed need’ was built to identify the speed requirement of drivers. Based on that, theidentification model of driver intention about torque requirement was studied, which canprovide information of the driver’s torque requirement for energy control strategyoptimization. Thirdly, for analyzing the influence of the result of driver’s intentionidentification on driving power consumption of HEV, varies of real vehicle tests of carloadand road slope was proceeded. The results showed that driver’s intention recognition model based on carload and road slope identification can save driving energy3.08%than theoriginal vehicle.In the research of model building of the serial-parallel HEV based on driving conditionrecognition, The full vehicle model of HEV energy control strategy was built byMATLAB/SIMULINK, including full vehicle dynamic module, driving conditionidentification module, driver intention recognition module, engine module, generatormodule, motor module, and power battery module, and the models’ effectiveness wasidentified with the real vehicle tests data. In aspect of engine module, the basic theory ofthe engine mean value model was studied. Based on this, in combination of the dynamicmodel and efficiency model of planetary gear coupling mechanism, an approach wasproposed which making use of the torque of generator to calculate the torque of engineoutput indirectly. And also an experiment was proceeded to prove it. It solved the problemof acquiring the engine output torque in the mean value model of engine. Besides,47groups of typical engine operation parameters were chosen from270groups of real vehicletests data (the test road type included city, suburb, freeway, and mountain area, the vehiclecondition included no-load, half load and full load, the road condition included flat andslope) to identify the37undetermined coefficient of the engine mean value model with amethod of least square identification and algorithms of particle swarm and genetic. And amean value model of1NZ—FXE engine which has4-Cylinder and16-Valve was founded.On base of this, an approach of taking advantage of engine mean value model to build theefficiency model of engine was proposed, which could solve the problem of deterioration ofsaving fuel and emission of HEV because of the changing of engine model during operatingof the vehicle. At last, in order to solve the problem of interpolation precision of the MAPmodels of power components efficiency and other parameters, on the base of the MAPmodel’s data, the neural network model of characteristic parameters of engine, generator,electric motor and power battery were founded.In the research of energy control strategy of multi driving cycles and multi modes,Firstly, based on the whole vehicle model of HEV energy control and driver’s intentionidentification model, the driver’s intention of torque acquirement was given as one of theinput parameters of energy control strategy of HEV. Secondly, taking advantage of global optimal and fast convergence of the genetic particle swarm intelligent bionic theory, multidriving condition and modes of HEV energy control strategy were studied. The strategyincludes5driving conditions (such as Ready, Starting, After starting, Braking, Reversing),and3kinds of driving modes (such as Sport, Economic, Balance), and4kinds of energyflow modes (Battery drive, Fuel drive, Fuel drive and battery charging, Fuel and batterydrive), and37groups of specific energy optimal strategy. The strategy takes integratedperformance of fuel consumption and emission of HEV as optimization target, and179584working points were optimized in all. The sport mode energy control strategy shows thecharacteristic of power, and the economic strategy shows the characteristic of low fuelconsumption, and the balance strategy is between the two. It was also found that HEV’senergy transform rate presents ‘U’ style rule with driver’s torque requirement increasing.The test shows that the strategies those were studied either in fuel consumption or emissionwere better than original vehicle. Thirdly, aiming at the reversing condition of PRIUS, thestrategy of hybrid drive mode was analyzed and real vehicle was tested. It turns out thatthere is energy waste of the original vehicle because of the opposite rotational direction ofengine and the driving wheels. And a scheme was proposed to improve it。At last, simulations tests and real vehicle tests were proceeded to verify the drivingcondition identification based multi modes energy control strategy of serial-parallel HEV.