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Research On Parameter Optimized Matching And Energy Management Strategy For Hybrid Electric Vehicle

Posted on:2016-06-29Degree:DoctorType:Dissertation
Country:ChinaCandidate:X L FuFull Text:PDF
GTID:1362330482463583Subject:Power electronics and electric drive
Abstract/Summary:PDF Full Text Request
Hybrid electric vehicle(HEV),which combines the merits of both traditional fuel vehicle and electric vehicle(EV),has the advantages of high performance,low energy consumption and low emission.It can not only improve the fuel economy,but also ensure the continued driving range.Thus HEV has become the main development direction of the new energy vehicle.The plug-in hybrid electric vehicle(PHEV),which is developed from the hybrid electric vehicle,has recently become a hot spot of research and development due to the larger battery capacity and the better emission-reduction performance.A lot of technical problems aren't solved in the development of hybrid electric vehicle,such as parameter optimized matching and energy management.The reasonable matching of every component parameter in hybrid power system is the prerequisite to improve the vehicle performance and make full use of HEV superiority.The traditional parameter matching method has thedisadvatages of great computational complexity,single target and poor reliability,andit cann't make the energy management strategy achieve the best results.Intelligent energy management system,as the brain of HEV,commands the coordination of the each system.Through optimizing the distribution of the desired torque between the engine and the motor in the energy management system,HEV can adapt the complexoperating condition,and it is the key of achieving energy-saving and emission-reduction.Consequently,the parameter optimized matching of power system is firstly studied for parallel HEV in this paper.Then,according to the existing problems of the energy management strategy in parallel HEV and PHEV,various advanced control theory and methods are adopted,and the energy management strategies are optimized and improved.The main research work and innovations are as follows.1.HEV is a highly complex nonlinear system,and the overall performance is closely related to the matching degree of system parameter.For the deficiency of the traditional methods,the parameter optimized matching methods based on the composite matrix are proposed.Firstly,according to the vehicle parameters and performance indicators,the requirement range of component parameters are analyzed and calculated.Then applying the weight coefficient method,the multi-objective optimization problem of fuel consumption and emission is converted into a single objective optimization problem.In the end,combining the simulation with Cruise,the effect of system parameter matching is evaluated through the objective function.The research results show that,compared with the traditional method,the proposed parameter matching method for HEV can improve the vehicle fuel economy and reduce the vehicle emission.And it provides a set of optimized vehicle configuration for the energy management strategy,which has a high application value in the vehicle design optimization project.2.Aiming at the inherent problem of fuzzy energy management strategy,such as poor adaptive ability and lack of autonomous learning,the neural network fuzzy energy management strategy for HEV based on driving condition identification is designed.The driving condition identification is realized by the method of the neural network sample learning and the characteristic parameters of driving condition analyzing,and the identification results are considered as the reference input of the fuzzy control strategy.Then through optimizing the membership function in fuzzy controller,the shortcoming of the poor operation condition pertinence in the traditional fuzzy control strategy is overcome.The research results show that,compared with the traditional fuzzy energy management strategy,the proposed control strategy in this paper can use the optimized membership function in different operation conditions,and the problem of poor operation condition pertinence is solved.The proposed control method has the strong practicability,the good robustness and the real-time performance,which has the high practical worth.3.Energy management strategy based on the road condition model for Plug-in hybrid electric vehicle is proposed.Combined the condition and driving data of the Chinese actual road,the driving cycle model is constructed.Then the state of charge(SOC)profile of the battery is optimized by using the dynamic programming algorithm for the drive cycle(long distance road condition).And then dividing the driving cycle into several segments between the traffic intersections,combining the road condition information(short distance road condition),and taking the minimal fuel consumption as cost function,the local optimal motor torque control sequence was calculated by the dynamic programming algorithm.The simulation results in Advisor show that,compared with the traditional charge depleting-charge sustaining strategy,the proposed energy management strategy can significantly improve PHEV fuel economy,and the vehicle can complete a greater potential of energy saving.4.In view of the hybrid dynamic characteristics of energy control system in HEV,comprehensive consideration of the relatively independent discrete event and continuous variable dynamic subsystem,the hybrid input/output automation model is established,and the interaction of discrete event and continuous variable is studied,which provides a method and means for the performance analysis of hybrid electric vehicle.On the premise of ensuring the fuel economy and the emission,the fixed rule energy management strategy considering the torque coordination process is improved and optimized by using the hybrid dynamic system theory for the first time,and the smooth switch of the mode for HEV is realized.The simulation results in Cruise show that,compared with the analysis method without considering the torque coordination process,the mode switching process by using the new method has the smaller torque fluctuation,the smoother mode switching and the better ride comfort.In addition,the study also found that the vibration of HEV has the greater impact to the vehicle NVH(Noise,Vibration and Harshness)and even to the driving performance.The vibration system of HEV can be simplified to a second order damped differential equation with time delay,thus,the oscillation performance of the second order damped differential equation with time delay is analyzed in the end,which provides the theoretical foundation for solving the vibration problem of HEV.
Keywords/Search Tags:hybrid electric vehicle, energy management strategy, multi-objective optimization, fuzzy control, road condition model, dynamic programming, hybrid dynamic system theory, torque coordination, vibration damping
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