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Research On Parameter Matching And Energy Management Of Pure Electric Vehicle Power System Based On Battery Life Model

Posted on:2018-04-12Degree:MasterType:Thesis
Country:ChinaCandidate:P S LiFull Text:PDF
GTID:2352330518460403Subject:Traffic and Transportation Engineering
Abstract/Summary:PDF Full Text Request
Based on the battery lifetime model and battery electric vehicles(BEVs).this paper proposes a novel method for the driveline system matching and the energy management.Compared with the traditional vehicles,there exist a higher proportion of the total cost,thereby it is consequential to find out an efficient approach for this field.The most difficult problems are the study with respect to state-of-health(SOH)and energy management of powertrain system.For our best knowledge,the study about SOH has been the research hotspot and difficulty at home and abroad,thereby existing rare related reaches.Considering the SOH model of battery,this paper mainly deals with two parts:based on data-driven and based on experimental.In addition.against the problem of energy management,the torque compensation is employed to assure the ride comfort and pedal response speed at first,and then the related constraints in the term of the failure mode is adopted to achieve the grade ability during fault period.In the SOH study of data-driven method,the DC-resistance method is implemented to define the SOH.Against the slow convergence rate of neural network(NN).ant colony algorithm is applied to optimize the input layer of NN.and therefore the DC resistance can be directly obtained.The results show that,the hybrid algorithm based on ant colony neural network model,can conduct the nice predictive performance.For the experiment-based method,the driving cycles,driving length.vehicle mass.regenerative energy as well as battery lifetime,are totally taken into account to as the input of this problem.Then based on the built total cost relationship and the software Autonomie.the optimal serial and parallel number of pack can be obtained by genetic algorithm(GA).Therefore.considering different driving cycle and operating limits of pack.the optimal matching problem can be solved.In the aspects of BEVs' energy management.the strategies based on the torque compensation of motor is introduced.under two familiar situations(i.e.the common mode and fault mode).For the condition of common mode,two approaches,i.e.based on look-up table and based on fuzzy control.are both implemented to study the performance of torque compensation,whose results show that the fuzzy controlled method can achieve more outstanding effect.Additionally,during the period of fault mode,the constraints based on the conditions of over-temperature motor and over-charge battery,are set up.aiming to conduct the grade ability and the system production.
Keywords/Search Tags:Battery electric vehicles(BEVs), battery lifetime model, matching of powertrain system, energy management, ant colony neural network algorithm, genetic algorithm(GA), fuzzy controlled algorithm
PDF Full Text Request
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