Study On State Estimation And Energy Management Strategy Of Hybrid Energy System For Pure Electric Vehicle

As an important part of new energy vehicles,electric vehicles have been paid more and more attention,invested and promoted by the government,enterprises,universities and other departments because of their non pollution,low noise and high energy efficiency.As the key component of electric vehicle,how to enhance the safety and prolong the service life of power battery has become a research hotspot and difficulty.Composite power supply,which is composed of power battery and super capacitor,can take advantage of the high charging and discharging power of super capacitor,reduce the peak power of power battery,restrain the high current charging and discharging of power battery,and achieve the effect of prolonging the service life of power battery,which has become an important research direction.In this paper,the state estimation and energy management strategies are studied,and the corresponding state estimation methods and energy management control strategies are proposed.Based on A&D5435,the hardware in loop experiment platform of the composite power system is developed.The control effect of the state estimation method and energy management strategy is verified by the combination of simulation analysis and experimental verification.The detailed contents are as follows:(1)In this paper,the dynamic model of electric vehicle,the simulation model of power battery and the simulation model of super capacitor are established,and the control structure of semi-active composite power supply is constructed.This structure contains many control modes,which can give full play to the power characteristics of the super capacitor,reduce the cost of use and improve the efficiency of energy use.Aiming at the problem of parameter identification of composite power supply model,based on ant lion optimization algorithm and chaos mapping theory,this paper develops an improved ant lion optimization algorithm to identify the model parameters of composite power supply.The identification results show that the proposed algorithm has fast convergence speed,good robustness,high accuracy,and can get small estimation error.Based on the continuous power energy function method,the relationship between the demand capacity of super capacitor and the power threshold of battery under typical conditions is analyzed.According to the distribution of vehicle demand power,the battery power threshold is determined,and then the super capacitor parameter configuration is obtained.(2)Aiming at the problem of energy state estimation,this paper systematically analyzes and discusses the existing problems and shortcomings of current power battery energy state estimation methods.Based on the residual vectors of state vector and measurement vector,an adaptive weighted Cubature Kalman filter algorithm is proposed.Combining PF and CKF,the adaptive weighted Cubature Particle filter(AWCPF)is develop to estimate the energy state of power battery.The Cubature Kalman filter is used to get the approximate optimal proposal distribution,which is then transferred to PF for importance sampling of the energy state particles,which can not only suppress the particle decay phenomenon of PF,but also improve the estimation accuracy of the energy state.Finally,the simulation results verify that AWCPF algorithm has higher accuracy and better robustness compared with PF and CKF method,and the convergence analysis shows that AWCPF method can quickly converge to the real state value.(3)Aiming at the problem of battery health state estimation,based on the voltage recovery curve after constant current and constant voltage charging,two characteristic parameters of battery health state are extracted: sample entropy and exponential coefficient.Then,based on support vector regression(SVR)algorithm,a battery health state estimation model is established with sample entropy and index coefficient as input and battery available capacity as output.Through the experimental verification and comparative analysis,the validity of the model based on SVR is verified.It shows that the sample entropy and exponential coefficient,as the characteristic parameters of battery health state are effective for battery health state estimation.(4)Aiming at the control strategy of hybrid power supply,this paper analyzes the nonlinear characteristics of current and voltage of semi-active composite power supply and the time-varying of model parameters,and proposes an energy management control strategy based on adaptive model predictive control algorithm.Based on the historical data,the improved ant lion optimization algorithm proposed in this paper is used to identify the parameters of battery model on-line,and update the parameter matrix of model predictive control in real-time.The adaptive weight cubature particle filter is used to estimate the model state.The results show that the adaptive model predictive control algorithm can obtain better control effect,and can be better applied to nonlinear time-varying model.(5)Aiming at the experimental verification of the control strategy of the composite energy system,a hardware in loop simulation experimental platform is built by using the power battery pack,super capacitor bank,DC / DC converter,Neware battery test system and A&D5435.The control model and optimization algorithm are modeled and tested in Matlab / Simulink environment,and then the C code is automatically generated by RTW module and downloaded to A&D5435 for operation.The experimental results show that the developed energy management strategy algorithm can basically achieve the effect of "peak shaving and valley filling" of supercapacitor,which reflects the advantages of composite enery system in slowing down the performance decline of power battery and extending the service life of power battery.