Inspection System Development And Model Parameter Estimation Of Vanadium Redox Flow Battery

Nowadays,it has become an inevitable trend that fossil energy is replaced by renewable energy,because it is drying up.More and more countries and organizations invest a lot of national strength in building renewable energy projects such as photovoltaic and wind power.However,these renewable energy sources cannot be directly connected to the network because of their volatility and randomness,which has become the main obstacle to the development of new energy and distributed generation.It requires the development of large-scale energy storage technology to effectively suppress the power fluctuation of photovoltaic and wind power,and optimize the structure of distributed generation.A green and safe energy storage device,Vanadium Redox flow Battery(VRB)on the historical stage,has been paid extensive attention because of its unique advantages.At first,this paper designs and develops a battery stack inspection system based on ARM,which is used to monitor the parameters of stack,such as voltage,current and temperature,in order to ensure the stability and safety of stack.Through the experiment,it is proved that the inspection system has the advantages of high precision,fast transmission speed,strong anti-interference ability,good extensibility,simple interface and so on.The experimental platform is built with this system to provide data support for the modeling and parameter estimation of the battery stack.Secondly,the electrochemical model and equivalent circuit model of VRB are compared,and a first-order RC Thevenin equivalent circuit model is selected which is more suitable for parameter estimation.The parameters of each component in the model are obtained by pulse charging and discharging experiments and the stack model is simulated in MATLAB/Simulink.The maximum error of the terminal voltage is less than 0.4V,which meets the accuracy requirement,and build a foundation for parameter estimation.Then,according to the two key parameters of the battery management system: the State Of Charge(SOC)and the State Of Health(SOH),two kinds of Kalman filtering and the Sliding Mode Observer(SMO)are used to estimate the parameters respectively.Among them,two Kalman filtering are Extended Kalman Filter(EKF)and Unscented Kalman Filter(UKF).The experimental results of mixed pulse charging and discharging are estimated.These two algorithms can converge in the case of initial error is not large,and the SOC maximum estimation error is no more than 2.5%,and the estimated error of ohmic internal resistance is less than 10m?,which can evaluate the SOH.By contrast,the accuracy and the convergence of UKF are better than EKF.The estimation result of SMO is more volatile,but its accuracy and robustness are better.It can be converged when the initial value of SOC is large.Through comparison,its convergence is better than the two Kalman filters,which is suitable for the parameter estimation of the larger initial value error.The final thesis is summarized and prospected.