Research on Soc Estimation of Lithium Iron Phosphate Battery Pack

Research on the estimation of the state of charge of lithium iron phosphate battery packs The estimation of the state of charge of lithium iron phosphate battery packs is to better use the battery pack as a power lithium-ion battery. In this paper, the second-order RC battery model is selected, and the trackless Kalman filter method with adaptive noise matching is used to estimate the state of charge of the battery pack, which improves the accuracy of the Kalman filter algorithm. Simulation results and experimental verification show that the algorithm has high estimation accuracy and good soc estimation effect. The calculation of the battery state of charge is an important prerequisite for the BMS system. Accurate estimation of the state of charge of the power lithium-ion battery pack can improve the safety performance of the battery, effectively protect the battery, extend the service life of the battery pack, and improve the efficiency of the battery. The difficulty in estimating the soc of power lithium-ion batteries lies in the complex dynamic characteristics of the battery system. Therefore, the key to soc estimation is to establish an appropriate battery model and select an appropriate estimation method. Commonly used battery models include electrochemical model, neural network model and equivalent circuit model. This paper chooses a second-order RC equivalent circuit model that can accurately reflect the dynamic characteristics of the battery pack. The Kalman filter algorithm can track the state of the system in real time and is suitable for the estimation of the state of charge of the power battery. The Kalman filter algorithm is applied to the evaluation method of linear systems, and the battery is a complex nonlinear system, so there is an extended Kalman filter (EKF) algorithm that uses Taylor to expand the linearized nonlinear system. The EKF algorithm can be applied to a good Research on battery soc estimation of battery, but the calculation process is relatively complicated and the stability of calculation is poor. Therefore, this paper adopts the unscented Kalman filter (UKF) algorithm, which is based on UKF to carry out UT transformation of system state variables. Several sampling points of the statistical properties of the state variables are then calculated in the system equations. The UKF algorithm is simpler and more stable than the EKF algorithm. In order to further improve the calculation accuracy, the adaptive matching algorithm is used to update the state noise and observation noise of the system in real time, which can further improve the accuracy of the system equation and the accuracy of the algorithm. Disclaimer: Some pictures and content of articles published on this site are from the Internet. If there is any infringement, please contact to delete. Previous post: Repair of Ni-MH batteries damaged due to long-term storage