u003cpu003e The soc estimation of the lithium iron phosphate battery pack
is to better use the battery pack as a power battery. The second-order RC battery model is selected in the article, and the unscented Kalman filter method with adaptive noise matching is used to estimate the soc of the battery pack, which improves the accuracy of the Kalman filter algorithm. Simulation results and experimental verification confirm that the algorithm has high estimation accuracy and the effect of soc estimation is better in time. The soc calculation of the power battery pack is an important prerequisite for the operation of the power battery pack BMS system. An accurate estimation of the soc of the power 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 the power battery pack is that the dynamic characteristics of the battery pack system are relatively complicated. Therefore, establishing a suitable battery model and choosing a suitable estimation method are the key to soc estimation. Common battery models are mainly electrochemical models, neural network models, and equivalent circuit models. The second-order RC equivalent circuit model is selected in the article, which can more 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 soc estimation research of power battery packs. The Kalman filter algorithm is an estimation method applied to linear systems, and the battery pack is a complex nonlinear system, so there is an extended Kalman filter (EKF) algorithm that uses Taylor expansion to linearize the nonlinear system. The EKF algorithm can compare It is good for the study of battery pack soc estimation, but the calculation process is more complicated and the calculation stability is poor. Therefore, the unscented Kalman filter (UKF) algorithm is used in the article. The UKF algorithm performs UT transformation on the state variables of the system. The state variable is converted into several sampling points that conform to the statistical characteristics of the state variable, and then brought into the system equation for operation. Compared with the EKF algorithm, the UKF algorithm is simpler and more stable. In order to further improve the calculation accuracy, the paper adopts an adaptive matching algorithm for the noise of the system, and updates 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. For more professional content, please refer to 'Soc Estimation Research of Lithium Iron Phosphate Battery Pack'u003c/pu003eu003c/pu003ecustom lithium ion battery
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