Soc estimation of iron phosphate lithium ion battery pack
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2022/08/25 15:14:56
T阿he State of charge estimation of iron phosphate lithium ion battery pack is to better apply the battery pack as a power lithium battery. In this paper, the second-order RC battery model is selected, and the adaptive noise matching trackless Kalman filter method is used to estimate the SOC 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 battery SOC is an important premise of BMS system. Accurate estimation of the SOC of power lithium battery pack can improve the safety performance of the battery, effectively protect the battery, prolong the service life of the battery pack, and improve the use efficiency of the battery.
The difficulty of SOC estimation of power lithium battery lies in the complex dynamic characteristics of battery system. Therefore, the key to SOC estimation is to establish an appropriate battery model and select an appropriate estimation method. The commonly used battery models include electrochemical model, neural network model and equivalent circuit model. The second order RC equivalent circuit model which can accurately reflect the dynamic characteristics of the battery pack is chosen in this paper. Kalman filter algorithm can track the state of the system in real time, which is suitable for the estimation of the state of charge of electric battery.
Kalman filtering algorithm is applied to the evaluation method of linear system, and the battery is a complex nonlinear system, so the use of Taylor expansion of linearization of nonlinear system extended kalman filtering (EKF) algorithm, the EKF algorithm can be applied to a good battery soc estimation research, but the calculation process is relatively complicated, the stability of the calculation is poorer, so this article use no trace Kalman filter (UKF) algorithm, which performs UT transformation on the system state variables based on UKF, conforms to several sampling points that the state variables can be converted into statistical properties of the state variables, and then performs operations in the system equations. UKF algorithm is simpler and more stable than 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 algorithm.
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