An Adaptive Optimization Method for Estimating Battery Soc Based on Kalman Filter Framework

Abstract

The invention discloses an adaptive optimization method for estimating battery SOC based on a Kalman filter framework, using a second-order RC equivalent circuit as a battery model, using battery pulse experimental data and MATLAB parameter identification toolbox to analyze the second-order RC equivalent circuit parameters Carry out identification, and then construct the state equation and observation equation of the battery according to Kirchhoff's voltage law. The extended Kalman filter algorithm is applied to battery SOC estimation. The results show that: compared with the traditional extended Kalman filter algorithm for estimating battery SOC, the method proposed by the present invention has an accuracy of 0.3% higher, and the fluctuation is smaller, which has good accuracy and practicability.

Description

technical field [0001] The invention belongs to the field of battery management system state estimation, and more specifically relates to an adaptive optimization method for estimating battery SOC based on a Kalman filter framework. Background technique [0002] In recent years, many studies have explored battery state-of-charge (SOC) estimation methods. One is the model-driven method, such as electrochemical model, equivalent circuit model. The electrochemical model uses the complex electrochemical reaction mechanism inside the battery to establish the battery power loss relationship. It has high accuracy, but the calculation is very complicated and it is difficult to apply it in actual engineering. The equivalent circuit model uses the external characteristics of the battery to estimate the SOC of the battery based on adaptive filtering methods such as ampere-hour integral and Kalman filter or particle filter, and reduces the estimation error caused by the initial value o...

AI Technical Summary

Problems solved by technology

[0004] The present invention proposes an adaptive optimization method for estimating battery SOC based on the Kalman filter framework for the case where the filter type can only be Gaussian white noise to limit the filter effect when estimating the battery SOC with the Kalman filter algorithm framework , so that when using the Kalman filter framework to estimate the battery SOC, the noise parameters can be adaptively changed according to the change of the measurement feedback, so that the filtering effect is better

Images