Adaptive optimization method for estimating SOC of battery based on Kalman filtering framework

Abstract

The invention discloses an adaptive optimization method for estimating SOC of a battery based on a Kalman filtering framework. The adaptive optimization method is characterized in that a second-orderRC equivalent circuit is used as a battery model; the second-order RC equivalent circuit parameters are identified by using the battery pulse experiment data and the MATLAB parameter identification tool box; and then a state equation and an observation equation of the battery are constructed according to the Kirchhoff voltage law, and an adaptive optimization strategy is added into an extended Kalman filtering algorithm on the basis of an estimated difference value of an observed quantity and the observation equation, and the optimized extended Kalman filtering algorithm is applied to SOC estimation of the battery. The result shows that compared with the traditional extended Kalman filter algorithm for estimating the SOC of the battery, the adaptive optimization method provided by the invention has the advantages that the precision is improved by 0.3%, and the fluctuation is smaller, and the accuracy and the practicability are very good.

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

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