Lithium battery parameter identification and SOC estimation method based on suburb wolf optimization algorithm

Abstract

The invention provides a lithium battery parameter identification and SOC estimation method based on a suburb wolf optimization algorithm. The lithium battery parameter identification and SOC estimation method comprises the following steps: 1, measuring the current and voltage of a lithium battery through intermittent constant-current discharge; 2, establishing a second-order RC equivalent circuit model of the lithium battery; 3, constructing a coyote optimization algorithm; 4, constructing an extended Kalman filtering algorithm; and 5, determining each parameter in the lithium battery model by using a coyote optimization algorithm, and estimating the SOC of the battery. The method has the beneficial effects that the lithium battery second-order RC model is established, the discrete state space expression of the lithium battery second-order RC model is deduced, and the model parameter identification is carried out by using the coyote optimization algorithm, so that compared with a traditional heuristic algorithm, the method has the advantages of high identification precision, high convergence speed, and small estimation error by using an identification result to carry out SOC estimation, and the accuracy of the coyote optimization algorithm in the aspect of parameter identification is verified.

Description

technical field [0001] The invention relates to the technical field of lithium battery modeling, in particular to a second-order RC model parameter identification and SOC estimation method for a lithium battery based on a coyote optimization algorithm. Background technique [0002] With the development of industry, the reserves of fossil fuels are difficult to meet the energy demand. The characteristics of new energy, such as pollution-free and renewable, make it a research focus of various countries. In recent years, electric vehicles have become more and more popular. Compared with traditional fuel vehicles, they can achieve zero emissions and lower energy costs. The actual operation of the electric vehicle is inseparable from the data information fed back by the battery management system (Battery Management System, BMS). As the most important parameter in the battery management system, the battery state of charge (SOC), its accuracy and Robustness is extremely important...

AI Technical Summary

Problems solved by technology

[0005] The object of the present invention is to provide a kind of lithium battery parameter identification and SOC estimation method based on coyote optimization algorithm, utilize coyote optimization algorithm and Extended Kalman filter (EKF) algorithm to carry out parameter identification and SOC estimation to lithium battery model, effectively It solves the problem that the traditional heuristic algorithm has a slow convergence speed and is easy to fall into a local optimum. The error of the SOC estimation value is small and the accuracy is high. The specific steps are as follows:

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