A lithium battery parameter identification and SOC estimation method based on coyote optimization algorithm

Abstract

The invention provides a lithium battery parameter identification and SOC estimation method based on the coyote optimization algorithm, comprising the following steps: Step 1: Measure the current and voltage of the lithium battery through intermittent constant current discharge; Step 2: Establish a second-order lithium battery RC equivalent circuit model; Step 3: Construct the Coyote optimization algorithm; Step 4: Construct the extended Kalman filter algorithm; Step 5: Use the Coyote optimization algorithm to determine each parameter in the lithium battery model and estimate the battery SOC. The beneficial effects of the present invention are: the present invention establishes the second-order RC model of the lithium battery, derives its discrete state space expression, and uses the coyote optimization algorithm to identify the model parameters. Compared with the traditional heuristic algorithm, the identification accuracy is high and the convergence speed is fast. Using the identification results to estimate the SOC, the estimation error is small, which verifies the accuracy of the coyote optimization algorithm in parameter identification.

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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