Storage battery parameter recognition method based on improved dragonfly algorithm

Abstract

The invention relates to a storage battery parameter recognition method based on an improved dragonfly algorithm. The storage battery parameter recognition method comprises the following steps of 1, constructing a three-order Thevenin equivalent circuit model; 2, collecting terminal voltage change data of variable current discharge of the storage battery, and constructing a fitness function according to the terminal voltage change data and a model output result; 3, selecting a dragonfly algorithm as an recognition method, and searching an initialized dragonfly population in a space by using Tent chaotic mapping; 4, further optimizing population diversity by using an elite reverse learning group strategy; 5, adaptively adjusting the flight step length based on the dragonfly algorithm and the behavior weight constructed by the S-shaped function, and updating the population position; sixthly, repeating the operation of the fourth step and the fifth step until the fitness of the prey meetsan expected value or reaches the maximum iteration step number; and 7, outputting the parameter information of the optimal individual as a model parameter of the storage battery, and drawing a fitting curve and an error curve. The method can replace a conventional storage battery recognition method, and effectively improves the recognition precision of the parameters of the storage battery.

Description

technical field [0001] The invention relates to a storage battery parameter identification method based on an improved dragonfly algorithm, and belongs to the technical field of battery management systems. Background technique [0002] As a clean energy with zero pollution and zero emission, battery has been widely used in the field of electric vehicles. The parameter identification of the battery model is an important basis for accurate estimation of the state of charge. In practical applications, accurately estimating the state of charge of the battery can prevent battery life shortening due to overcharging and overdischarging, or energy output reduction caused by setting too much redundant power. Therefore, obtaining accurate battery model parameters has become an important part of battery research. [0003] At present, the identification methods of the battery model mainly include: nonlinear least squares method, Kalman filter identification method and identification m...

AI Technical Summary

Problems solved by technology

The nonlinear least squares method has the advantages of small calculation, less memory and no need to store all data, and can realize real-time online identification of battery model parameters, but the nonlinear least squares method has low identification accuracy for complex nonlinear systems

The Kalman filter identification method can achieve a more accurate estimation of the signal containing noise, but it has the disadvantages of relying too much on the statistical characteristics of the noise and having to determine the initial value of the state variable

The intelligent optimization algorithm has the advantages of less restrictive conditions and strong ability to identify nonlinearities. It has been widely used in the parameter identificat

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