Lithium ion battery state of charge prediction method based on grey theory

Abstract

The invention provides a method for predicting the State of Charge (SoC) of a lithium-ion battery based on gray theory. In order to solve the problem that the existing method is difficult to realize online prediction under complex working conditions, and the accuracy is low, the method of the present invention first reads the data of factors affecting SoC and SoC data, constructs a gray correlation matrix, and then uses the gray correlation theory to calculate each influencing factor. Relevance between factors and SoC ξ i , and then determine the weight ω of the influence degree of each influencing factor on the SoC i , and then establish the two-dimensional gray prediction model GM(1,2) of each influencing factor and SoC value to predict the SoC value at K+1 time, denoted as SoC i (K+1), the final ω will be obtained i and SoC i (K+1) data, perform weighted average calculation to obtain the predicted value of SoC(K+1) at the time of battery K+1. The method of the invention predicts the state of charge of the battery, and has the characteristics of simple structure, high prediction accuracy and the like.

Description

technical field [0001] The invention belongs to an electric vehicle battery management system, in particular to a method for predicting the state of charge of a lithium-ion battery based on gray theory. Background technique [0002] With the increasingly prominent problems of environmental protection and energy saving, due to the advantages of high specific energy and green environmental protection, lithium-ion batteries have gradually been applied in the fields of automobiles, aerospace, and ships. However, due to problems such as overcharging and overdischarging of batteries, inconsistency among batteries, and heating of batteries, it is easy to cause battery failure, seriously affect service life, and cause major loss of life or property. Underutilized battery materials also cause a waste of resources. Therefore, for power batteries, the battery management system is very important. [0003] Among many parts of the battery management system, the prediction of the battery...

AI Technical Summary

Problems solved by technology

The ampere-hour measurement method is simple to calculate, but its prediction accuracy is poor

Moreover, precision ampere-hour measurement is expensive, and the working environment is demanding, so it is not suitable for electric vehicles with complex working conditions and severe vibration.

Although the open circuit voltage method can better obtain the SoC at the initial moment of the battery, it is not suitable for real-time prediction of electric vehicles in complex situations

Although the Kalman method has high prediction accuracy and strong tracking performance, the existing Kalman filter method has a large amount of calculation when estimating the SoC of lithium-ion batteries, and it is difficult to achieve online prediction, and it is not easy for the battery The accuracy of the model is required to be high, and an inaccurate battery model will bring large errors, which will greatly affect the convergence and accuracy of the Kalman filter, resulting in low reliability of the online estimated value of the lithium-ion battery SoC

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