Lithium battery model parameter recognition method through least square method with forgetting factor

Abstract

The invention discloses a lithium battery model parameter recognition method through a least square method with a forgetting factor, and the recognition method comprises the steps: 1, building a second-order RC equivalent circuit model; 2, carrying out the bilinear transformation, and obtaining a difference equation of the system input and output of the equivalent circuit model; 3, building a recursive least square method with the forgetting factor (shown in the description); 4, building a coefficient equation; 5, collecting the parameters of a battery; 6, recognizing the parameters of the equivalent circuit model. According to the invention, the OCV-SOC function relation and the least square method with the forgetting factor are employed for the dynamic parameter recognition of the second-order RC equivalent circuit model, and the forgetting factor is introduced to a normally used recursive least square method. The method solves a problem of old data overlapping in an operation recursion process, and avoids the phenomenon of data saturation.

Description

technical field [0001] The invention relates to the field of batteries, in particular to a method for identifying parameters of a lithium battery model with a least squares method with a forgetting factor. Background technique [0002] The internal chemical reactions in the charge and discharge process of lithium-ion batteries are relatively complex, so this process is time-varying and nonlinear, so it is very difficult to obtain the parameters of the model through theoretical analysis. Although the model parameters are identified by the offline exponential fitting method at present, due to the time-varying nature of the battery system, the model parameters will also change greatly with the changes of battery SOC, external temperature, cycle times and other factors, so In order to improve the estimation accuracy of SOC and enhance the adaptability of the system, it is necessary to identify the battery model parameters online and make real-time corrections. Parameter estimat...

AI Technical Summary

Problems solved by technology

However, in parameter identification, the recursive least squares method is an algorithm with infinite memory length. For the battery system, the least squares method has more and more old data during the recursive operation process, which will cause the recursive results to fail to reflect the new data well. characteristics

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