Fractional order KiBaM model parameter identification method and system of power battery

Abstract

The present invention discloses a fractional order KiBaM model parameter identification method and system of a power battery, wherein the identification method comprises the steps of 1 initializing a linear parameter vector, utilizing an iterative learning identification method to identify the fractional order of a fractional order KiBaM model, and obtaining a fractional order initial value to thereby identify a non-linear parameter vector; 2 utilizing the identified non-linear parameter vector and combining the fractional order to identity the linear parameter vector and further determine the fractional order KiBaM model of the power battery; 3 acquiring the actual output of the power battery, calculating and determining whether the norm of the deviation of the output of the determined fractional order KiBaM model of the power battery and the acquired actual output of the power battery reaches a minimum value, if yes, ending to identify, and obtaining an optimal parameter of the fractional order KiBaM model of the power battery; otherwise returning to the step 1.

Description

technical field [0001] The invention belongs to the field of power battery modeling, and in particular relates to a fractional-order KiBaM model parameter identification method and system for a power battery. Background technique [0002] With the innovation of world technology, facing the crisis of oil consumption and the aggravation of air pollution, increasing investment in research and development of hybrid electric vehicles, pure electric vehicles, fuel cell vehicles and other electric vehicles and policy support have become the energy development strategies of various countries. important move. In the new energy development system, the power battery is a key part of the energy storage of the electric vehicle power system, photovoltaic power station and energy storage power station. How to better reduce the production cost of the power battery, prolong the life of the power battery and make full use of the available capacity of the battery has become current research h...

AI Technical Summary

Problems solved by technology

Both have their own advantages and disadvantages and both have certain defects: First, impedance spectrum analysis is aimed at half-cells, and its input must be a non-destructive continuous excitation to the battery, which is not suitable for actual working conditions, and its application is limited to the research and development stage of the battery

Second, the model identification in the time domain can obtain a very accurate model, but the time domain identification of the fractional model lacks corresponding theoretical methods, so there are certain difficulties. application, etc.)

Third, the identification of fractional order is the key link of fractional order model identification. However, the existing fractional order identification methods mostly use the interval segmentation method to easily fall into the false order, which greatly affects the accuracy of model identification.

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