Support vector machine (SVM) based prediction method for the degradation tendency of super-capacitor capacitance value

Abstract

The invention discloses a support vector machine (SVM) based prediction method for the degradation tendency of super-capacitor capacitance value. The prediction method utilizes the regression function of the support vector machine to predict the degradation tendency of the super capacitor capacitance value and comprises: 1) pre-processing the input value and the output value; 2) carrying out trainings to the training set data for a regression estimation function; 3) using the particle swarm optimization algorithm to automatically optimize the relevant parameters of the support vector machine; 4) according to the optimization result, configuring the corresponding parameter values of the support vector machine; substituting the training set data into a correlation vector machine model to obtain a regression prediction model for the degradation tendency of the capacitance value; and 5) substituting the training set data into the regression prediction model to obtain the degradation tendency of the capacitance value. According to the invention, it is possible to conduct online prediction to the degradation tendency of the capacitance value. Through the introduction of a particle swarm optimization algorithm to modify the parameter optimization method, the prediction efficiency and accuracy of the algorithm are increased so that it can be applied in a larger scope.

Description

technical field [0001] The invention proposes a method for predicting the degradation trend of the capacitance value of a supercapacitor based on a support vector machine (SVM), and belongs to the technical field of energy storage. Background technique [0002] Supercapacitors have been widely used due to their advantages such as high power density, short charge-discharge time, long cycle life, and wide operating temperature range. The remaining service life of the supercapacitor is also called the state of health (StateOf Health; SOH) of the supercapacitor, which is one of the important state parameters of the supercapacitor. The industry generally believes that when the capacitance value of the supercapacitor drops by 20%, the service life of the supercapacitor can be considered to be over. Therefore, accurately predicting the change trend of the capacitance value of supercapacitors and providing support information for predictive maintenance, repair and optimization of s...

AI Technical Summary

Problems solved by technology

[0003] The prediction method based on the failure mechanism model analyzes the operation mechanism of the capacitor from the perspective of the essential mechanism of the electrochemical reaction of the supercapacitor and establishes an aging model. Research, and the model parameters used in this type of method are generally obtained according to the physical characteristics of the electrode, so it is difficult to dynamically track changes in environmental conditions with this type of model, which will lead to poor accuracy of the model

At the same time, for a complex and changeable electrochemical system such as a supercapacitor, if it is necessary to describe the degradation characteristics and aging causes in detail, the complexity of the model is relatively high, and there are many parameters, which cause great difficulties for practical application.

The Arrhenius law mainly describes the influence of temperature on the chemical reaction rate, and does not consider the material properties of the electrodes and electrolyte of the capacitor, as well as the charging and discharging voltage, current and other conditions. Therefore, the scope of use has relatively large limitations and the prediction accuracy lower

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