A Prediction Method of Supercapacitor Capacitance Degradation Trend Based on Support Vector Machine

Abstract

The invention discloses a method for predicting the degradation trend of the capacitance value of a supercapacitor based on a support vector machine. The prediction method uses the regression prediction function of the support vector machine to predict the degradation trend of the capacitance value of the supercapacitor, including: 1) input value 2) Train the training set data to obtain the regression estimation function; 3) Use the particle swarm optimization algorithm to automatically optimize the relevant parameters of the support vector machine; 4) Set the support vector machine according to the optimization results. The corresponding parameter values ​​of the vector machine, the data of the training set are substituted into the relevant vector machine model, and the regression prediction model of the degradation trend of the capacitance value is obtained; 5) the data of the test set are substituted into the regression prediction model, and the prediction degradation trend of the capacitance value is obtained. The invention can realize the real-time on-line prediction of the degradation trend of the capacitance value; by introducing the particle swarm optimization algorithm, the parameter optimization method is improved, the prediction efficiency and precision of the algorithm are improved, and the applicability is wide.

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...

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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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