A Method for Predicting the Cycle Life of Lithium-ion Batteries Based on Acoustic Measurements

Abstract

The invention discloses a lithium ion battery cycle life prediction method based on acoustic measurement. The method comprises the following steps that: (1) charge-discharge circulation is carried outon a lithium ion battery, and ultrasonic scanning is carried out on the lithium ion battery during the charging and discharging of the lithium ion battery or in the intermittent period of the charging and discharging of the lithium ion battery to obtain ultrasonic transmissivity and make an ultrasonic transmission scanning image; (2) the number of circulation turns corresponding to a region withthe ultrasonic transmissivity less than or equal to K and the area more than or equal to A on the ultrasonic transmission scanning image is defined as a sound stability life; and (3) the predicted cycle life of the to-be-measured lithium ion battery is equal to the product of the sound stable life of the to-be-measured lithium ion battery and life multiplying power Sc, wherein the life multiplyingpower Sc is the result of dividing the real cycle life of a lithium ion battery in the same system as the to-be-measured lithium ion battery by the sound stable life of the lithium ion battery. By utilizing the multiplying power relationship between the sound stable life and the cycle life of the lithium ion battery, the time required for predicting the cycle life of the lithium ion battery can be greatly shortened.

Description

technical field [0001] The invention belongs to the field of lithium-ion batteries, and more particularly relates to a method for predicting cycle life of lithium-ion batteries. Background technique [0002] As a new type of energy, lithium-ion batteries have been used more and more in daily life in recent years because of their advantages such as high working voltage, large specific energy, high charge and discharge efficiency, low self-discharge rate, and no memory effect. In addition to daily devices such as mobile phones, tablets, and notebooks, lithium-ion batteries are also widely used in important fields such as aerospace vehicles, satellites, electric vehicles, and drones. During the long-term use of lithium-ion, a series of electrochemical reactions and physical changes will occur inside, which will seriously affect the life and safety of lithium-ion batteries, and even cause property damage and casualties. Predicting the life of lithium-ion batteries can help the ...

AI Technical Summary

Problems solved by technology

However, the computational efficiency of artificial neural networks is low, the algorithm complexity is high, the convergence speed is slow, and the training time is long.

2) The support vector machine takes the principle of structural risk minimization as the theoretical basis, and has a strong learning generalization ability, and overcomes the problems of slow convergence speed and local extremum in the artificial neural network, but there are still some problems that are difficult to determine Penalty factors and loss functions, sensitive to error boundaries and lack of uncertainty expression ability in prediction results

[0007] In general, the existing prediction methods for the cycle life of lithium-ion batteries are based on the measured values ​​of the electrical characteristics of lithium-ion batteries. Since the early changes of many lithium-ion batteries do not lead to significant changes in electrical characteristics, the current Some methods have many deficiencies in prediction time, parameter measurement, model adaptability, convergence speed, prediction accuracy, etc.

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