Abstract
Aiming at the accelerated aging problem that may occur during the use of high specific energy lithium-ion batteries, this article proposes a method to judge the accelerated aging of lithium-ion batteries. Taking the IC curve and DV curve as the starting point, the complete characteristic curve of the new battery is used as a comparison benchmark, and the battery characteristic curves of different aging stages are compared and analyzed, and the parameters that have obvious changes before and after the accelerated aging of the battery are extracted as the characteristics that the battery has accelerated aging. It is important to establish the relevant characteristic parameter matrix, and use the logistic regression method to train the accelerating aging model of the battery to realize the diagnosis of accelerating aging fault. In view of the fact that lithium batteries rarely undergo a complete charging process, the characteristic parameter sets established in this paper are based on the IC curve and the DV curve in the 15–75% SOC range, which have certain practicability.
Highlights
Aging of Lithium-Ion Battery BasedIn order to reduce greenhouse gas emissions and pollution caused by fossil fuels, the promotion of new energy vehicles worldwide has become a general trend
A characteristic parameter system including the distance between the DV curve
This paper uses the logistic regression method to realize the judgment of the accelerated aging fault of the lithium battery. This method can solve the situation that the complete charging curve of the battery cannot be obtained in practice to a certain extent, focusing on the extraction of the charging curve only in the 15–75% SOC range. It aims to characterize the characteristic parameters of accelerating battery aging, establish the relevant characteristic parameter matrix, and use it as the training set of the logistic regression method
Summary
In order to reduce greenhouse gas emissions and pollution caused by fossil fuels, the promotion of new energy vehicles worldwide has become a general trend. This difference will cause the model to underfit in the long-term prediction process; within the literature [7], starting from the lithium-ion battery charging voltage and charging current curve, a method for battery capacity prediction based on the sparse Bayesian learning method is proposed. The parameters are used to predict the battery capacity, but the complete charging curve of the battery needs to be obtained, and the applicability is general Another way is to extract the characteristic parameters that have a strong correlation with capacity changes during battery cycles, and use the characteristics of different characteristics of the characteristic parameters before and after the inflection point of the battery capacity diving to set the threshold for stable battery operation. The characteristic parameters of the lithium battery characterize the accelerated aging state of the lithium battery; based on the main peak area of the IC curve of the new battery, the change of the main peak area of the IC curve of the lithium battery during the aging process is analyzed to characterize the aging state of the lithium battery
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