Battery life prediction method and system, electronic device and storage medium

Abstract

The invention discloses a battery life prediction method and system, an electronic device and a storage medium. The method comprises the following steps: acquiring historical data of battery capacity; preprocessing the historical data to obtain principal component data and secondary component data of battery capacity attenuation; inputting the principal component data and the secondary component data into a pre-trained long short-term memory neural network; receiving an output result of the long short-term memory neural network, and processing the output result to obtain an attenuation sequence of the battery capacity; and judging whether the numerical value in the attenuation sequence reaches a preset battery failure threshold value or not so as to predict the residual life of the battery, and inputting historical data and a prediction result into a long-short-term memory neural network for reverse training, so that generalization, long-term and effective prediction of the service life of the battery can be realized.

Description

technical field [0001] The invention relates to the technical field of batteries, in particular to a battery life prediction method, system, electronic device and storage medium. Background technique [0002] With the development of new energy technologies, lithium-ion batteries have been widely used in many important fields. However, lithium-ion batteries still face many challenges, one of which is performance degradation. There are many factors involved in performance degradation. For example, when many chemical side reactions of the anode, electrolyte, and cathode are affected, the performance of the battery will be degraded, and the battery’s capacity is partially regenerated, self-charging phenomenon, user habits, and ambient temperature. Under factors such as road vibration and road vibration, the battery capacity may be attenuated, which will affect the battery life. [0003] Therefore, it is of great significance to predict the remaining life of the battery to ensur...

AI Technical Summary

Problems solved by technology

[0004] However, the existing battery life prediction methods do not separate the effects of various factors that cause battery capacity fading, or can only achieve short-term predictions, or can only predict the life of a specific battery, so they cannot achieve generalization, long-term and valid forecast

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