Lithium battery SOH long-term prediction method based on multi-battery data fusion

Abstract

The invention discloses a lithium battery SOH long-term prediction method based on multi-battery data fusion. The method comprises the steps of collecting data of the same kind of lithium batteries in a charging and discharging process, preprocessing, constructing an input matrix of multi-battery data fusion, and sending the input matrix into a multiple-input multiple-output long-short-term memory network model for training; preprocessing the data of the predicted batteries in real time and then sending to the multiple-input multiple-output long-short-term memory network model for prediction; collecting a historical prediction result after prediction and historical real data in the charging and discharging process, and training an NARNN model; and taking the prediction result at the current moment as the input of the NARNN model, and outputting a health state parameter SOH among a plurality of times of charging and discharging in the future. The method overcomes the defects that a traditional battery SOH prediction algorithm is only used for modeling the predicted batteries, generalization is weak, and long-term prediction precision is low. Training samples are greatly increased, and model combination is optimized, so that the accuracy of model prediction is improved, and the accuracy of SOH long-term prediction is improved.

Description

technical field [0001] The invention relates to a method for measuring battery parameters in the field of battery health control, and relates to a method for long-term prediction of lithium battery state of health (SOH) based on Many to Many LSTM-NARNN based on multi-battery data fusion. Background technique [0002] Due to the advantages of long cycle life, high energy density, and good safety, lithium batteries have been widely used in many important scenarios, including electric vehicles, smartphones, etc. However, there are still many important factors restricting the development of lithium batteries, one of which is battery aging. As the lithium battery ages, the performance of the battery will gradually decline. If the new battery is not replaced in time, this will affect the performance of the battery-powered object, and sometimes even have a devastating impact on it. Battery State of Health (SOH) is an important indicator to evaluate the current battery performance,...

AI Technical Summary

Problems solved by technology

However, the data-driven method depends on the size of the data. The small amount of data cannot train an accurate

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