Lithium ion battery remaining service life prediction method based on long and short term memory LSTM and particle filter PF

Abstract

The invention discloses a lithium ion battery remaining service life prediction method based on long and short term memory LSTM and particle filter PF, and belongs to the field of new energy electricvehicle lithium ion battery remaining service life prediction. The method comprises the following specific steps: analyzing performance degradation characteristic parameters of the lithium ion batteryextracted from voltage, current and temperature of the lithium ion battery, fusing the characteristic parameters by utilizing an improved principal component analysis method to serve as a health index of the lithium ion battery, and fully characterizing performance degradation characteristics of the lithium ion battery without redundant information; training a lithium ion battery capacity prediction model based on a long-and-short-term memory neural network to predict the capacity of the lithium ion battery; and taking the capacity prediction value of the LSTM prediction model as the observation value of the particle filtering prediction model, adjusting and updating the capacity prediction value in each iteration process of the particle filtering algorithm, and comparing the capacity prediction value with the capacity failure threshold so as to predict the residual service life of the lithium ion battery. The method can effectively monitor and predict the performance degradation process of the lithium ion battery.

Description

technical field [0001] The invention belongs to the technical field of lithium-ion batteries for electric vehicles, and more specifically relates to a method for predicting the remaining service life of lithium-ion batteries based on LSTM and PF. Background technique [0002] As people's awareness of environmental protection continues to increase, new energy electric vehicles are favored by users. Lithium-ion batteries are widely used in new energy electric vehicles due to their many advantages such as good safety, high specific energy, and high charge and discharge efficiency. However, performance degradation of lithium-ion batteries is inevitable, and performance degradation can easily cause system failures in new energy electric vehicles. even catastrophic accidents. Therefore, it is of great significance to find an accurate and reliable prediction method for the remaining service life of lithium-ion batteries, accurately predict the remaining service life of lithium-ion...

AI Technical Summary

Problems solved by technology

Aiming at the problem that the particle filter prediction model has no measurement value in the prediction stage, the state cannot be updated and the prediction accuracy is low. The present invention uses the capacity prediction value obtained by the LSTM prediction model as the observation value, and uses the particle filter algorithm to iteratively update the capacity prediction value, and compares the capacity predictions. value and failure threshold to predict the remaining service life of lithium-ion batteries and improve prediction accuracy

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