Prediction method for battery self-discharge voltage drop based on pre-classifier

The self-discharge voltage drop (SDV-drop) is an important indicator in measuring the performance of lithium -ion batteries. Traditional SDV-drop measurement methods are time-consuming and require considerable manpower and material resources. This study proposes a method for predicting battery SDV-drop based on pre-classifier. The features for predicting the SDV-drop are obtained in two ways: direct extraction from the charge-discharge curve and generation based on the classifier. Subsequently, the features are input into the BP neural network model optimized by the Particle Swarm Optimization (PSO) and Levenberg-Marquardt (L-M) algorithms to predict the SDV-drop. Simulation results show that the proposed method can accurately estimate the SDV-drop.

Automated summary

This study proposes a method for predicting the self-discharge voltage drop (SDV-drop) of lithium-ion batteries based on a pre-classifier. The method obtains features for predicting SDV-drop through direct extraction and generation from the charge-discharge curve, which are then input into an optimized neural network model to accurately predict SDV-drop.