Data-driven model for predicting the current cycle count of power batteries based on model stacking

Electric vehicles have been heavily promoted in recent years to reduce carbon emissions. With rising fuel prices, more and more electric vehicles are being chosen by consumers. The safety performance of electric vehicle batteries is an indicator of great concern to the new energy vehicle industry and consumer.Many researchers have used machine learning to train on data to obtain models that can predict the current or the remaining number of battery cycles to achieve battery safety management. To further improve the model's prediction accuracy, this work designs an algorithm structure combining a neural network and a gradientboosting decision tree (GBDT) class. The neural network structure is in two layers, the first layer is a parallel network composed of deep neural networks (DNN) and long short-term memory (LSTM), and the other layer is a network with a DNN structure. The trained neural network model is used to transform the input features into new features, and then the CatBoost algorithm is used to train the old and new features to obtain the prediction model. The results show that the method reduces the mean absolute error (MAE) by 41 % on the original basis. And it provides an idea for the research of related problems.

Automated summary

This study designs an algorithm structure that combines neural networks and gradient boosting decision trees to predict the safety performance of electric vehicle batteries, leading to improved prediction accuracy.