Deep Learning Approaches to Predict the Life Cycle of Lithium-Ion Batteries in Electric Vehicles

<div class="section abstract"><div class="htmlview paragraph">Predictive maintenance is crucial for Industry 4.0, and deep neural networks are a promising approach for predicting the capacity of electric batteries. However, few applications effectively utilize neural networks for this purpose with lithium-ion batteries. In this work, different deep learning models are developed, starting with simple neural networks, dense neural networks, convolutional networks, and recurrent networks. Using a public domain dataset, training, testing, and validation datasets were generated to predict battery capacity as a function of the number of cycles. Despite the limited number of samples in the dataset, deep learning techniques are employed to ensure robust prediction performance. The work presents the loss functions for each iteration of the algorithms and the average absolute error. The models made good generalizations over the test dataset within a short prediction time window. Finally, the work presents an average absolute error below 0.3, ensuring good convergence, avoiding data overfitting, and ensuring good generalization of the model to the test set. This work is a first estimate of the life cycle of electric vehicle batteries using small samples as inputs for estimating the remaining useful life of this electric vehicle component.</div></div>