Generating Comprehensive Lithium Battery Charging Data with Generative AI

TL;DR

This paper introduces a novel generative AI model, RCVAE, that synthesizes comprehensive lithium battery electrochemical data, addressing data scarcity and quality issues to improve battery state prediction.

Contribution

The study develops the RCVAE model with an embedding layer and quasi-video data preprocessing to generate detailed battery data for EOL and ECL conditions, pioneering data synthesis in this domain.

Findings

Successfully generates electrochemical data including voltage, current, temperature, and capacity.

Enables creation of comprehensive datasets for battery state prediction.

Provides a new approach to data augmentation in lithium battery research.

Abstract

In optimizing performance and extending the lifespan of lithium batteries, accurate state prediction is pivotal. Traditional regression and classification methods have achieved some success in battery state prediction. However, the efficacy of these data-driven approaches heavily relies on the availability and quality of public datasets. Additionally, generating electrochemical data predominantly through battery experiments is a lengthy and costly process, making it challenging to acquire high-quality electrochemical data. This difficulty, coupled with data incompleteness, significantly impacts prediction accuracy. Addressing these challenges, this study introduces the End of Life (EOL) and Equivalent Cycle Life (ECL) as conditions for generative AI models. By integrating an embedding layer into the CVAE model, we developed the Refined Conditional Variational Autoencoder (RCVAE). Through preprocessing data into a quasi-video format, our study achieves an integrated synthesis of electrochemical data, including voltage, current, temperature, and charging capacity, which is then processed by the RCVAE model. Coupled with customized training and inference algorithms, this model can generate specific electrochemical data for EOL and ECL under supervised conditions. This method provides users with a comprehensive electrochemical dataset, pioneering a new research domain for the artificial synthesis of lithium battery data. Furthermore, based on the detailed synthetic data, various battery state indicators can be calculated, offering new perspectives and possibilities for lithium battery performance prediction.