Electrical and Computer Engineering Faculty Publications

Intelligent Battery Performance Prediction Based on Deep Learning Algorithm

Document Type

Conference Proceeding

Publication Date

11-16-2026

Abstract

The development of advanced batteries has become an important area to meet the rapidly growing demands of modern applications, such as electric vehicles, energy storage, and portable devices. However, traditional design methods of batteries usually utilize computing-consuming numerical simulation and time-consuming real-world experiments, which have limited the exploration of modern batteries. The emerging deep learning algorithms have the ability to analyze complex and high-dimensional datasets, making it possible to change the game in battery design process. In this paper, we propose a comprehensive framework that combines deep learning (DL) algorithms with essential battery parameters to predict battery performance under different operational conditions. A DL model is established using the dataset from various battery experiments to accurately predict the key metrics, e.g., charge-discharge efficiency and lifecycle performance. By enhancing prediction accuracy, our method will increase design efficiency while achieving the required battery performance, which will drive innovation in battery technology and contribute to the development of next-generation energy storage systems.

Comments

https://rdcu.be/Y5N7vrKEPdna

First Page

137

Last Page

146

Publication Title

Lecture Notes in Mechanical Engineering

DOI

https://doi.org/10.1007/978-3-032-07675-5_8

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