Code for Optimized ANN-Based Prediction of Battery Capacity Using Voltage/Current Cycling Data. Related Paper: “Computational Micromechanics and Machine Learning-Informed Design of Composite Carbon Fiber-Based Structural Battery for Multifunctional Performance Prediction”

DOI:10.4121/2040ea92-10a9-4b56-b1b0-36bcaddf0762.v1
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/2040ea92-10a9-4b56-b1b0-36bcaddf0762

Datacite citation style

Raja, Mohamad A.; Kim, Seong Su (2025): Code for Optimized ANN-Based Prediction of Battery Capacity Using Voltage/Current Cycling Data. Related Paper: “Computational Micromechanics and Machine Learning-Informed Design of Composite Carbon Fiber-Based Structural Battery for Multifunctional Performance Prediction”. Version 1. 4TU.ResearchData. software. https://doi.org/10.4121/2040ea92-10a9-4b56-b1b0-36bcaddf0762.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Software

This repository contains MATLAB code for predicting battery capacity using an Artificial Neural Network (ANN) trained on structured cycling data. The script utilizes Bayesian optimization to fine-tune hyperparameters, enabling more accurate forecasting of capacity degradation over time. This code was used in the paper titled: "Computational Micromechanics and Machine Learning-Informed Design of Composite Carbon Fiber-Based Structural Battery for Multifunctional Performance Prediction."

It is a clear and modular code that takes voltage and current data as input features, performs normalization, splits the data into training/validation/testing sets, and builds an ANN using MATLAB’s Deep Learning Toolbox. In my case, the code was applied to carbon fiber-based structural battery data to evaluate long-term electrochemical performance. This code was developed during my Master’s research at KAIST (Korea Advanced Institute of Science and Technology).

History

  • 2025-05-19 first online, published, posted

Publisher

4TU.ResearchData

Format

MATLAB/.m/.mat Image/.png

Organizations

Korea Advanced Institute of Science & Technology (KAIST), Department of Mechanical Engineering
TU Delft, Faculty of Aerospace Engineering, Department of Aerospace Structures and Materials

DATA

Files (10)