cff-version: 1.2.0
abstract: "<p>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: <em>"Computational Micromechanics and Machine Learning-Informed Design of Composite Carbon Fiber-Based Structural Battery for Multifunctional Performance Prediction."</em></p><p>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).</p>"
authors:
  - family-names: Raja
    given-names: Mohamad A.
    orcid: "https://orcid.org/0009-0007-4646-9751"
  - family-names: Kim
    given-names: Seong Su
    orcid: "https://orcid.org/0000-0001-8722-0505"
title: "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”"
keywords:
version: 1
identifiers:
  - type: doi
    value: 10.4121/2040ea92-10a9-4b56-b1b0-36bcaddf0762.v1
license: MIT
date-released: 2025-05-19