%0 Computer Program
%A Raja, Mohamad A.
%A Kim, Seong Su
%D 2025
%T 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”
%U 
%R 10.4121/2040ea92-10a9-4b56-b1b0-36bcaddf0762.v1
%K Battery capacity prediction
%K Artificial neural network
%K Bayesian optimization
%K MATLAB
%K Machine learning
%X <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>
%I 4TU.ResearchData