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”
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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).
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