State-of-charge estimation of medium and high voltage batteries using LSTM neural networks optimized with genetic algorithms - CODE and DATA
Description
The research focuses on predicting the battery’s state-of-charge by using historical SOC data as training input, comparing two predictive techniques: LSTM neural networks optimized with genetic algorithms (LSTM+GA) and multiple linear regression (MLR). The results show that the LSTM+GA model presents superior metrics (MSE: 0.0223; RMSE: 0.1494; MAE: 0.181) in comparison with the MLR model (MSE: 0.1590; RMSE: 0.3987; MAE: 0.2925), excelling in solving complex and nonlinear problems.
Files
Steps to reproduce
Installation Guide for the Use and Application of LSTM Neural Networks Optimized via Genetic Algorithms to Estimate the State of Charge of Medium and High Voltage Batteries 1. **Installation of Anaconda** Download from: [https://www.anaconda.com](https://www.anaconda.com) 2. **Create a New Environment** In the "environments" section, create a new environment. Select Python version 3.10.16. 3. **Install Libraries** In the created environment, open the terminal. Install the following libraries: Deap, tensorflow, sklearn, matplotlib, numpy, pandas, os. 4. **Run the Code** Once all the libraries have been installed, open Jupyter Notebook. From the Jupyter browser, navigate to the folder where the code and time series are located. - **Folder Name:** LSTM-OPTIMIZED AG Open the notebook: `LSTM-GA.ipynb` Verify that the folder contains the .csv files (soc_wltp_2.csv). Run the code. - **Folder Name:** MLR Open the notebook: `graficas metricas.ipynb` Verify that the folder contains the .csv files (soc_wltp_2.csv). Run the code.