phillipblunck/master-thesis

Repository of my master’s thesis "Development and evaluation of a model for predicting the state of health of traction batteries based on artificial neural networks"

master-thesis

Repository meiner Masterarbeit "Entwicklung und Evaluierung eines Modells zur Prognose des State of Health von Traktionsbatterien auf Basis künstlicher neuronaler Netze"

Repository of my master’s thesis "Development and evaluation of a model for predicting the state of health of traction batteries based on artificial neural networks"

Running instructions

To use this project, you have to install a linux distribution. In this case you can use Ubuntu 20.04.3 LTS. It comes with the build automation tool make. It is used to run the commands in the terminal.

First start

After you cloned this repository, you should use the following command to prepare your system.

make prepare

It installs all necessery libraries and Python packages.
After this you need to generate three directories.

mkdir models data

Example

After the preperation you can start using the main steps to calculate the internal resistance of a specific battery.

1. Place a single logfile into the directory data
2. Run make signals
3. Set the limits of the signal section in seconds in the makefile (LO_LIM and UP_LIM)
4. Run make resample to resample the signal section
5. Run make training to train the MLP and SVM with the resampled signal section
6. Run make simulation to calculate the internal resistance.

Predicting the state of health

If you want to predict the SoH, you have to repeat the steps shown in the example. Each time you calculate the interal resistance, save the values into the discharge_resistances.csv file. Remember to only place one logfile at a time in the directory data. After you calculated enough values you can run the command make resistance to get a diagram with all the calculated internal resistances of the csv-file.
Now you can make a prediction about the SoH based on the course of the internal resistances.