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https://github.com/umerghafoor/battery-degradation-trajectory-prediction
This repository contains Python scripts for reading, analyzing, and visualizing time-series data, specifically related to a battery or energy storage system.
https://github.com/umerghafoor/battery-degradation-trajectory-prediction
data-visualization jupyter-notebook machine-learning python
Last synced: about 16 hours ago
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This repository contains Python scripts for reading, analyzing, and visualizing time-series data, specifically related to a battery or energy storage system.
- Host: GitHub
- URL: https://github.com/umerghafoor/battery-degradation-trajectory-prediction
- Owner: umerghafoor
- Created: 2023-10-08T14:04:20.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-02-26T05:37:39.000Z (11 months ago)
- Last Synced: 2024-02-26T06:33:07.657Z (11 months ago)
- Topics: data-visualization, jupyter-notebook, machine-learning, python
- Language: Jupyter Notebook
- Homepage:
- Size: 246 MB
- Stars: 4
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Time-Series Data Analysis with Python
This repository contains Python scripts for reading, analyzing, and visualizing time-series data, specifically related to a battery or energy storage system. The code uses pandas for data manipulation, matplotlib for plotting, and includes various functions for data processing.
## Table of Contents
1. [Prerequisites](#prerequisites)
2. [Getting Started](#getting-started)
3. [Variables](#variables)
4. [Contributing](#contributing)
## Prerequisites
Before running the scripts, ensure you have the following installed:
- Python (3.x recommended)
- Pandas
- Matplotlib
You can install the required dependencies using:
```
pip install pandas matplotlib pandas numpy
```
# Getting Started
1. Clone this repository:
```
git clone hhttps://github.com/umerghafoor/battery-degradation-trajectory-prediction
```
2. Install dependencies:
```
pip install pandas matplotlib pandas numpy
```
3. Download and palce data in data folder
https://publications.rwth-aachen.de/record/818642
4. place in `data` folder
5. run `extract data.ipynb`
# Variables
- AhAkku: Total ampere-hours. With predominant discharge this value becomes negative [Ah]
- AhEla: Ampere-hours of all executed discharge steps until now [Ah]
- AhLad: Ampere-hours of all executed charge steps until now [Ah]
- AhStep: Ampere-hours of the current program step [Ah]
- Energie: Total energy. With predominant discharge this value becomes negative [Wh]
- Programmdauer: Time [ms]
- Prozedur: (secondary importance) Subprogram currently running.
- Prozedurebene: (secondary importance) Level of the subprogram depth currently running.
- Schritt: The program step that was executed when creating the registry entry [/]
- Schrittdauer: Time since the beginning of the step performed when creating the registry entry [ms]
- Spannung: Voltage [V]
- Strom: Current [A]
- TempX: cell surface temperature [°C]. Number X can be neglected and is cell specific.
- WhStep: Energy of the current program step [Wh]
- Zeit: Unix timestamp
- Zustand: State of the battery tester.
- Zyklus: In programs with loop constructions the Zyklus is an information about how many repetitions of the loop the registration entry was created.
- Zyklusebene: Can be neglected. (only non-zero when the test there is a loop within a loop)
# Contributing
Contributions are welcome! If you find any issues or have suggestions for improvements, please open an issue or create a pull request.