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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.

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umerghafoor/battery-degradation-trajectory-prediction

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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
  2. Getting Started
  3. Variables
  4. 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
  1. Install dependencies:
pip install pandas matplotlib pandas numpy
  1. Download and palce data in data folder https://publications.rwth-aachen.de/record/818642
  2. place in data folder
  3. 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.

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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.

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