NASA Lithium Battery Data

NASA Lithium Battery Data

This post describes how to use NASA Lithium Battery Data to predict Remaining Useful Life (RUL) through AI learning.

Table of Contents

  1. Data Introduction
  2. Data Visualization with Machbase Neo
  3. Table Creation and Data Upload in Machbase Neo
  4. Experimental Methodology
  5. Experiment Code
  6. Experimental Results

1. Data Introduction


  • DataHub Serial Number: 2024-16.
  • Data Name: NASA Lithium Battery Data.
  • Data Collection Methods: Collected from a custom-designed battery prediction testbed developed by the NASA Ames Prognostics Center of Excellence (PCoE).
  • The testbed comprises:
    • Commercially available Li-ion 18650 sized rechargeable batteries.
    • Programmable 4-channel DC electronic load.
    • Programmable 4-channel DC power supply.
    • Voltmeter.
    • ammeter and thermocouple sensor suite.
    • Custom EIS equipment.
    • Environmental chamber to impose various operational conditions.
    • PXI chassis based DAQ and experiment control.
  • Data Source: Link
  • Raw data size and format: 574MB, CSV.
  • Number of tags: 16739 (Discharge data for each battery).
TAG Description Unit
Voltage_measured Battery terminal voltage Volts (V)
Current_measured Battery output current Amperes (A)
Temperature_measured Battery temperature Celsius (°C)
Current_charge Current measured at the load Amperes (A)
Voltage_charge Voltage measured at the load Volts (V)
Time Time vector of the cycle Seconds (s)
Capacity Battery capacity discharged down to 2.7V Ampere-hours (Ah)

2. Data Visualization with Machbase Neo


  • Data visualization is possible through the Tag Analyzer in Machbase Neo.
  • Select desired tag names and visualize them in various types of graphs.
  • Below, access the 2024-16 DataHub in real-time, select the desired tag names from the data of 16739 tags, visualize them, and preview the data patterns.
DataHub Viewer

3. Table Creation and Data Upload in Machbase Neo


  • In the DataHub directory, use setup.wrk located in the NASA Lithium Battery Dataset folder to create tables and load data, as illustrated in the image below.

1) Table Creation

  • The table is created immediately upon pressing the "Run" button in the menu.
  • If the nasa_battery table exists, execute the first line and then the second. If it does not exist, start from the second line.

2) Data Upload


  • Loading tables in two different ways.
Method 1) Table loading method using TQL in Machbase Neo (since machbase-neo v8.0.29-rc1

  • Pros

    • Markbase Neo loads as soon as you hit the launch button.
  • Cons

    • Slower table loading speed compared to other method.
Method 2) Loading tables using commands

  • Pros

    • Fast table loading speed.
  • Cons

    • The table loading process is cumbersome.
    • Run cmd window - Change machbase-neo path - Enter command in cmd window.
  • If run the below script from the command shell, the data will be entered at high speed into the nasa_battery table.
curl http://data.yotahub.com/2024-16/datahub-2024-16-NASA-Lithium_battery.csv.gz | machbase-neo shell import --input - --compress gzip --header --method append --timeformat ns nasa_battery
  • If specify a separate username and password, use the --user and --password options (if not sys/manager) and add the options as shown below.
curl http://data.yotahub.com/2024-16/datahub-2024-16-NASA-Lithium_battery.csv.gz | machbase-neo shell import --input - --compress gzip --header --method append --timeformat ns nasa_battery --user USERNAME --password PASSWORD 

4. Experimental Methodology


  • Model Objective: Lithium Battery RUL Prediction.
  • Tags Used: B0001, B0005, B0007 Battery Discharge data.
  • Model Configuration:
  • Health Index Extraction model: LSTM AutoEncoder.
  • Health Index Forecasting model: Dlinear.
  • Learning Method: supervised Learning.
    • Train: Health Index Extraction Model Training.
    • Validation: Health Index Forecasting Model Training.
    • Test: Model Performance Evaluation Based on Health Index Forecasting.
  • Model Optimizer: Adam.
  • Model Loss Function: Mean Squared Error.
  • Model Performance Metric: Mean Squared Error & R2 Score.
  • Data Loading Method
    • Loading the Entire Dataset.
    • Loading the Batch Dataset.
  • Data Preprocessing
    • MinMax Scaling.

5. Experiment Code


  • Below is the code for each of the two ways to get data from the database.
  • If all the data can be loaded and trained at once without causing memory errors, then method 1 is the fastest and simplest.
  • If the data is too large, causing memory errors, then the batch loading method proposed in method 2 is the most efficient.

Method 1) Loading the Entire Dataset


  • The code below is implemented in a way that loads all the data needed for training from the database all at once.
  • It is exactly the same as loading all CSV files (The only difference is that the data is loaded from Machbase Neo).
  • Pros
    • Can use the same code that was previously utilizing CSVs (Only the loading process is different).
  • Cons
    • Unable to train if trainable data size exceeds memory size.

Method 2) Loading the Batch Dataset


  • Method for loading data from the Machbase Neo for a single batch size.
  • The code below is for fetching a time range sequentially for a single batch size.
  • Pros
    • It is possible to train the model regardless of the data size, no matter how large it is.
  • Cons
    • It takes longer to train compared to method 1.

6. Experimental Results


Method 1) Loading the Entire Dataset Result


Method 2) Loading the Batch Dataset Result


  • The green dashed line in the result graph represents the actual failure point.
  • The R2 score for loading the entire dataset resulted in 0.98, loading the batch dataset resulted in same 0.93.




※ Various datasets and tutorial codes can be found in the GitHub repository below.

datahub/dataset/2024 at main · machbase/datahub
All Industrial IoT DataHub with data visualization and AI source - machbase/datahub

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