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
- Data Introduction
- Data Visualization with Machbase Neo
- Table Creation and Data Upload in Machbase Neo
- Experimental Methodology
- Experiment Code
- 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) |
- Data Time Range: 2010-07-21 15:00:00 to 2010-07-21 15:47:00.
- Number of data records collected: 4,615,024.
- CSV data URL: https://data.yotahub.com/2024-16/datahub-2024-16-NASA-Lithium_battery.csv.gz
- Data Migration: NASA Lithium Battery Data Migration
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.
- The entire code can be run through 16.NASA_Battery_General.ipynb.
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.
- The entire code can be run through 16.NASA_Battery_New_Batch.ipynb.
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