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Created By: Manivannan Sivan Public Project Link: https://studio.edgeimpulse.com/public/102553/latest
Wio Terminal prototype monitoring a lithium-ion battery pack with a thermal camera

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Faulty Lithium ion Cell BMS Pack

This prototype uses a Wio Terminal and Edge Impulse to predict overheated faulty cells in a BMS pack. For this project, I used an MLX 90640 Thermal Camera with the Wio Terminal to collect thermal data from a BMS pack. A working demo of my prototype is available on YouTube here:

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Problem Statement

In an existing BMS pack, a temperature sensor is integrated with each cell pack, consisting of 14 cells, for identifying an overheated cell pack. But there is no system to identify an individual faulty cell that is overheating in a BMS pack.
Existing BMS architecture with temperature sensors monitoring cell groups

Existing BMS pack architecture


Battery pack diagram showing one temperature sensor for multiple cells
  • Only one temperature sensor is deployed to detect the overall temperature of battery packs (14 * Li-ion Cells).
  • Identifying the individual cell temperature is challenging due to infrastructure cost for a BMS pack.
Cost for deploying Temperature sensor for each cell:
  • Number of cells in BMS pack: 112
  • Cost of Temperature sensor: 500 INR ($0.75 USD)
  • Total cost: 112 * 500 INR = 56,000 INR ($760 USD) *approx
Additionally, there is no interface support in a microcontroller to support 112 individual temperature sensor readings.

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Solution using TinyML model

Proposed TinyML architecture using a Wio Terminal and thermal camera
I have developed a prototype by using the MLX90640 thermal camera and Wio Terminal to collect the thermal data of the BMS pack and uploaded the data sets (Label: Faulty Battery 1…6 and “Normal” to Edge Impulse).

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Hardware Setup

In this prototype, 6 lithium-ion cells are connected to the load (Rheostat) and the MLX90640 and Wio Terminal are attached to the stand where the MLX90640 thermal camera is facing downwards over the lithium-ion cells.
Thermal camera mounted above lithium-ion cells connected to the Wio Terminal

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Algorithm

The MLX90640 sends 32x24 thermal data to the Wio Terminal through I2C. Since this project focuses on identifying an overheated cell in the pack, I have used simple filtering logic to filter out the normal cell temperature by setting it to zero.
Filtering algorithm diagram for isolating overheated cells in thermal data
Upload the datasets created for this project from the below link. Go to Edge Impulse -> Data acquisition and then the Uploader option to upload the datasets.
Edge Impulse Data acquisition page for uploading thermal datasets
If you want to develop new datasets from scratch, flash the below code to the Wio Terminal using the Arduino IDE. For that, you need to configure Wio Terminal setup in the Arduino IDE. Please follow this link to get setup: https://wiki.seeedstudio.com/Wio-Terminal-Getting-Started/ This code will print the thermal data in array format, later it can be converted to .csv format as mentioned in the above datasets. Ideally the .csv data looks like this:
Example CSV table containing thermal array values for model training
Once the datasets are uploaded, then in the “Create impulse” section change the Window size to 768 ( 24*32 = 768 ).
Create impulse page configured for a 768-value thermal input
Next, in Feature Explorer, we can see the generated raw features of thermal data.
Feature Explorer view of generated raw thermal features

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Neural Network Configuration

Classifier settings page for the thermal cell model
I have used reshape to change the 1D data to 2D data with 24 columns (due to placement of the thermal camera) , in some cases it might be 32 to get the best accuracy. Then I have included couple of 2D conversion layers with pool layers, followed by a Flatten layer. Then 2 DNN (30 neurons , 10 neurons) in sequential is used.
Neural network architecture layers for reshaping and classifying thermal data

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Deployment

In the Deployment section , select Arduino code and download the firmware package.
Deployment page with Arduino code selected for the Wio Terminal
Then add the Zip file as a Library in Arduino IDE.
Arduino IDE library menu for adding the downloaded ZIP package
Once it is added, download the final application code from this GitHub link (repo has since been deleted), and flash it to the Wio Terminal.

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Output

In a model training, 100% accuracy is achieved, and in model testing 87.5% accuracy is achieved.
Model testing output showing accuracy for faulty cell classification
In normal case, when all the battery in the pack is operating in normal temperature.
Wio Terminal display indicating normal battery pack temperature
In a faulty battery condition, the model will predict the cell location index and display it with a predicted value. In this particular setup, a faulty cell is placed in location 5 and discharged for 1 hour. The cell gets overheated, and the model predicts the overheated cell location, number 5 in this battery pack.
Wio Terminal display predicting the overheated cell location
If you cannot create a faulty cell for testing, you can simulate it using this method. Place a heated soldering iron on top of (near, but do not touch!) a battery cell, or move the soldering iron from across the battery pack from cell 1 to cell 6 in the pack. The model will predict the overheated cell locations as 1 to 6, as the soldering iron moves from 1 to 6. By adding the heat from the soldering iron, you can simulate the faulty battery condition and test it.
Soldering iron heating a battery cell to simulate a fault

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Schematics

Wiring schematic for the Wio Terminal thermal camera prototype

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Summary

This project demonstrated a cheap and effective way to use computer vision and thermal imaging using the Wio Terminal, to identify lithium ion battery cells that are overheating, in more granular fashion than would be normally possible. This is a prototype of course, but could be used in robotics, automated warehouse and forklift devices, electric vehicles, or other places where batteries are arranged into packs.