BMS-DNN

KO | EN

gitlite — search

#python #ai #llm #golang #go #android #machine-learning #rust #deep-learning #linux #docker #awesome #openai #open-source

(★ 17)

This project implements a battery management system (BMS) that uses a deep neural network (DNN) to predict the state of charge (SOC) of a battery based on measured sensor data. The BMS is implemented in Python using the TensorFlow and Keras libraries.

BMS-DNN Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# BMS-DNN
This project implements a battery management system (BMS) that uses a deep neural network (DNN) to predict the state of charge (SOC) of a battery based on measured sensor data. The BMS is implemented in Python using the TensorFlow and Keras libraries.

## Requirements
To run this project, you will need the following software:

pip install -r requirements.txt

## Data
The BMS uses a synthetic dataset of sensor data and corresponding SOC values. The dataset includes the following features:
- Voltage_measured
- Current_measured
- Temperature_measured
- Current_load
- Voltage_load
- Time
- SOC

## Training the DNN
The DNN model is trained on the synthetic dataset using the following steps:
- Load the dataset using Pandas.
- Split the dataset into features and labels.
- Split the data into training and testing sets.
- Scale the features using a StandardScaler.
- Define the DNN model using Keras.
- Compile the model using an optimizer and loss function.
- Train the model using the training data.
- Evaluate the model using the testing data.
- Save the model to a file.

## Simulating the BMS
The BMS simulates real-time data collection and SOC prediction using the trained DNN model. The simulation is implemented in the following steps:
- Load the trained DNN model from a file.
- Define a function to generate simulated sensor data.
- Define a function to preprocess the sensor data and make a SOC prediction using the DNN model.
- Run a loop that generates simulated sensor data, preprocesses the data, makes a SOC prediction, and prints the prediction to the console.
- Sleep for a short amount of time between iterations to simulate real-time data collection.

## Adding PID Control
To add PID control to the BMS, the following steps are implemented:
- The PID algorithm is implemented in a separate module.
- The BMS simulation script is modified to import the PID control module and use its functionality to adjust the charging and discharging rates of the battery based on the predicted SOC.

## Conclusion
This project demonstrates the implementation of a battery management system using a deep neural network to predict the state of charge of a battery. The system can be extended by adding PID control to adjust the charging and discharging rates of the battery to maintain a desired SOC.

here's a table of the example dataset used in this project:

| Voltage_measured | Current_measured | Temperature_measured | Current_load | Voltage_load | Time           | SOC           |
|------------------|-------------------|----------------------|--------------|--------------|----------------|--------------|
| 3.136718202      | -0.629734142     | 14.15969893          | 0.345405988  | 3.472392993  | 393.6355203   | 0.648256954  |
| 4.116214321      | 0.083801895      | 13.64425797          | 0.593362794  | 3.86923496   | 473.4356594   | 0.172386362  |
| 3.744389701      | 0.745891672      | 36.71891032          | -0.499064202 | 3.792273581  | 854.5473932   | 0.872394563  |
| 3.517719423      | 0.464449773      | 13.73411699          | 0.249748199  | 2.761629838  | 340.0043861   | 0.613116239  |
| 2.765231689      | 0.613122296      | 14.51824041          | 0.143491966  | 2.753724099  | 869.6496848   | 0.157203884  |
| 2.765190685      | 0.317566733      | 31.57893918          | 0.665660754  | 2.955896425  | 88.13443098   | 0.962338057  |
| 2.598742141      | 0.384553129      | 20.74089449          | 0.812174121  | 3.113827035  | 776.7984372   | 0.518365463  |

The dataset includes 7 columns:

- Voltage_measured: voltage measured by a sensor
- Current_measured: current measured by a sensor
- Temperature_measured: temperature measured by a sensor
- Current_load: current load on the battery
- Voltage_load: voltage load on the battery
- Time: time at which the measurement was taken
- SOC: state of charge of the battery, which is the target variable for the prediction model.
The synthetic dataset used in this project is a simulated battery dataset that was generated using a battery model. It consists of 7 columns, including Voltage_measured, Current_measured, Temperature_measured, Current_load, Voltage_load, Time, and SOC.

The Voltage_measured column represents the voltage of the battery at a specific point in time, while the Current_measured column represents the current flowing through the battery at that same point in time. The Temperature_measured column represents the temperature of the battery, and the Current_load column represents the amount of current that is being drawn from the battery. The Voltage_load column represents the voltage of the load, and the Time column represents the time at which the data was collected. Finally, the SOC column represents the state of charge of the battery at each point in time.

Overall, this synthetic dataset was used to train a deep neural network model to predict the state of charge of a battery based on its electrical measurements. The trained model can be used in battery management systems to optimize the charging and discharging of the battery, which can improve the battery's performance and extend its lifetime.

## Deploying the BMS Model on a Raspberry Pi using Keras
Deploying a BMS model on a Raspberry Pi is a great way to monitor and control the state of charge (SOC) of a battery using a deep neural network (DNN) and a proportional-integral-derivative (PID) controller. In this guide, I will walk you through the steps to deploy a BMS model on a Raspberry Pi using Keras.

## Requirements
To deploy a BMS model on a Raspberry Pi using Keras, you will need the following:

- Raspberry Pi 4 or later
- Micro SD card (minimum 16 GB)
- Power supply (minimum 5V/3A)
- Keyboard and mouse
- HDMI monitor or TV
- Internet connection (Ethernet or Wi-Fi)
- USB drive or network connection for transferring files
- Breadboard and jumper wires
- Resistors and capacitors
- Battery and load to be monitored
- Camera module or USB webcam

## Step 1: Install the necessary libraries and dependencies
Before we can start deploying our BMS model on a Raspberry Pi, we need to install the necessary libraries and dependencies. We will use the following command to install these packages:

```
sudo apt install python3-pip
pip3 install tensorflow
pip3 install keras
pip3 install pandas
pip3 install sklearn
pip3 install numpy
```

## Step 2: Transfer your trained Keras model and Python script
Next, we need to transfer our trained Keras model and Python script to our Raspberry Pi. We can use a USB drive or a network connection to copy the files. Our Keras model should be saved as an .h5 file that contains both the model architecture and the weights. Our Python script should contain the code to load the model, access the camera module or USB webcam, generate sensor data, make predictions, and control the GPIO pins.

## Step 3: Connect your Raspberry Pi to the battery and the load using the GPIO pins
Now, we need to connect our Raspberry Pi to the battery and the load using the GPIO pins. We can use jumper wires and breadboards to make the connections. We also need to connect our camera module or USB webcam to our Raspberry Pi. Make sure that you follow the correct wiring diagram and use the appropriate resistors and capacitors to avoid damaging your device or causing a short circuit.

## Step 4: Run your Python script on your Raspberry Pi
Finally, we can run our Python script on our Raspberry Pi. We can use the `python3` command to execute our script. We should see some output on our terminal or monitor that shows the sensor data, the predicted SOC, and the PID output. We should also hear some music or see some lights if our script detects Santa Claus in the frame.

## Conclusion
In this guide, we have shown you how to deploy a BMS model on a Raspberry Pi using Keras. By following these steps, you can create a powerful and versatile battery management system that can monitor and control the state of charge of your battery using advanced machine learning algorithms and real-time data processing. We hope you find this guide helpful, and we wish you good luck with your projects!

BMS-DNN

BMS-DNN Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

BMS-DNN

BMS-DNN Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README