🔋 Battery_Temp_Pred_MATLAB

This MATLAB project predicts the battery surface temperature under varying duty cycles and conditions using modeling and image datasets.

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📁 Repository Structure

Battery_Temp_Pred_MATLAB/
│
├── c1_15du_new_Images/           # Image data for Class 1, 15 DU
├── c1_15du_new_Models/           # Models for Class 1, 15 DU
├── c1_15du_newoth_Images/        # Other Image data for Class 1, 15 DU
├── c1_15du_newoth_Models/        # Other Models for Class 1, 15 DU
├── c1_35du_new_Images/           # Image data for Class 1, 35 DU
├── c1_35du_new_Models/           # Models for Class 1, 35 DU
├── c2_15du_new_Images/           # Image data for Class 2, 15 DU
├── c2_15du_new_Models/           # Models for Class 2, 15 DU
├── c3_15du_new_Images/           # Image data for Class 3, 15 DU
├── c3_15du_new_Models/           # Models for Class 3, 15 DU
├── 1c 15du new.xlsx              # Excel data for Class 1, 15 DU
├── 2c 15du new.xlsx              # Excel data for Class 2, 15 DU
├── 3c 15du new.xlsx              # Excel data for Class 3, 15 DU
├── 3c 35du new.xlsx              # Excel data for Class 3, 35 DU
├── c1_15du.m                     # MATLAB script for Class 1, 15 DU
├── c2_15du.m                     # MATLAB script for Class 2, 15 DU
├── c3_15du_new.m                 # MATLAB script for Class 3, 15 DU
├── c3_35du_new.m                 # MATLAB script for Class 3, 35 DU
└── oth_1c_35du.m                 # MATLAB script for Other conditions, Class 1, 35 DU

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📌 Objective

To build predictive models for battery surface temperature using training datasets under different duty cycles and operating classes using MATLAB.

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🚀 Getting Started

Prerequisites

• MATLAB R2021b or later
• Curve Fitting and Image Processing Toolboxes (recommended)

Usage

1. Clone the repo:

   git clone https://github.com/Harshit-Chaudhry/Battery_Temp_Pred_MATLAB.git
   cd Battery_Temp_Pred_MATLAB

2. Open MATLAB.

3. Run any of the script files such as:

   run('c1_15du.m')

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📊 Sample Plot

Here’s a sample output graph showing temperature prediction:

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🧠 Model Info

• Data Source: Excel sheets & pre-labeled image datasets.
• Processing: Data fitting, model training, and prediction plotting.
• Output: Comparative plots of predicted vs actual temperatures.

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📌 Applications

• Battery Management Systems (BMS)
• Predictive Maintenance
• Electric Vehicle (EV) Thermal Monitoring

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👤 Author

Harshit Chaudhary

🔗 GitHub Profile

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📝 License

This project is licensed under the MIT License - see the LICENSE file for details.