Smart Battery Management System

Deep Learning for Battery Life Optimization in Portable Devices

Overview

This project presents an innovative Smart Battery Management System (BMS) for portable devices using deep learning techniques to enhance battery life and performance. The research addresses critical challenges in battery health monitoring and lifecycle prediction for modern electronic devices and autonomous vehicles.

Problem Statement

Traditional Battery Management Systems face limitations in accurately predicting battery state and remaining useful life, leading to:

  • Suboptimal battery performance
  • Unexpected battery failures
  • Reduced device lifespan
  • Safety concerns in critical applications

Methodology

Machine Learning Approach

Developed comprehensive ML models using:

  • Frameworks: Scikit-learn and TensorFlow
  • Programming: Python for model development and data analysis
  • Algorithms: Neural networks for pattern recognition and prediction

Key Prediction Targets

  1. State of Charge (SoC): Real-time battery capacity estimation
  2. State of Health (SoH): Long-term battery degradation assessment
  3. Remaining Useful Life (RUL): Predictive maintenance and replacement planning

Technical Implementation

Data Processing

  • Feature engineering from battery sensor data
  • Time-series analysis for charge-discharge cycles
  • Normalization and preprocessing of voltage, current, and temperature data

Model Architecture

  • Deep neural networks optimized for battery state estimation
  • Hyperparameter tuning for maximum accuracy
  • Cross-validation to ensure generalization

Evaluation Metrics

  • Mean Absolute Error (MAE): Measuring prediction accuracy
  • Root Mean Square Error (RMSE): Quantifying prediction variance
  • R² Score: Assessing model fit quality

Results & Achievements

  • Significant improvements over traditional BMS methods across all metrics
  • High accuracy in SoC and SoH prediction
  • Robust RUL estimation for maintenance scheduling
  • Published in International Journal of Innovative Research in Technology (IJIRT), Vol. 10, Issue 11, April 2024
Performance comparison showing improvements in battery life prediction accuracy.

Applications

  • Portable Electronics: Smartphones, laptops, tablets
  • Electric Vehicles: Battery health monitoring for EVs
  • Autonomous Vehicles: Critical for safe operation
  • IoT Devices: Extended battery life for remote sensors

Technologies Used

  • Python, Scikit-learn, TensorFlow
  • NumPy, Pandas for data manipulation
  • Matplotlib, Seaborn for visualization
  • Deep Learning architectures

Supervisor

Prof. Sunil MP
Jain University, Bangalore, India

Publication

Kone, S., & Sunil, M.P. (2024). “Smart Battery Management System for Portable Devices to Enhance Battery Life Based on Machine Learning.” International Journal of Innovative Research in Technology, Vol. 10, Issue 11. ISSN: 2349-6002.

Future Work

  • Real-time implementation on embedded systems
  • Integration with IoT platforms
  • Expansion to different battery chemistries
  • Cloud-based monitoring dashboard

References