Group Project Presentation
Battery Health Monitoring with Fault Alert
Names & Roll Numbers
Details of the course being presented for
Details of the faculty in charge
Date of presentation
Highlighting safety in Electric Vehicles (EVs)
Importance of battery safety in energy storage systems
Role of Battery Management Systems (BMS) in safety
The need for real-time fault detection
Proactive measures to prevent battery faults.
Design a MATLAB-based monitoring system
Detect and alert four key conditions: Undervoltage, Overvoltage, Overcurrent, Overtemperature
Monitoring for insufficient voltage levels.
Monitoring for excessive voltage levels.
Provide simple visualization via GUI
Battery model created in MATLAB/Simulink
GUI developed for user interaction
Threshold values set for voltage, current, temperature
Fault Flag triggered when safe limits exceeded
MATLAB and Simulink used as the simulation environment
Represents the battery pack being monitored
Sensors (Voltage, Current, Temperature) used for data collection
MATLAB GUI used for data visualization and control
Fault Detection Logic implemented in MATLAB
Fault Alert indicating abnormal conditions
MATLAB GUI simulates firmware logic
SOC estimation using Coulomb Counting
Threshold comparison for faults
Visual alert system (Red/Green indicator)
Algorithm used for SOC estimation
Plot of Voltage vs Time
Plot of SOC vs Time
Plot of Current vs Time
Plot of Temperature vs Time
Fault Flag Signal indicating detected faults
Tabulated results for different capacities
Analysis of simulation results
Performance between different capacities
Testing and validation of threshold values
Discussion about the validity and accuracy of the battery model used.
Smaller batteries → faster SOC drop
Overcurrent cases triggered early faults
Temperature rise visible under heavy load
Limitations of simplified modeling
Discussion about the practical application of the system
GUI-based BMS prototype, fault detection system
Objectives met successfully
Technical significance: foundation for EV safety systems
Integration with Machine Learning for advanced predictive fault detection
Ideas to refine the system and improve its performance
Reference to textbooks used
Reference to research papers
Reference to tools used
Acknowledgment of faculty support
Acknowledgment of other resources