Condition monitoring and predictive maintenance of EV batteries
- 1. CONDITION MONITORING AND PREDICTIVE MAINTENANCE OF EV BATTERY BY UNDER THE SUPERVISION OF Dr. K HIM AJA, L/EEE DEPAERMENT OF ELECTRICAL AND ELECTRONICS GOVERNMENT POLYTECHNIC,BELLAMPALLY.
- 2. PLAN OF PREESENTATION 1. introduction 2. problem statement 3. Objectives 4. Innovation & Creativity 5. Technical Complexity 6. Practical Application 7. Proto model 8. Results and discussions 9. Features of system 10. Conclusion
- 3. 1. INTRODUCTION Condition monitoring and predictive maintenance of EV batteries involve continuously assessing battery health using sensors, data analytics, and AI to detect anomalies and predict failures. This approach helps optimize battery performance, extend lifespan, and prevent unexpected breakdowns. By leveraging real-time monitoring and predictive algorithms, it enhances safety, efficiency, and cost-effectiveness in electric vehicle operations.
- 4. 2. PROBLEM STATEMENT EV BATTERIES DEGRADE UNPREDICTABLY, LEADING TO FAILURES, SAFETY RISKS, AND HIGH COSTS. LACK OF REAL-TIME MONITORING AND PREDICTIVE MAINTENANCE RESULTS IN PREMATURE FAILURES AND INEFFICIENCIES.
- 5. 3. OBJECTIVES Predictive Maintenance Using Cycle Count – Analyze battery charge-discharge cycles to estimate maintenance needs.I IoT-Based Hardware Model – Develop a smart monitoring system using IoT sensors for real-time data collection. Machine Learning Integration – Utilize AI algorithms to predict battery degradation and optimize performance. Real-Time Data Analysis – Continuously monitor battery health to detect anomalies and prevent failures. Optimized Maintenance Scheduling – Use predictive insights to plan efficient and cost-effective battery maintenance.
- 6. 4. INNOVATION & CREATIVITY USES REAL-TIME SENSOR DATA AND AI-DRIVEN ANALYTICS FOR PREDICTIVE MAINTENANCE. DIGITAL TWIN MODELS, IOT-ENABLED BMS, AND MACHINE LEARNING ALGORITHMS. EARLY FAULT DETECTION AND OPTIMIZED MAINTENANCE SCHEDULES.
- 7. 5. TECHNICAL COMPLEXITY INTEGRATION OF AI, IOT, AND CLOUD-BASED ANALYTICS FOR REAL-TIME MONITORING. ADVANCED ML MODELS TRAINED ON HISTORICAL BATTERY DATA FOR FAULT PREDICTION. USE OF DIGITAL TWIN TECHNOLOGY FOR ACCURATE SIMULATION AND ANALYSIS.
- 8. 6. PRACTICAL APPLICATION IMPACT: ENHANCES BATTERY LIFE, REDUCES WASTE, IMPROVES SAFETY. FEASIBILITY: SCALABLE FOR INDIVIDUAL EV OWNERS AND FLEET OPERATORS. SUSTAINABILITY: REDUCES RAW MATERIAL WASTE AND OPTIMIZES BATTERY RECYCLING.
- 9. 7. PROTO MODEL IoT Sensors: Voltage, current, temperature, and internal resistance sensors. Microcontroller (): To process sensor data. Voltage sensor : Battery Management System (BMS): To regulate battery charging and discharging.
- 10. PROTO MODEL
- 11. Ethical Considerations SAFETY: ENSURING RELIABLE AND SECURE BATTERY MONITORING SYSTEMS. ENVIRONMENTAL: REDUCING E-WASTE AND OPTIMIZING RESOURCE UTILIZATION. LEGAL: COMPLIANCE WITH DATA PRIVACY, SAFETY STANDARDS, AND BATTERY RECYCLING LAWS.
- 12. PROGRAMMES USED For components data interfacing, used c++ programm For programe coding , used python For battery status used unsupervised learning (regression learning)
- 13. Implementation Steps 1. Setup Sensors & Hardware: Integrate loT sensors with the battery. 2. Develop IoT Connectivity: Transmit data to the cloud. 3. Build Data Pipeline: Store and process data on an loT platform. 4. Train ML Models: Use collected data to predict battery health. 5. Deploy on Edge/Cloud: Implement ML models for real-time decision- making.
- 16. 8. RESULTS
- 17. 9.Features of system • Real-time Monitoring: Battery voltage, current, temperature, and charge cycles. • Predictive Maintenance: Alerts on battery degradation before failure. • Fault Detection: Detects overheating, overcharging, or deep discharge conditions. • Cloud-Based Data Storage: Enables remote access and historical trend analysis. • Dashboard & Alerts: Web/mobile app interface for monitoring and notifications.
- 18. 10.Conclusion • Predictive Maintenance & cost efficiency • Real time monitoring & Alerts • Automated Alerts &Actions • Remote Accessibility • Real time monitoring