Cloud_BMS_Project_Presentation[2] useful for project.pptx

It's useful for Engineering Project

1. Battery Management System using Cloud Presented By: Amarinder Singh 21EAREE002 Amit Kumar 21EAREE003 Devraj Kumhar 21EAREE012 Rakesh Meena 21EAREE037 Sudhansu Shekhar 22EAREE210 Submitted To: Dr. Prabhat Kumar HOD , Electrical Department Guided By: Er . Balram Kasniya Assistant Professor

2. Abstract : Cloud-Integrated Battery Management System (Cloud-BMS) Real-time monitoring of voltage, current, temperature, SoC IoT sensors transmit data to a secure cloud platform Remote monitoring, analytics, and predictive maintenance Features: AI fault detection, OTA updates, smart grid integration Improves battery safety, efficiency, and lifespan Applicable in EVs, renewable energy, industrial systems

3. Introduction of Project: Definition: BMS is an electronic system that manages and protects battery packs. Purpose: Ensures safe operation, maximizes performance, and extends battery lifespan. Key Functions: Monitor voltage, current, temperature, and state-of-charge ( SoC ) Balance cells to prevent overcharging or deep discharging Protect against faults like overvoltage, short circuit, and overheating Importance: Enhances battery efficiency and reliability Critical for electric vehicles, renewable energy storage, and portable electronics Modern Trends: Integration with IoT , AI, and cloud platforms for smarter management

4. Limitations of Traditional BMS: Limited remote monitoring and control No real-time data analytics or insights Poor scalability for large battery systems Limited predictive maintenance capabilities Manual firmware updates and diagnostics Less integration with smart grids and IoT

5. Objectives of Project: Develop a cloud-connected Battery Management System (BMS) Interface Smart BMS with ESP32 using suitable communication protocols Enable real-time monitoring and alert notifications Create a mobile-friendly dashboard for data visualization Implement data logging and historical analysis in the cloud Support OTA firmware updates for remote maintenance Incorporate basic fault detection and error logging

6. Motivation for Cloud Integration: Real-time access from anywhere Predictive maintenance Improved safety and efficiency Centralized control for multiple systems

7. System Architecture:

8. Overview of Hardware Components: Smart BMS (100A , 12V) Li-ion Battery Pack (LiFePO4 12.8V, 15Ah) Charger (15V, 2A) ESP32 Wiring , connectors, safety enclosures

9. Smart BMS: Model: DALY Smart BMS (100A, 12V) Key Features: Overcharge/Over-discharge protection Overcurrent and short-circuit protection Active/passive cell balancing UART communication for data logging Configuration: Programmable via USB/UART Customizable voltage/current thresholds

10. Li-ion Battery Pack: Chemistry: LiFePO ₄ Capacity: 15Ah Nominal Voltage: 12.8V Key Benefits: Thermal stability Long cycle life

11. Charger Specifications: Input: AC Output: 15V, 2A Charging Method: CC-CV (Constant Current, Constant Voltage) Safety Features: Overvoltage cutoff Current limit

12. ESP 32 Microcontroller: Features: Dual-core, Wi-Fi, Bluetooth, GPIO Protocols: UART, I²C, SPI Advantages: Cost-effective Cloud compatible

13. Data Flow Architecture: Battery Dash Board Cloud Server ESP 32 MQTT/ HTTP BMS

14. Software Stack: 1. ESP32 Firmware: Arduino IDE / PlatformIO 2. Cloud Integration: HTTP Client Blynk Iot & its Library 3. Dashboard UI: Web or Mobile Interface Real-time Graphs & Notifications

15. Dashboard Features: 1.Real-time display: Voltage Current Temperature 2.Battery Status Indicators: SOC (State of Charge) SOH (State of Health) 3.Data Visualization: Graphs Historical logs 4.Notifications : Alerts for abnormal conditions Status updates via cloud

16. Communication Protocols: Protocol : UART (Universal Asynchronous Receiver/Transmitter) Connection: Smart BMS ↔ ESP32 Baud Rate: Typically 9600 bps (configurable) Data Format: Hex or ASCII frames Processing: ESP32 parses data strings to extract voltage, current, temp, SOC, SOH

17. Wiring Diagram :

18. Real Time Monitoring Results: Battery Percentage : 92.88% Cell 1 Voltage : 6.77 V Cell 2 Voltage : 6.70 V Cell 3 Voltage : 11.14 V Temperature : 31°C Humidity : 48.5%

19. Safety & Protections Implemented: OV/UV – Stops charge/discharge on high/low voltage OC – Cuts power on overcurrent Temp – Shuts down if too hot Balancing – Equalizes cell voltages Humidity – Alerts on high moisture Cloud Alerts – Sends real-time warnings Remote Off – Cloud-based shutdown control

20. Testing & Result Validations:   Operation Initial SoC (%) Final SoC (%) Voltage (V) Current (A) Duration (hh:mm) Max Temp (°C) Charging 20% 100% 12.0 –14.6 1.95 06:10 34.2 Discharging 100% 15% 13.2 –11.4 3.0 (avg load) 04:50 37.5 Charging 18% 100% 11.9 –14.5 1.92 06:20 34.7 Discharging 100% 17% 13.3 –11.6 2.8 (avg load) 04:40 36.9

21. Challenges Faced: Data Delay – Lag in real-time cloud updates. Sensor Accuracy – Minor drift in voltage/temp readings. Connectivity Issues – Unstable internet affects performance. Power Management – High consumption by IoT modules. Cloud Integration – Difficulty in API configuration. Alert Reliability – Occasional false or missed alerts. Security Concerns – Ensuring safe data transmission.

22. Solutions & Workarounds: Calibrated Sensors – Improved accuracy of readings. Auto-Reconnect – Solved network drop issues. Low-Power IoT – Reduced energy consumption. Simplified API Use – Eased cloud integration. Secured Data – Used encryption for safe communication.

23. Applications of the Project: EV Battery Monitoring – Real-time tracking and control. Solar Energy Storage – Remote health check of battery banks. Industrial UPS Systems – Predictive maintenance via cloud. IoT Energy Devices – Smart power management and alerts. Battery Rentals/Swapping – Cloud tracking for usage and faults.

24. Future Enhancements: AI & Predictive Analytics: Integrating AI for predictive maintenance and battery health management, optimizing charging and discharging processes. IoT & Edge Computing: Enhancing IoT support with edge computing for real-time data processing and low-latency control. Grid Integration & Sustainability: Integrating BMS with energy grids for efficient storage and Vehicle-to-Grid (V2G) capabilities, while focusing on sustainability and battery recycling.

25. Conclusion of Project: Cloud-Based BMS : Enhances real-time monitoring, efficiency, and predictive maintenance using IoT and cloud technologies. AI & Analytics : Optimizes charging, discharging, and battery health with AI and cloud-based analytics. Scalable & Sustainable : Adapts to various applications and integrates with smart grids for energy efficiency and sustainability. Future Potential : Upcoming advancements in AI, battery tech, and grid integration will further improve performance and sustainability.

26. Thank You Ask Questions if Any?