optimizing charging pattern project.pptx

Optimization of battery

1. Optimizing charging patterns to prevent battery degradation using EVSE Team members Sriharish G (2021502038) Raghunathan R (2021502033) Jeeva (2021502324) Nataraj T (2021502303)

2. Problem Statement Battery degradation is a significant concern for electric three-wheelers. Frequent fast charging, deep discharges, and improper charging practices can lead to reduced battery performance, shorter lifespan, and higher maintenance costs.

3. Solution Optimizing charging process using adaptive charging algorithms - State of Charge ( SoC) Management - State of Health ( SoH ) Monitoring -Dynamic Charging rates -Charge Cycles Management Temperature Management -Temperature Monitoring -Temperature control mechanisms -charging adjustments -preconditioning

4. Hardware Requirements -EVSE (with connectivity modules) -Electric three wheeler -Telematics devices -Battery monitors -temperature sensors -Microcontrollers

5. Software Requirements -Firmware (embedded software) -Machine learning tools -TensorFlow -Scikit-learn -Mobile app or web-based dashboard -Cloud-based platform -Database - Analytics tools -Integration and connectivity -IoT connectivity -Smart grid integration -Testing and Diagnostic software -Development and programming tools

6. Step by step process Hardware Development Component Procurement Purchase or source all necessary hardware components based on the requirements analysis. Hardware Assembly Assemble the EVSE unit, including the power supply unit, control and communication components, charging connectors, and safety systems. Integrate temperature sensors and cooling/heating systems into the EVSE unit and electric three-wheelers. Testing and Calibration Test hardware components individually to ensure they function correctly. Calibrate sensors and cooling/heating systems to ensure accurate measurements and optimal performance.

7. Software Development Firmware Development Develop firmware for the EVSE unit to manage charging processes and communication with the electric three-wheelers. Implement charging control algorithms based on SoC, SoH , and temperature data. Mobile and Web Application Development Design and develop a mobile app and web-based dashboard for user interaction, monitoring, and control. Implement real-time notifications, data visualization, and control features. Cloud Platform and Data Management Set up cloud-based storage for data collection and analysis. Develop machine learning models for predictive maintenance and charging optimization. Integration and Testing Integrate firmware with hardware components and software applications. Perform system testing to ensure all components work together seamlessly and meet project goals.

8. Implementation and Deployment 5.1 Pilot Testing Deploy the system on a small scale with a subset of electric three-wheelers. Monitor performance, gather feedback, and make necessary adjustments. 5.2 Full-Scale Deployment Roll out the optimized charging system to the entire fleet. Provide training and support for users to ensure effective utilization. 5.3 Real-Time Monitoring and Maintenance Continuously monitor the system's performance and make adjustments as needed. Perform regular maintenance and updates to ensure long-term efficiency.

9. Performance Evaluation Analyze the impact of the optimized charging system on battery health, charging efficiency, and vehicle performance. Compare results against initial goals and benchmarks

10. Expected result Extended battery life -Reduced degradation -improved health metrics Optimized charging efficiency -enhanced charging profiles -reduced charging time Improved performance and reliability -increased vehicle uptime -consistent vehicle performance Real time moitoring and control -remote management capabilities Data driven insights - ccomprehensive data analysis -predictive maintenance