AI-Managed EV Battery Swapping System
Budget: ₹37,500 – ₹75,000 INR
Project Description Bullet Points
Project Overview: Develop an AI-driven management and reservation system for EV battery swapping stations. The system will enable users to monitor battery health (State of Charge/State of Health), locate available stations, and reserve battery slots in real-time.
Core Functionality:
User Interface: Create a dashboard/mobile app for users to track battery status, view station availability via GPS, and manage reservations.
IoT & Hardware Integration: Interface microcontrollers (e.g., ESP32/Arduino/Raspberry Pi) with sensors (voltage, current, temperature) to transmit real-time data to a cloud server.
AI/Predictive Analytics: Implement AI models to optimize charging strategies, predict battery health, and forecast station demand.
Backend & Cloud: Establish cloud connectivity for remote monitoring, billing, and inventory management of charged battery packs.
Technical Requirements:
Tech Stack: Experience with IoT protocols (MQTT/HTTP), cloud platforms (AWS/Firebase), and mobile/web development frameworks.
Hardware Knowledge: Proficiency in interfacing sensors and microcontrollers with battery management systems (BMS).
Security & Safety: Implement robust safety protocols for battery monitoring and secure communication between the vehicle, station, and cloud.
Deliverables:
Source code for the mobile/web dashboard and firmware for the IoT modules.
Documentation of the system architecture, hardware schematics, and AI model integration.
Testing/validation reports for system reliability and battery safety metrics.
Project Overview: Develop an AI-driven management and reservation system for EV battery swapping stations. The system will enable users to monitor battery health (State of Charge/State of Health), locate available stations, and reserve battery slots in real-time.
Core Functionality:
User Interface: Create a dashboard/mobile app for users to track battery status, view station availability via GPS, and manage reservations.
IoT & Hardware Integration: Interface microcontrollers (e.g., ESP32/Arduino/Raspberry Pi) with sensors (voltage, current, temperature) to transmit real-time data to a cloud server.
AI/Predictive Analytics: Implement AI models to optimize charging strategies, predict battery health, and forecast station demand.
Backend & Cloud: Establish cloud connectivity for remote monitoring, billing, and inventory management of charged battery packs.
Technical Requirements:
Tech Stack: Experience with IoT protocols (MQTT/HTTP), cloud platforms (AWS/Firebase), and mobile/web development frameworks.
Hardware Knowledge: Proficiency in interfacing sensors and microcontrollers with battery management systems (BMS).
Security & Safety: Implement robust safety protocols for battery monitoring and secure communication between the vehicle, station, and cloud.
Deliverables:
Source code for the mobile/web dashboard and firmware for the IoT modules.
Documentation of the system architecture, hardware schematics, and AI model integration.
Testing/validation reports for system reliability and battery safety metrics.