Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MCP EV Digital Twin Agent predict state of health for battery 001"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
AI Engineer | Automotive Software Enthusiast | Computer Engineer
Passionate about Agentic AI, Model Context Protocol (MCP), Large Language Models, Digital Twins, and Intelligent Automotive Software Systems.
Related MCP server: GridWatch MCP
An AI-powered EV Digital Twin platform that combines battery health prediction, fleet analytics, intelligent tool orchestration through the Model Context Protocol (MCP), and LLM-powered reasoning to monitor, analyze, and explain electric vehicle battery behavior.
Unlike a traditional dashboard, this project dynamically discovers available MCP tools, selects the most appropriate tool using an LLM, executes engineering analyses, injects fleet-aware context, and generates structured battery health reports through multi-step reasoning.
Battery State of Health (SOH) Prediction
Remaining Useful Life (RUL) Estimation
Estimated Driving Range Prediction
Battery Health Classification
Digital Twin Timeline Visualization
What-if Scenario Simulation
Fleet-wide Battery Comparison
Fleet Health Ranking
Fleet Anomaly Detection (Isolation Forest)
Battery Outlier Identification
Data Drift Monitoring
Model Context Protocol (MCP) Integration
Dynamic MCP Tool Discovery
Automatic Tool Selection with Gemma
Multi-Step Reasoning
Fleet-aware Context Injection
Engineering Report Generation
Intelligent Tool Orchestration
The agent combines numerical battery predictions with engineering knowledge to explain:
Battery degradation
State of Health (SOH) interpretation
Charging recommendations
Battery maintenance suggestions
Risk assessment
Engineering-oriented battery reports
Streamlit Dashboard
Dockerized Deployment
Hugging Face Spaces
Git LFS Model Management
User Question
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Discovery MCP Agent
│
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Dynamic Tool Discovery
│
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Gemma Tool Selection
│
▼
MCP Tool Server
│
┌──────────────┬──────────────┬──────────────┐
▼ ▼ ▼
Battery Twin Fleet Analytics Driving Analytics
│ │ │
└──────────────┴──────────────┘
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Engineering Reasoning
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Battery Health ReportModule | Status |
Battery SOH Prediction | ✅ |
Remaining Useful Life (RUL) | ✅ |
Driving Range Estimation | ✅ |
Battery Digital Twin | ✅ |
Digital Twin Timeline | ✅ |
What-if Scenario Simulation | ✅ |
Fleet Analytics | ✅ |
Fleet Anomaly Detection | ✅ |
Data Drift Detection | ✅ |
MCP Tool Calling | ✅ |
Dynamic Tool Discovery | ✅ |
Multi-Step Reasoning | ✅ |
Battery Knowledge Base | ✅ |
Docker Deployment | ✅ |
Hugging Face Deployment | ✅ |
Scikit-learn
Random Forest
Isolation Forest
Gemma LLM
Ollama
Model Context Protocol (MCP)
FastMCP
Dynamic Tool Discovery
Agentic AI
Python
Pandas
NumPy
Joblib
Streamlit
Plotly
Docker
Hugging Face Spaces
Git LFS
Multi-Agent EV Architecture
Vector Database Integration
Retrieval-Augmented Generation (RAG)
Real-Time Vehicle Telemetry Integration
Predictive Maintenance Scheduling
Fleet Decision Support System
This project demonstrates how Model Context Protocol (MCP), Agentic AI, LLMs, and predictive battery analytics can be combined to build an intelligent EV Digital Twin capable of autonomous tool discovery, engineering reasoning, and fleet-level battery monitoring.
The project was designed to explore modern AI agent architectures while addressing real-world battery monitoring challenges in electric vehicles.
Mahmut Can Boran
💼 LinkedIn: https://www.linkedin.com/in/mahmutcanboran/
💻 GitHub: https://github.com/mahmutcanborann
If you're interested in Agentic AI, MCP, Digital Twins, Battery Analytics, or Automotive Software Engineering, feel free to connect or reach out.
This server cannot be deployed
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