🔋 MCP-Based EV Digital Twin Agent

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👨‍💻 Author

Mahmut Can Boran

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.

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🚀 Overview

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.

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🚀 Features

🔋 Battery Digital Twin

• 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

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🚗 Fleet Intelligence

• Fleet-wide Battery Comparison
• Fleet Health Ranking
• Fleet Anomaly Detection (Isolation Forest)
• Battery Outlier Identification
• Data Drift Monitoring

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🤖 Agentic AI

• 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

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📚 Battery Knowledge Base

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

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⚙️ Deployment

• Streamlit Dashboard
• Dockerized Deployment
• Hugging Face Spaces
• Git LFS Model Management

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🏗️ System Architecture

                     User Question
                           │
                           ▼
               Discovery MCP Agent
                           │
                           ▼
                Dynamic Tool Discovery
                           │
                           ▼
              Gemma Tool Selection
                           │
                           ▼
                    MCP Tool Server
                           │
      ┌──────────────┬──────────────┬──────────────┐
      ▼              ▼              ▼
 Battery Twin   Fleet Analytics   Driving Analytics
      │              │              │
      └──────────────┴──────────────┘
                     ▼
             Engineering Reasoning
                     ▼
           Battery Health Report

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📊 Current Capabilities

| Module | 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 | ✅ |

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🛠️ Tech Stack

AI / Machine Learning

• Scikit-learn
• Random Forest
• Isolation Forest
• Gemma LLM
• Ollama

Agent Framework

• Model Context Protocol (MCP)
• FastMCP
• Dynamic Tool Discovery
• Agentic AI

Backend

• Python
• Pandas
• NumPy
• Joblib

Frontend

• Streamlit
• Plotly

Deployment

• Docker
• Hugging Face Spaces
• Git LFS

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📸 Demo

Battery Digital Twin

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Fleet Intelligence

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Agentic AI Assistant

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🔮 Roadmap

• Multi-Agent EV Architecture
• Vector Database Integration
• Retrieval-Augmented Generation (RAG)
• Real-Time Vehicle Telemetry Integration
• Predictive Maintenance Scheduling
• Fleet Decision Support System

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⭐ Why this project?

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.

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📬 Contact

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.