Detect component anomalies across your EV fleet using trained machine learning models on vehicle telemetry. Shift from reactive repairs to data-driven maintenance scheduling that reduces unplanned downtime.
Overview
This Guidance demonstrates how automotive manufacturers and fleet operators can transform connected electric vehicle data into actionable insights by combining real-time diagnostics, predictive maintenance, and AI-powered analysis through digital twin technology. Telemetry data from vehicle sensors flows into AWS, where specialized AI agents analyze component health across tires, motors, and inverters to detect anomalies and predict failures before they occur. These agents retrieve maintenance guidance from official vehicle manuals using retrieval-augmented generation, then generate prioritized recommendations with immediate driver actions and detailed technical analysis. The system includes agentic AI capabilities that can automate procurement tasks, such as navigating to external websites to order replacement parts based on approved maintenance recommendations. You reduce vehicle downtime and maintenance costs while improving fleet safety through proactive issue detection and automated workflows that turn raw sensor data into intelligent, context-aware operational decisions.
Benefits
Predict failures before they happen
Simplify diagnostics with natural language
Enable fleet operators to diagnose vehicle issues through conversational AI agents grounded in authoritative maintenance manuals. Reduce diagnostic complexity and accelerate time to resolution.
Automate fleet maintenance at scale
Orchestrate specialist AI agents that monitor tires, motors, and inverters across your entire fleet. Scale predictive maintenance operations without adding manual inspection overhead.
How it works
EV Digital Twin Architecture
This architecture diagram helps you build an AI-powered predictive maintenance system for electric vehicle fleets using generative AI agents, retrieval-augmented generation, and browser automation on AWS.
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Step 1
Electric Vehicle (EV) fleet vehicles generate telemetry data (tire pressure, motor temperature, inverter thermals). AWS IoT Core ingests this data into Amazon Simple Storage Service (Amazon S3) for storage and processing. Amazon S3 also stores EV manuals as unstructured data.
Step 2
Combining vehicle and enterprise data using Amazon SageMaker Unified Studio to create vehicle data products that provide component-level information like tire (tread depth) and motor (vibration).
Step 3
Amazon SageMaker AI trains vehicle component predictive maintenance models and anomaly detection analytics using fleet telemetry data.
Step 4
Amazon S3 stores unstructured vehicle maintenance manuals. Amazon Bedrock Knowledge Bases indexes them using Amazon OpenSearch Serverless for vector search. The agent uses retrieval-augmented generation (RAG) with authoritative manual citations from the Knowledge Base before generating recommendations.
Step 5
AgentCore Runtime hosts the AI agents and uses Amazon Bedrock large language model (LLM) (Claude), to orchestrate 17 specialized tools for vehicle analysis, machine learning (ML) predictions, and maintenance guidance.
Step 6
AgentCore Memory stores short-term and long-term conversation history, enabling recurring issue detection and cross-vehicle fleet pattern analysis across sessions.
Step 7
AgentCore Gateway exposes vehicle history and report generation as Model Context Protocol (MCP) tool endpoints, enabling integration with enterprise applications and external fleet management systems.
Step 8
Amazon Nova Act drives browser automation for example automated tire ordering with live session viewing, streaming results back to the user interface (UI) for fleet operator oversight.
Step 9
Fleet operators access the Digital Twin UI through Amazon CloudFront, which delivers it globally from Amazon S3, with natural language chat for vehicle diagnostics.
Agent Orchestration Architecture of EV Digital Twin
This architecture shows how a Vehicle Maintenance Orchestrator on Amazon Bedrock AgentCore delegates diagnostics to specialist agents, supported by RAG, memory, and browser automation.
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Step 1
Fleet operators send requests from the EV Digital Twin portal through Amazon API Gateway and a proxy AWS Lambda to the orchestrator agent.
Step 2
The Vehicle Maintenance Orchestrator on AgentCore Runtime (Strands Agent) analyzes each request and routes it to the appropriate specialist agent — tire, motor, inverter, or fleet operations.
Step 3
AgentCore Memory maintains short-term and long-term conversation history across all agents, enabling recurring issue detection and cross-vehicle fleet pattern analysis.
Step 3a
The Tire Specialist Agent on AgentCore Runtime handles tire analysis, wear prediction, anomaly detection, and tire-specific KB guidance.
Step 3b
The Motor Specialist Agent handles motor performance analysis, failure prediction, and anomaly detection with motor-specific KB guidance.
Step 3c
The Inverter Specialist Agent handles inverter condition analysis, degradation prediction, and inverter-specific guidance.
Step 3d
The Fleet Operations Agent handles fleet-wide health monitoring, cross-vehicle pattern analysis, appointment scheduling
Step 4
Specialist agents retrieve authoritative maintenance guidance via RAG — Amazon Bedrock Knowledge Base queries the EV manual through Amazon OpenSearch Serverless vector search
Step 5
AgentCore Browser with Amazon Nova Act automates tasks on 3rd-party external or internal web portals with live DCV browser session streaming for operator oversight.
Step 6
AgentCore Observability captures agent action traces, tool invocations, and memory operations via Amazon CloudWatch for monitoring.
Step 7
AgentCore Gateway exposes vehicle history and report generation as MCP tool endpoints via a Vehicle History Lambda, or enabling integration with 3rd-party APIs and tools
Deploy with confidence
Everything you need to launch this Guidance in your account is right here.
Let's make it happen
Ready to deploy? Review the sample code on GitHub for detailed deployment instructions to deploy as-is or customize to fit your needs.