AI-Optimized Electric Vehicle Fleet Charging System
## Intelligent Charging Orchestration Deploys a **7+ agent AI system** for dynamic EV fleet optimization. Forecasts energy demand, analyzes battery health, optimizes charging schedules against grid pricing, coordinates with utility demand response programs, and plans infrastructure capacity—ensuring vehicles are always ready for service at minimum cost.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Crisis Detection & Input Configuration - Real-time capacity crisis monitoring
Agent Orchestration Hub
AI Reasoning & Knowledge Retrieval - Chain-of-thought reasoning interface
Results & Explainability Dashboard
AI Agents
Specialized autonomous agents working in coordination
EV Orchestrator Agent
## Multi-Agent Coordination EV fleet optimization requires coordinating demand forecasting, battery health, charging schedules, route planning, and grid coordination. Without central orchestration, agents may produce conflicting recommendations.
Core Logic
## Hierarchical Supervision Architecture Powered by **Claude-3-Opus** (200K context) as supervisor: - Manages 4 specialist agents (Demand Forecaster, Fleet Optimizer, Route Planner, Safety Validator) - Uses 4 tools: `delegate_task`, `request_consensus`, `synthesize_decision`, `escalate_to_human` - Implements hierarchical and consensus-based collaboration patterns - Builds consensus through voting on conflicting recommendations - Tracks 847+ completed tasks with 96% success rate - Manages 2.45M+ tokens across optimization scenarios - Handles escalation for critical decisions affecting >500 passengers
Demand Forecaster Agent
## Energy Demand Uncertainty Fleet energy requirements vary with routes, weather, passenger loads, and traffic conditions. Without accurate forecasting, operators cannot plan charging schedules effectively.
Core Logic
## Predictive Analytics Pipeline Powered by **Claude-3-Sonnet** (200K context) with forecasting models: - Executes Prophet + LSTM time series forecasting - Uses 4 tools: `query_feature_store`, `run_forecast_model`, `detect_anomalies`, `calculate_uncertainty` - Analyzes 6 data sources: GPS telematics (1,247 points), APC (892 points), Weather API (24 points), Traffic API (3,456 points), Events Calendar (12 events), Ticketing (4,521 points) - Calculates weather-adjusted range predictions with confidence intervals - Tracks 2,340+ completed forecasts with 94% accuracy - Uses 1.82M+ tokens for demand modeling
Fleet Optimizer Agent
## Vehicle-Route Matching Complexity Assigning EVs to routes requires balancing range constraints, charging needs, service schedules, and vehicle availability—a complex constraint satisfaction problem.
Core Logic
## Constraint-Based Fleet Allocation Powered by **Claude-3-Sonnet** (200K context) with OR-Tools: - Performs linear programming and multi-objective optimization - Uses 4 tools: `get_fleet_state`, `run_optimizer`, `evaluate_allocation`, `check_constraints` - Matches vehicles to routes based on energy requirements and battery levels - Considers vehicle status: ready, charging, in-service, maintenance, low-charge - Tracks 1,890+ allocation tasks with 92% success rate - Uses 1.54M+ tokens for optimization scenarios - Outputs optimal fleet allocation with charging priorities
Route Planner Agent
## Energy-Efficient Routing EV routes must consider elevation changes, traffic conditions, and temperature impacts on range. Standard routing ignores energy consumption patterns unique to electric vehicles.
Core Logic
## Energy-Aware Path Optimization Powered by **Claude-3-Sonnet** (200K context) with routing algorithms: - Implements Dijkstra/A* algorithms with energy weighting - Uses 4 tools: `get_traffic_data`, `calculate_route`, `estimate_eta`, `identify_bottlenecks` - Integrates real-time traffic data from HERE API (3,456 data points) - Models HVAC impact and temperature effects on consumption - Calculates conservative ranges with 90% confidence margins - Tracks 3,210+ route plans with 95% success rate - Uses 980K+ tokens for path optimization
Safety Validator Agent
## Operational Risk Management Automated charging and fleet decisions carry risks—stranded vehicles, grid overloads, battery damage. Decisions must be validated before execution to prevent service failures.
Core Logic
## Risk Assessment and Validation Powered by **Claude-3-Sonnet** (200K context) as validator: - Uses 4 tools: `assess_risk`, `check_compliance`, `validate_constraints`, `create_rollback_plan` - Validates all optimization decisions against 6 safety guardrails - Blocks actions affecting >500 passengers without human approval - Enforces 85% minimum confidence threshold for automated execution - Creates rollback plans for reversible decisions - Triggers auto-rollback if KPIs degrade >10% - Maintains 99% validation success rate across 1,560+ validations - Uses 680K+ tokens for risk assessment
Battery Guardian Agent
## Battery Degradation Risk Improper charging patterns—deep discharges, fast charging abuse, temperature extremes—accelerate battery degradation, reducing vehicle range and increasing replacement costs.
Core Logic
## Battery Health Optimization Specialized agent for battery lifecycle management: - Monitors real-time State of Health (SOH) scores (0-100) - Tracks charge cycle patterns and degradation trends - Optimizes charging profiles to minimize degradation - Recommends charge level targets (avoid 0% and 100%) - Predicts remaining battery lifespan based on usage patterns - Alerts on abnormal degradation rates - Coordinates with charging optimizer for health-preserving schedules
Grid Coordinator Agent
## Grid Integration Challenges EV fleet charging creates significant grid load. Without coordination, operators miss cost savings from dynamic pricing and cannot participate in demand response revenue opportunities.
Core Logic
## Dynamic Grid Integration Specialized agent for utility coordination: - Integrates real-time dynamic pricing signals from grid operators - Participates in demand response programs for revenue generation - Manages Vehicle-to-Grid (V2G) capabilities when beneficial - Balances fleet charging load to avoid peak demand charges - Forecasts grid conditions for proactive scheduling - Coordinates with charging optimizer for cost-minimizing schedules - Enables fleet to act as distributed energy resource
Capacity Planner Agent
## Infrastructure Planning Gap As EV fleets grow, charging infrastructure must scale. Operators lack visibility into future capacity needs, leading to underinvestment or costly over-provisioning.
Core Logic
## Infrastructure Capacity Modeling Specialized agent for long-term planning: - Analyzes current charger utilization percentages - Projects future fleet size and vehicle types - Calculates capacity gaps between current and needed infrastructure - Models different growth scenarios and their infrastructure requirements - Generates investment recommendations - Considers depot locations and grid connection constraints - Produces capital planning reports for executive decisions
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
7 technologies
Architecture Diagram
System flow visualization