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EV Charging & Fleet
Management
Platform

The commercial EV fleet market is projected to exceed $100 billion by 2030, yet most operators lack digital infrastructure for efficient charging operations. A California-based electric trucking company operating 500+ heavy-duty trucks across 10 depots approached us with a critical challenge: their fleet was expanding, but charging management remained manual and fragmented.

500+

Electric vehicles managed across
multiple depots

29%

Reduction in charging costs through
smart optimization

Platform Type

Enterprise EV Fleet Management Platform

Architecture

Event-Driven / IoT-Based Microservices

Stack

Flutter · React.js · Node.js · PostgreSQL · AWS · OCPP

Launch Status

Production-Ready

About the Client

A Growing Electric Fleet Company Scaling Charging Operations Across Multiple Depots

The client is a California-based electric trucking company operating more than 500 heavy-duty electric vehicles across 10 depots. With rapid fleet expansion, the company faced increasing challenges in managing charging infrastructure, energy consumption, and operational visibility.

As EV adoption accelerated, manual charging management became inefficient, resulting in underutilized chargers, higher electricity costs, and limited fleet monitoring capabilities.

The company envisioned a unified platform capable of optimizing charging schedules, monitoring fleet health in real time, and enabling predictive maintenance while supporting future fleet growth.

PLATFORM CAPABILITIES

Smart Charging Optimization

Real-Time Fleet Monitoring

Predictive Maintenance

Dynamic Energy Analytics

CORE MODULES

  • Charging Schedule Management
  • Fleet & Vehicle Monitoring
  • Battery Health Management
  • Automated Reporting & Analytics

The Challenge

Scaling EV Charging Infrastructure Required Intelligent
Automation from Day One

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    Smart Charger Utilization

    With 500+ vehicles and 200+ charging stations across 10 depots, charger availability was largely unknown. Vehicles often waited for charging while other stations remained underutilized, reducing overall fleet efficiency and increasing turnaround times.

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    Rising Energy Costs

    Electricity tariffs fluctuated throughout the day, with peak-hour rates reaching up to 3x higher than off-peak pricing. Without intelligent scheduling, charging operations generated unnecessary expenses exceeding $100,000 per month.

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    Limited Fleet Visibility

    Operations teams lacked a centralized view of charging sessions, vehicle battery health, and charger performance. Manual monitoring consumed 15+ hours weekly, delaying operational decisions and reducing responsiveness.

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    Unplanned Downtime & Maintenance Risks

    Battery degradation and charger failures resulted in nearly 12% monthly downtime, directly impacting fleet availability. Detecting issues early required continuous monitoring and predictive maintenance capabilities that did not exist within the existing infrastructure.

The Solution

Intelligent EV Infrastructure Decisions — Building a Scalable Charging Ecosystem

SMART CHARGING OPTIMIZATION

AI-Powered Charging Scheduling

  • Intelligent charger assignment based on battery levels, departure schedules, and charger availability.
  • Real-time electricity tariff integration to automatically shift charging to off-peak periods.
  • Depot load balancing to prevent energy spikes and maximize charger utilization.
  • Delivered 29% reduction in charging costs and significantly improved operational efficiency.

REAL-TIME FLEET INTELLIGENCE

Unified Monitoring Platform

  • Live monitoring of 500+ vehicles and 5,000+ daily charging sessions.
  • Real-time visibility into charger status, battery health, and fleet availability.
  • Dynamic energy consumption analytics across vehicles, depots, and charging sessions.
  • Route-aware charging recommendations improving driver efficiency by 15%.

PREDICTIVE MAINTENANCE & INFRASTRUCTURE

Production-Ready EV Ecosystem

  • AI-based anomaly detection using battery temperature, voltage, current, and charging duration data.
  • OCPP 1.6 & 2.0 gateway supporting 200+ charging stations.
  • AWS IoT infrastructure processing 5,000 messages per second.
  • Auto Scaling infrastructure with Multi-AZ failover and disaster recovery.

ARCHITECTURE

Platform Architecture

The platform was designed as a decoupled integration layer sitting between driver-facing mobile apps, the operations dashboard, and the physical charging network. Rather than bolting logic onto any single surface, the backend orchestrates schedule optimization, telemetry ingestion, predictive maintenance, and reporting centrally.

The architecture is built for scalability, resilience, and operational visibility — supporting 500+ vehicles and 200+ chargers today, with headroom for 10x growth without re-architecture.

Pirate Pets Architecture
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Database Architecture

PostgreSQL + Redis — built for scale and consistency

PostgreSQL handles relational and time-series data — users, fleets, 500+ vehicles, 5,000+ daily charging sessions, energy consumption, and maintenance alerts — with GiST indexes for geospatial queries and time-series partitioning for historical efficiency. Redis absorbs read-heavy traffic in front of it, caching hot data and coordinating distributed locks. Connection pooling supports 150 concurrent connections across the cluster.

  • Time-series partitioning for historical data
  • GiST geospatial indexing
  • RDS Multi-AZ with read replicas
  • 150 concurrent connection pooling
  • Redis write-through cache + distributed locks

Performance Benchmarks

Vehicle API response (p95) <45ms
Charging availability query <35ms
Charger assignment (p95) <80ms
OCPP communication latency <50ms
Concurrent DB connections 150+
IoT telemetry throughput 5,000/sec

KEY OUTCOMES & BUSINESS VALUE

From Manual Charging to Measurable Savings — Built to Scale

  • $768K+

    Total Annual Savings

    Combined charging optimization and reduced downtime, against $102K/year infrastructure cost.

  • 29%

    Charging Cost Reduction

    Smart scheduling shifts flexible charging to off-peak hours using real-time utility tariffs.

  • Mo. 8

    ROI Achieved

    Full return on infrastructure investment reached within eight months of deployment.

  • 93%

    Vehicle Availability

    Up from 74%, driven by predictive maintenance that catches faults before failure.

  • Cost Optimization Model

    Charging assignments are driven by real-time utility tariffs, battery levels, and departure schedules — shifting flexible load to off-peak hours. The result is a 29% cost reduction, worth $348,000+ a year, without any change to fleet output.

  • Scale & Reliability

    Built for 500+ vehicles and 200+ chargers across 10 depots, with 99.9% API uptime over 12 months and auto-scaling infrastructure that supports 10x fleet growth without re-architecture.

Conclusion

EV Fleet Platform: Lessons Learned

  • Smart Charging Optimization Is Essential

    Smart Charging Optimization Is Essential

    Manual scheduling causes peak-hour expense — automated, tariff-aware optimization is what turns 200+ scattered chargers into one coordinated system.

  • Predictive Maintenance Prevents Downtime

    Predictive Maintenance Prevents Downtime

    Reactive maintenance causes unplanned outages — AI-based anomaly detection on telemetry catches faults before they become failures.

  • OCPP Compliance Is Non-Negotiable

    OCPP Compliance Is Non-Negotiable

    Proprietary protocols create vendor lock-in — OCPP 1.6/2.0 keeps 200+ chargers compatible today and the platform future-proof for tomorrow.

Ready to electrify your fleet operations?

Let's talk architecture, timelines, and how we can get your EV charging platform to production-ready in record time.

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