ANPR Integration in EV Charging: A Technical Implementation Guide

Streamlining the User Experience

One of the primary goals in modern EV charging infrastructure is minimizing user friction. While Plug & Charge (ISO 15118) offers a standardized certificate-based approach, ANPR (Automatic Number Plate Recognition) provides a viable alternative for seamless access control using computer vision technology.

Effective ANPR integration requires a robust combination of optical hardware, edge processing, and backend logic to function reliably across various environmental conditions. This guide explores the architectural considerations for deploying ANPR in a production environment.

Technical Challenges in Optical Recognition

Recognizing a license plate in a controlled environment is straightforward. However, outdoor deployments introduce variables such as lighting changes, weather, and vehicle angles. Addressing these factors is critical for system reliability.

1. Optical Configuration

Standard security cameras often struggle with the specific requirements of ANPR due to two main factors: Motion Blur and Lighting Contrast.

• Shutter Speed: To maintain character sharpness on moving vehicles, cameras typically require shutter speeds of 1/1000s or faster.
• WDR (Wide Dynamic Range): Essential for balancing exposure when a vehicle's headlights are active against a darker background.
• IR Illumination: For night-time operation, 850nm IR floodlights are standard. License plates are retro-reflective, making them highly visible under IR light even when the surrounding area is dark.

Technical Note: The use of varifocal lenses allows installers to adjust the field of view to the specific lane width, maximizing the "pixels per degree" on the target plate area for optimal OCR accuracy.

2. Processing Architecture: Edge vs. Cloud

There are two primary architectures for processing video feeds for OCR (Optical Character Recognition):

• Cloud Processing: Involves sending images to a centralized server. While this allows for powerful models, it introduces network latency which can delay barrier operations.
• Edge Processing: Performs OCR directly on the camera or a local gateway. This minimizes latency and bandwidth usage.

Hybrid Approach: A common industry practice is a hybrid model. The edge device performs the initial detection and OCR. If confidence is high (>90%), the action triggers immediately. If confidence is marginal, the frame can be sent to the cloud for secondary verification or human review.

System Integration Workflow

Integrating ANPR into an OCPP (Open Charge Point Protocol) ecosystem involves the system acting as an authentication proxy.

1. Detection: The camera detects a vehicle entering the configured Region of Interest (ROI).
2. Capture & OCR: The system captures multiple frames and applies a voting algorithm to determine the most probable alphanumeric string.
3. Normalization: The string is sanitized (removing non-alphanumeric characters) and corrected for common OCR errors (e.g., confusing '0' and 'O').
4. Authorization: The backend checks the normalized string against a database of registered payment methods.
5. Session Initiation: Upon validation, the system sends a RemoteStartTransaction command to the specific EVSE (charger) associated with the camera's location.

Managing Operational Edge Cases

Reliability in ANPR systems often depends on how effectively they handle exceptions.

Cross-Bay Identification

In locations with adjacent charging bays, there is a risk of a camera detecting a vehicle in a neighboring spot.

• Solution: Strict definition of detection polygons is necessary. The system should only validate a plate if its coordinates fall specifically within the assigned bay's zone.

Vehicle Exit Detection

Ensuring a session ends or the bay status updates correctly when a vehicle leaves is crucial for availability tracking.

• Solution: Combining visual data with charger state (Current Import > 0A) helps prevent false positives. If a charger is actively delivering power, the system should effectively "lock" the session to that vehicle to prevent interference from passing traffic.

Environmental Obstruction

Mud, snow, or physical damage can render a plate unreadable.

• Fallback Strategy: ANPR should be treated as a convenience layer rather than the sole access method. Interfaces must always provide alternative authentication options, such as RFID or QR codes, to ensure service continuity.

Data Privacy and Compliance

Handling images of vehicle movements requires strict adherence to privacy regulations like GDPR.

1. Transient Processing: Ideally, video streams are processed in memory and not committed to storage unless a specific event triggers a log.
2. Data Minimization: For audit trails, systems should store only the cropped license plate image rather than the full scene, reducing the capture of unrelated personal data.
3. Secure Storage: Sensitive data should be encrypted at rest. In high-security applications, storing a hash of the license plate rather than the raw string can further protect user privacy.

Anthaathi's Implementation Approach

At Anthaathi, we integrate ANPR directly into the EV Platform to support fleet operations. This allows fleet managers to register vehicle lists, enabling a "plug-in" experience for drivers without the need for physical RFID cards. The focus is on reducing operational friction while maintaining robust audit logs for fleet management.

For detailed specifications on camera integration and site assessment, please contact our technical team.