With the rapid adoption of electric vehicles, the number of public and private charging stations is growing exponentially. The stability and safety of charging facilities directly affect user experience and grid operation. Traditional methods relying on manual inspection and centralized monitoring can no longer meet the demands of large-scale charging networks. Deploying an ARM AI embedded controller with edge intelligence for real-time fault detection and automatic isolation has become a key trend in the industry.
Common issues in EV chargers include:
Hardware failures: relay stuck, communication interruption, controller board malfunction.
Electrical anomalies: overcurrent, short circuit, grounding faults, leakage.
Environmental & safety risks: overheating, smoke, water ingress.
If these problems are not identified and isolated in time, they may cause charging interruption, user complaints, or even power safety incidents. Therefore, deploying an ARM-based controller with local AI inference capability at the charging station enables real-time monitoring and automatic isolation.
The ARM AI embedded controller integrates data acquisition, AI inference engine, decision-making logic, and communication modules, and can be directly deployed at the charging site:
Real-time Fault Detection
Collects signals from current, voltage, temperature, and leakage sensors, then applies AI models to detect abnormal patterns and identify potential faults quickly.
Automatic Isolation & Recovery
When a critical fault with high confidence is detected, the controller immediately disconnects the output circuit of the faulty charger for safe isolation. Automatic restart attempts can be executed; if recovery fails, the charger remains isolated and the event is reported to the cloud.
Edge Intelligence + Cloud Collaboration
Local decision-making ensures low-latency safety response, while diagnostic logs and event details are synchronized with the cloud platform for centralized monitoring, trend analysis, and remote command dispatch.
Graded Alarms & Traceability
Fault events are classified by severity, and detailed diagnostic reports are generated. Operators can quickly locate issues through a visual interface.
Security & Reliability
Device certificates, encrypted communication, and audit logs protect the system from cyber threats and misoperations.
Physical Layer: EV chargers, circuit breakers, sensors (current, voltage, temperature, leakage).
Edge Layer (ARM AI Controller BL440 Series):
Data collection and preprocessing
AI inference (TFLite/ONNX models)
Decision-making & isolation execution
Local logging and alarm reporting
Cloud Platform: O&M dashboard, historical data analysis, AI model updates, OTA firmware upgrades.
User Layer: Operators receive alarms and can issue remote commands via mobile app or web portal.
Reduced Fault Handling Time: from hours of manual inspection to millisecond-level automated isolation.
Enhanced Safety: prevents escalation of electrical incidents and ensures system stability.
Lower O&M Costs: fewer on-site inspections and reduced emergency repair costs.
Improved User Experience: keeps more chargers online and available.
The ARM AI embedded controller brings self-diagnosis, self-isolation, and self-recovery capabilities to EV charging stations, transforming operations from “manual monitoring” to “AI-driven autonomy.” This enhances equipment reliability, improves operational efficiency, and provides a strong foundation for the large-scale expansion of future EV charging infrastructure.
