End User
EMOT
Description
The Predictive Maintenance service for EV Charging Points implements a monitoring and prognostics framework that detects abnormal behaviour, emerging faults, and degradation patterns in EV charging infrastructure before they cause service interruption. Combining rule-based diagnostics with data-driven anomaly detection, the service supports operators in reducing charger downtime, improving maintenance planning, extending charger lifetime, and ensuring safer and more reliable charging services.
Core Capabilities
Monitoring & Anomaly Detection
Predictive & Prescriptive Analytics
Business Need
EV charging points experience a wide range of issues — connector wear, overheating, communication faults, contactor degradation — that often first appear as subtle telemetry deviations before developing into visible failures. In distributed field conditions where maintenance access may be delayed, early detection enables planned intervention during convenient maintenance windows and prevents charger unavailability that affects user satisfaction and community energy management.
Key Performance Indicators
Precision, recall, F1-score, and false alarm rate for fault detection
Detection lead time relative to confirmed charger failures
Charger availability improvement and reduction in failed charging sessions
API processing latency and service uptime
Data Provided
Charger health indicators, anomaly flags, fault likelihood scores, and maintenance alerts per charger
Degradation trends, fault category suggestions, time-to-intervention recommendations (where data supports)
Explanatory signals identifying contributing telemetry variables
Inputs: charger status codes, charging session data, energy delivery, temperature, voltage/current, fault codes, communication logs
TEF
TEF EV