Health and Uptime

The Problem

Why does this extension exist?

A faulted charger is lost revenue and a frustrated driver. The average CPO discovers a charger fault 4-8 hours after it occurs - often from a driver complaint. Manual recovery takes 1-3 days. Meanwhile, the charger sits offline, earning nothing. Automated recovery and predictive maintenance change this equation entirely.

The Solution

What you get

Automated fault recovery pipeline: soft reset, hard reset, unlock connector, escalate - all without human intervention
Predictive fault detection: 90-day error pattern analysis with risk scoring per charger
Uptime SLA tracking per charger and per location - know your actual uptime vs target
Ticketing integration: Zendesk and Jira - automatic ticket creation when automated recovery fails
MTTR (Mean Time To Recovery) analytics - measure and improve your recovery performance
OpenTelemetry distributed tracing for deep technical observability
Prometheus metrics endpoint - export to Grafana for custom dashboards

🎯 Who is this for?

Any CPO managing physical chargers - especially operators with 10+ chargers, those with SLA commitments, or operators in regulated markets with uptime requirements.

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🔌 Modular by design

Enable or disable per tenant. Pay only for what you use. All marketplace integrate seamlessly.

Key Capabilities

Everything included

Automated Recovery

Soft reset, hard reset, unlock connector. The system tries every recovery option before escalating to a human. Most faults resolved in under 5 minutes.

Predictive Maintenance

90-day error pattern analysis assigns a risk score to every charger. Charger 42: 78% failure probability in 7 days - schedule maintenance before the breakdown.

Uptime SLA Tracking

Track actual uptime per charger and per location. Compare against your SLA targets. Identify chronic underperformers.

Auto-Ticketing

When automated recovery fails, a Zendesk or Jira ticket is created automatically with full fault context. Technician dispatched with all the information they need.

MTTR Analytics

Track Mean Time To Recovery over time. See whether your recovery performance is improving. Benchmark against industry standards.

Prometheus and Grafana

Export metrics to Prometheus and visualise in Grafana. Build custom dashboards for your operations team.

Use Cases

See it in action

How operators around the world can use this extension to solve real problems and grow their business.

Charger fault resolved in 4 minutes without human intervention

Charger CP042 reports ConnectorLockFailure at 2:17 AM. The system attempts a soft reset - fails. Hard reset - succeeds. Charger back online at 2:21 AM. Total downtime: 4 minutes. No technician called. No driver complaint.

Predictive maintenance prevents breakdown

The system flags Charger 17: PowerSwitchFailure errors increasing - 82% failure probability in 5 days. The operations manager schedules a maintenance visit. The technician finds a failing relay and replaces it. The charger never goes offline.

MTTR improves from 4.2 hours to 47 minutes

A CPO enables automated recovery across 80 chargers. Over 3 months, MTTR drops from 4.2 hours (manual process) to 47 minutes (automated recovery plus auto-ticketing). Uptime improves from 94.2% to 98.7%.

Simulated Case Study

A UK CPO improves uptime from 94% to 98.7% with automated recovery

A UK CPO operating 120 chargers across 15 locations was manually responding to faults - average MTTR was 4.2 hours. Many faults occurred overnight and were not discovered until morning. After enabling Health and Uptime with automated recovery, 73% of faults are resolved automatically in under 10 minutes. MTTR dropped to 47 minutes. Network uptime improved from 94% to 98.7%.

98.7%

Network uptime

47 min

Average MTTR

73%

Faults auto-resolved

-89%

MTTR improvement