Distributed Equipment with No Central Visibility
The client sold mobile EV charging units that could be deployed without permits, electrical upgrades, or on-site construction. Trailers, pods, and skid-mounted systems that showed up, charged vehicles, and moved on when the job was done. The hardware worked. The problem was knowing what it was doing.
Each unit operated independently. If something faulted, if fuel ran low, if a charging session failed, the only way to find out was a phone call from an unhappy customer or a technician visit that turned up nothing wrong by the time they arrived. The operations team was small and the equipment was scattered. They needed a way to see the whole fleet from one screen and get notified before problems became outages.
A Control Layer Across the Entire Fleet
Sequoia Applied Technologies is a Santa Clara software engineering firm that builds IoT platforms, embedded systems, and cloud infrastructure for cleantech, life sciences, and enterprise software companies. This engagement covered the supervisory control platform that would sit above the client's fleet of mobile charging equipment.
The platform needed to collect telemetry from each unit, evaluate it against operational rules, surface alarms when thresholds were crossed, and give operators a dashboard for status and control. It also needed to work when connectivity was spotty, which meant edge logic that could keep the equipment running safely even if the network link dropped.
Edge Resilience and Centralized Decisions
The architecture split responsibility between the edge and the cloud. Each charging unit ran an edge compute layer that collected sensor readings, executed local safety rules, and cached data when the network was unavailable. When connectivity returned, the edge device synced its stored telemetry upstream. This meant a unit deployed in a parking lot with weak cellular coverage could still operate correctly and report its history once it reconnected.
Upstream, a messaging framework carried telemetry to the supervisory engine. The engine maintained a state model for each unit and evaluated incoming readings against a configurable rule set. Some rules were simple threshold checks: generator fuel below a certain level, battery temperature above a certain limit. Others combined multiple conditions: a charging session that started but showed no energy delivery after a timeout period.
When a rule fired, the system generated an alarm with a severity level. High severity alarms pushed notifications to operators immediately. Lower severity alarms were logged and surfaced in the dashboard for review during the next shift. Operators could acknowledge alarms, add notes, and mark them resolved. Every state change and control action was written to time series storage, creating an audit trail for diagnostics and compliance.
The dashboard showed live status for each unit: location, operational state, active sessions, alarm counts, and recent history. Operators could drill into individual units for detailed telemetry, replay past sessions, and push configuration changes to the edge. The architecture was modular enough that adding a new equipment type or a new rule set did not require reworking the core platform.
Fewer Truck Rolls, Faster Response
The platform gave the operations team the visibility they had been missing. They could see which units were healthy, which needed attention, and which were trending toward trouble before anything actually broke. Alarms replaced guesswork. The technicians who used to drive out on speculation now drove out with a specific fault code and a plan.
The edge resilience design proved its worth in the field. Units deployed to sites with unreliable connectivity continued operating and caught up their telemetry when they reconnected. No data was lost, no alarms were missed, just delayed until the link came back. For a fleet spread across a wide geography, that reliability was the difference between a manageable operation and a constant firefight.
Common Questions About Supervisory Control for Industrial IoT
What is a supervisory control system for industrial IoT?
A supervisory control system sits above individual devices and makes decisions based on their combined state. It collects telemetry from sensors and equipment, evaluates that data against configured rules, and triggers actions or alerts when conditions are met. In an industrial IoT context, the system might monitor dozens or hundreds of distributed assets, each reporting temperature, power draw, connectivity status, and operational metrics. The supervisory layer turns that flood of data into actionable signals for operators.
Why does mobile EV charging equipment need remote monitoring?
Mobile charging units are deployed to temporary sites, fleet depots, event venues, and locations without permanent grid infrastructure. They may run unattended for days or weeks. Without remote visibility, operators have no way to know if a unit has faulted, if fuel or battery reserves are low, or if utilization patterns have shifted. Remote monitoring prevents stranded assets and lets a small operations team manage equipment spread across a wide geography.
What does edge automation mean in this context?
Edge automation means the control logic runs on hardware at the equipment site rather than relying entirely on a cloud connection. If the network link drops, the edge device can continue operating, logging data locally, and executing safety rules without waiting for instructions from a remote server. When connectivity returns, the edge device syncs its stored telemetry upstream. This resilience matters for equipment deployed in areas with spotty cellular coverage.
How does the alerting system work?
The supervisory engine evaluates incoming telemetry against a rule set that defines normal operating ranges and fault conditions. When a reading crosses a threshold or a combination of states indicates a problem, the system generates an alarm with a severity level. High severity alarms notify operators immediately through the dashboard, email, or SMS. Lower severity alarms are logged for review. Operators can acknowledge alarms, mark them resolved, or escalate them depending on the situation.
What data does the system capture for compliance and diagnostics?
Every state change, control action, and sensor reading is written to time series storage with a timestamp. This creates a complete audit trail that supports both diagnostics and compliance reporting. If a charging session fails or equipment behaves unexpectedly, engineers can replay the telemetry to see exactly what happened. For regulatory or contractual reporting, the stored data can be aggregated into utilization summaries, uptime metrics, and energy delivery totals.
What did Sequoia build for this engagement?
Sequoia Applied Technologies built a modular supervisory control platform covering the edge compute layer, messaging framework, decision engine, alarm management, and operator dashboard. The architecture was designed to accommodate additional equipment types and rule sets without reworking the core system. The platform integrates with the client's existing fleet of mobile charging units and provides the visibility and control they needed to scale operations.