Time-series data is data recorded as a sequence of values over time, where each measurement has a timestamp. In EV charging, time-series data captures how charging, site load, and equipment status change minute-by-minute (or second-by-second), rather than only storing totals.
Time-series data is essential for understanding real operating behavior, diagnosing issues, and optimizing performance across charging sites.
Why Time-series Data Matters in EV Charging
Time-series data enables insights that aggregated totals cannot provide:
– Detect outages and intermittent connectivity issues that don’t show in monthly reports
– Diagnose root causes by correlating events (fault → power drop → session stop)
– Optimize load management and enforce a maximum site demand limit in real time
– Analyze peak demand drivers and support time-of-use optimization
– Improve uptime by tracking fault frequency, resets, and recovery patterns
– Support evidence-based billing and dispute resolution with session timelines
– Build accurate TCO dashboards and sustainability reporting with better granularity
Common EV Charging Time-series Data Streams
Time-series data in charging environments often includes:
Charger and Connector Status
– Availability/occupied/charging/fault states over time
– Heartbeat and connectivity health (online/offline periods)
– Start/stop events, authorization events, fault codes (event series)
Electrical Measurements
– Instantaneous power (kW), current (A), and voltage (V)
– Energy accumulation (kWh counter progression) during sessions
– Phase measurements in three-phase sites (L1/L2/L3 currents, phase imbalance)
– Power factor and other power quality indicators (where monitored)
Site Energy and Constraints
– Site import power and feeder loads from meters or CT clamps
– Demand threshold crossings and curtailment actions
– Battery storage charge/discharge power if stationary storage is present
– Solar PV generation if on-site renewables are integrated
Thermal and Health Signals
– Internal temperatures and temperature derating events
– Fan status and cooling control signals
– Tamper detection events and cabinet access logs (where available)
How Time-series Data Is Used
Time-series data supports multiple operational and business workflows:
– Real-time monitoring and alerting (telemetry streaming)
– Load allocation and dynamic power control (load balancing)
– Reliability analytics: uptime, MTTR, recurring faults
– Energy cost optimization under ToU tariffs
– Customer reporting: utilization, throughput, sustainability metrics
– Capacity planning: when to add chargers, upgrade feeders, or add storage
Design Considerations for Time-series Data Systems
– Sampling rate: higher frequency improves diagnostics but increases data volume
– Time synchronization: consistent timestamps and time zones are critical
– Data model: stable asset IDs (site → charger → connector) enable clean roll-ups
– Event vs measurement streams: store both for complete context
– Retention policy: keep high-resolution data for troubleshooting, then downsample
– Security and privacy: access control for user-linked data and location data
Common Pitfalls
– Collecting too much data without a clear use case (noise and cost)
– Missing metadata, making it impossible to link data to the correct charger or site
– Timezone/DST mistakes causing incorrect ToU billing and analytics
– Relying only on session totals, losing information about curtailment and faults
– Lack of data quality flags (gaps, duplicates, estimated values)
Related Glossary Terms
Telemetry Streaming
Load Management
Load Balancing
Maximum Site Demand Limit
Time-of-Use Optimization
Charger Uptime
Mean Time To Repair (MTTR)
Power Quality
Temperature Derating
Sustainability Dashboards