Energy analytics is the collection, processing, and interpretation of energy data to understand how, when, and where electricity is used—and to optimize cost, performance, and sustainability. In EV charging, energy analytics turns raw charging-session and site-meter data into insights that improve uptime, utilization, load management, fleet readiness, and carbon reporting.
What Is Energy Analytics?
Energy analytics uses measured data (kWh, power demand, time, tariffs, emissions factors) to produce actionable metrics and recommendations.
– Charger/session data: kWh delivered, peak power, session duration, idle time, start/stop times
– Site electrical data: building load, transformer utilization, feeder limits, PV generation, battery dispatch
– Commercial data: tariffs, demand charges, cost allocation, revenue, refunds/chargebacks (where relevant)
– Sustainability data: carbon intensity, emission factors, renewable attribution
The result is a clearer view of energy behavior across chargers, sites, and fleets.
Why Energy Analytics Matters for EV Charging
EV charging can become one of the largest controllable loads at a site. Analytics enables control and accountability.
– Identifies peak demand drivers and supports peak shaving and capacity planning
– Improves fleet operations by tracking readiness and charging availability
– Helps CPOs and site owners increase ROI through higher utilization and fewer operational issues
– Supports fair billing models and cost allocation across users or cost centers
– Enables sustainability reporting such as CO₂e per session, per vehicle, or per depot
– Detects inefficiencies like excessive idle time, low power delivery, or recurring aborted sessions
Key Metrics Used in EV Charging Energy Analytics
– kWh per session and kWh per connector/day
– Load profiles: hourly/daily power demand and peak periods
– Utilization rate and occupancy vs energy delivered
– Charging efficiency indicators (where measured): delivered vs measured upstream energy
– Idle time and bay turnover performance
– Demand charge exposure: maximum kW peaks and their timing
– Uptime and fault correlation: energy and session anomalies linked to errors
– Carbon metrics: kg CO₂e per kWh, per session, per vehicle (using emission factors)
How Energy Analytics Works
Energy analytics typically combines multiple data sources into a unified view.
– EVSE data via OCPP from chargers to a CPMS
– Metering data (MID meter, site meters, sub-meters) for billing-grade accounting
– Building management or energy management systems for facility load context
– Tariff and pricing tables for cost modeling
– Optional DER data: PV output, battery state-of-charge, inverter power flows
Analytics platforms then clean, normalize, and aggregate the data to generate dashboards, alerts, and optimization actions.
Common Use Cases
– Workplace charging: track employee charging patterns and plan expansion
– Fleet depots: ensure vehicle readiness and optimize charging schedules around shifts
– Public destination charging: improve utilization and pricing strategy
– Multi-site operations: benchmark sites, detect underperformers, and standardize best practices
– Power management: validate dynamic load balancing performance and avoid overload events
– Sustainability reporting: quantify carbon footprint, renewable impact, and CO₂ savings
Optimization Actions Enabled by Energy Analytics
– Implement dynamic load management based on real demand and grid constraints
– Shift charging to lower-cost periods using scheduling or smart charging policies
– Right-size the number of chargers and power capacity upgrades based on utilization evidence
– Adjust pricing (where applicable) to reduce idle time and improve bay turnover
– Improve maintenance by detecting early warning signals (thermal events, repeated faults, unstable power delivery)
– Improve fleet routing and dispatch readiness with predictive charging insights
Limitations to Consider
– Data quality depends on metering accuracy, timestamps, connectivity, and consistent charger configuration
– Analytics may require integration between CPMS, billing, and site energy systems
– Privacy and access control must be managed when tracking user-level charging behavior
– Carbon analytics depends heavily on correct emission factors and reporting boundaries
– Insights only create value when linked to operational processes and accountability
Related Glossary Terms
Charge Point Management System (CPMS)
OCPP
Load Management
Dynamic Load Balancing
Demand Charges
Tariffs
Carbon Intensity
Emission Factors