Battery Energy Storage Monitoring & EMS Analytics
Primary service
Real-time Monitoring & Anomaly Detection
Secondary service
Performance & Cost Optimization
Proposed Cadvel capability — not a client case study.
Target client and segment
BESS operators, solar plus storage owners and EMS providers
Challenge
A battery fleet can produce extensive telemetry without giving operators a reliable explanation of lost availability or unusual behavior. The data must connect each symptom to an asset, operating state and maintenance action.
Key capabilities
- A consistent operational view from battery module to site.
- Early investigation of abnormal telemetry, connectivity loss and performance deviations.
- Efficient access to detailed incident history and longer-term performance trends.
Business Challenge
Asset context is incomplete
Modules, inverters and gateways are replaced or reassigned. Without historical relationships, an apparent trend may combine different physical assets.
Missing data resembles equipment failure
Telemetry gaps, sensor faults and genuine operating anomalies require different responses. Alerts need data-quality checks before escalation.
Operating conditions affect comparisons
State of charge, temperature and dispatch mode influence observed performance. Comparing unlike conditions produces misleading conclusions.
High-frequency history is costly
Keeping every sample at full resolution can increase cost while broad aggregations erase the detail needed for incident investigation.
Architecture and Tech Stack
Read-only BMS, EMS and inverter feeds → buffered ingestion → asset hierarchy and quality checks → time-series analytics → operator alerts and maintenance context. Retain detailed incident windows and governed longer-term aggregates.
Technology options
Kafka for buffered telemetry; ClickHouse for detailed time-series analysis; Datadog for ingestion health and alert routing. Object storage supports economical historical retention. Deploy on AWS or Azure according to site connectivity and residency needs.
Delivery boundary
OEM battery protection and EMS control remain authoritative. Cadvel analytics supports diagnosis; state-of-health estimates require suitable data and validation against the client’s accepted method.
Implementation and Solution
Proposed implementation stages
Define failure questions
Agree asset hierarchy, available sensors, operational limits and incident labels with engineers.
Establish reliable telemetry
Normalize units and timestamps; test gateway outages, backfill and component replacement history.
Validate analytics
Tune condition-aware alerts using known incidents and controlled tests; review false positives with operators.
Operationalize and tune cost
Connect maintenance workflows, set retention tiers and review alert usefulness and storage cost.
What this enables
Measurable success criteria — to be agreed and validated with the client; not achieved results.
Valid telemetry coverage
Valid samples received ÷ expected samples, by asset and operating period.
Detection lead time
Minutes between first actionable alert and confirmed incident or operator discovery.
Alert precision
Reviewed alerts confirmed as actionable ÷ all reviewed alerts, by alert type.
Telemetry unit cost
Monthly data-platform cost per monitored asset at the agreed sampling and retention settings.
KPI targets and acceptance tolerances are set with the client during discovery; no achieved outcomes or ROI are asserted.
Source inspiration / reference
Source-derived facts
Codibly describes Yotta Energy EMS enhancements for SOLARLEAF batteries, including site visualization, commissioning, asset mapping, OCPP integration and live diagnostics. The proposed condition-aware anomaly rules and retention strategy are not claims about Yotta’s implemented system or achieved savings.
Reference context only. Proposed Cadvel design and KPIs are independent of the source delivery.
