Intelligence

Fleet Battery Life Forecasting Platform

  • ERPNext
  • n8n
  • TimescaleDB
  • Neo4j
  • Ollama

Problem

Hundreds of dual-string VRLA banks across NSW age at block-level rates that nobody can see: discharge tests live on paper forms, impedance surveys pile up in spreadsheets, and the 80%-capacity moment arrives as a surprise — colliding with a supplier who is sometimes eight to ten weeks from having stock. Every year the capex bid for replacements is an educated guess, and the two battery teams route themselves across the state by calendar rather than by which strings actually need attention.

Solution

A per-block digital twin of the entire fleet: field readings are captured on a tablet or phone, normalised and temperature-corrected by an n8n pipeline, and written to TimescaleDB (chosen over a vector store because impedance and discharge curves are numeric time-series needing continuous aggregates, and it is a mature open-source Postgres extension) while Neo4j holds the structure — which block sits in which string, at which site, of which generation, with what criticality. An Ollama-hosted model fits each block's impedance and capacity trend to forecast its 80% date; forecasts feed a wave planner that groups whole-string replacements, flags gen-one blocks worth redeploying to lower-criticality sites, checks contracted supplier stock, and drafts purchase orders in ERPNext far enough ahead of the ten-week China lead time. The same forecasts drive a rolling three-year capex view and route the two test teams to the strings closest to end of life first, with the existing DNP3 charger 'battery fail' alarms ingested as an early-warning overlay.

What this lets you offer your customers

Substation and asset planners get something the utility has never been able to give them: a named replacement date for every string in the fleet, a defensible three-year capex forecast instead of an annual guess, and a standing commitment that no critical DC system will be running on a string forecast inside twelve months of end of life. The supplier gets a rolling demand forecast feed, which turns the period contract from a stockholding gamble into a scheduled pipeline — and turns the eight-week lead time from a crisis into a non-event.

Estimated Operational ROI

Estimated elimination of most surprise string failures (currently the dominant cause of emergency callouts), an estimated 15-25% reduction in test-team kilometres by routing on forecast risk rather than calendar, and an estimated one-cycle improvement in capex accuracy — replacement budgets bid against forecast dates rather than averages, with purchase orders raised an estimated 14+ weeks ahead of need so the 10-week China lead time never bites.

This is a design, not a system I have built. Book a call and I will tell you what it would take to build.

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