Live demo

Fleet Battery Life Forecasting Platform

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.

• ERPNext
• n8n
• TimescaleDB
• Neo4j
• Ollama