Orchestrating EV‑Charging Analytics at Scale: An Apache Airflow Blueprint for Energy‑Infrastructure Pipelines

A production-grade Apache Airflow 3.0 pipeline orchestrating high-volume EV-charging telemetry to deliver near–real-time energy analytics, predictive maintenance, and grid-load forecasting at scale. The system processes 40M+ records daily with sub-15-minute latency, significantly reducing charger downtime and optimizing infrastructure performance.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-05-03
DOI
https://doi.org/10.5281/zenodo.15334158
Primary Topic
Vehicle emissions and performance
Type
article
Field-Weighted Citation Impact
0.00
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article

Orchestrating EV‑Charging Analytics at Scale: An Apache Airflow Blueprint for Energy‑Infrastructure Pipelines

Zenodo (CERN European Organization for Nuclear Research)
Vehicle emissions and performance
article

Orchestrating EV‑Charging Analytics at Scale: An Apache Airflow Blueprint for Energy‑Infrastructure Pipelines

article en

Abstract

A production-grade Apache Airflow 3.0 pipeline orchestrating high-volume EV-charging telemetry to deliver near–real-time energy analytics, predictive maintenance, and grid-load forecasting at scale. The system processes 40M+ records daily with sub-15-minute latency, significantly reducing charger downtime and optimizing infrastructure performance.

Zenodo (CERN European Organization for Nuclear Research)
General Motors (United States) (US)
Industry, innovation and infrastructure
Openalex Percentile: Top 54%
Vehicle emissions and performance
DOIOpen Access PDFOpenAlex
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Orchestrating EV‑Charging Analytics at Scale: An Apache Airflow Blueprint for Energy‑Infrastructure Pipelines · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS