Citation

Hossen, Md Sabbir and Ramasamy, Gobbi and Ngu, Eng Eng and Shern, Siow Jat and Sarker, Md Tanjil (2026) OCPP integrated artificial intelligence for forecasting scheduling and anomaly detection in city scale electric vehicle charging under urban tariffs. Scientific Reports, 16 (1). ISSN 2045-2322

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Abstract

Urban EV uptake is raising feeder peaks and energy costs in city networks. We present an AI framework integrated with the open charge point protocol (OCPP), a standard communication protocol for EV charging systems that combines short-horizon demand forecasting with tariff-aware and fairness-aware scheduling and runtime anomaly detection from charger telemetry. Decisions are encoded with OCPP 1.6 and 2.0.1 operations including SetChargingProfile, ClearChargingProfile, GetCompositeSchedule, RemoteStartTransaction and RemoteStopTransaction. The system runs in rolling 15-minute control on commodity hardware. Using a two-year multi-station tariff-aware urban dataset with 1,553 operational days and 455 days with tariff coverage, the scheduler delivers average reductions of 5.0% for GA and 8.2% for a hybrid scheduling approach combining genetic algorithms (GA) and Q-learning (a reinforcement learning method) in both feeder peak and charging cost relative to the baseline. The policy conserves daily energy and applies a fairness mechanism that limits excessive delays for individual users limiting the concentration of deferrals. Under fixed caps and posted tariffs, the pipeline is deterministic, so day-level confidence intervals collapse to a point, and small parameter jitter leaves the mean unchanged within measurement noise. For anomaly detection, a CNN reaches ROC AUC 0.914 and the Autoencoder and Isolation Forest reach 0.735 and 0.636. A latency budget covering ingestion, forecasting, scheduling and OCPP round trip confirms near realtime feasibility with optimization overhead below 0.1 s. We provide a minimal reproducibility package with code, data splits, and scripts. The results show that standards-compliant and OCPP-aware AI can deliver measurable grid and cost benefits at the city scale and offer a practical path for sustainable and equitable EV charging in smart city contexts.

Item Type:	Article
Uncontrolled Keywords:	Electric vehicle charging
Subjects:	T Technology > TL Motor vehicles. Aeronautics. Astronautics > TL1-484 Motor vehicles. Cycles
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	04 Aug 2026 04:00
Last Modified:	04 Aug 2026 04:00
URII:	http://shdl.mmu.edu.my/id/eprint/16477

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