A reliability-enhancing framework for remote electric vehicle maintenance telemetry using 5G QoS-Aware network slicing

Abstract

Remote maintenance of electric vehicles depends on continuous and heterogeneous telemetry to support diagnostics, fault detection, and maintenance decision-making. The operational value of this telemetry depends on timely and continuous delivery, but shared cellular congestion can delay or drop safety-critical maintenance messages. This paper proposes a quality-of-service (QoS)-aware 5G radio access network slicing framework that aligns maintenance traffic criticality with differentiated network treatment. EV maintenance uplink telemetry is formalized into three representative traffic classes, namely routine diagnostics, maintenance alerts, and real-time fault reporting. Each class is mapped to differentiated 5G QoS flows and QoS-aware scheduling according to its operational urgency. The framework is implemented in ns-3 using the 5G-LENA module and evaluated through a controlled background-load sweep comparing best-effort and slice-aware configurations. Performance is assessed using packet delivery ratio (PDR) and delivered-packet latency to quantify the reliability and timeliness of telemetry delivery. The slice-aware configuration can preserve delivery and low latency for maintenance alerts and real-time fault reporting under congestion, while the baseline shows packet loss and increased latency as offered load increases. Routine diagnostic traffic remains best-effort and degrades under high load, confirming selective protection rather than uniform improvement. The paper connects remote EV maintenance semantics to RAN-visible 5G QoS treatment and evaluates this mapping under controlled congestion. It shows how maintenance-aware prioritization preserves delivery continuity and timeliness for safety-critical telemetry under network congestion.