Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Autonomous Edge-Deployed AI Agents for Electric Vehicle Charging Infrastructure Management

Mohammed Cherifi

TL;DR

Auralink SDC (Software-Defined Charging), an architecture deploying domain-specialized AI agents at the network edge for autonomous charging infrastructure management, and implementation patterns for edge-deployed industrial AI systems with safety-critical constraints are presented.

Abstract

Public EV charging infrastructure suffers from significant failure rates -- with field studies reporting up to 27.5% of DC fast chargers non-functional -- and multi-day mean time to resolution, imposing billions in annual economic burden. Cloud-centric architectures cannot achieve the latency, reliability, and bandwidth characteristics required for autonomous operation. We present Auralink SDC (Software-Defined Charging), an architecture deploying domain-specialized AI agents at the network edge for autonomous charging infrastructure management. Key contributions include: (1) Confidence-Calibrated Autonomous Resolution (CCAR), enabling autonomous remediation with formal false-positive bounds; (2) Adaptive Retrieval-Augmented Reasoning (ARA), combining dense and sparse retrieval with dynamic context allocation; (3) Auralink Edge Runtime, achieving sub-50ms TTFT on commodity hardware under PREEMPT_RT constraints; and (4) Hierarchical Multi-Agent Orchestration (HMAO). Implementation uses AuralinkLM models fine-tuned via QLoRA on a domain corpus spanning OCPP 1.6/2.0.1, ISO 15118, and operational incident histories. Evaluation on 18,000 labeled incidents in a controlled environment establishes 78% autonomous incident resolution, 87.6% diagnostic accuracy, and 28-48ms TTFT latency (P50). This work presents architecture and implementation patterns for edge-deployed industrial AI systems with safety-critical constraints.

Autonomous Edge-Deployed AI Agents for Electric Vehicle Charging Infrastructure Management

TL;DR

Auralink SDC (Software-Defined Charging), an architecture deploying domain-specialized AI agents at the network edge for autonomous charging infrastructure management, and implementation patterns for edge-deployed industrial AI systems with safety-critical constraints are presented.

Abstract

Public EV charging infrastructure suffers from significant failure rates -- with field studies reporting up to 27.5% of DC fast chargers non-functional -- and multi-day mean time to resolution, imposing billions in annual economic burden. Cloud-centric architectures cannot achieve the latency, reliability, and bandwidth characteristics required for autonomous operation. We present Auralink SDC (Software-Defined Charging), an architecture deploying domain-specialized AI agents at the network edge for autonomous charging infrastructure management. Key contributions include: (1) Confidence-Calibrated Autonomous Resolution (CCAR), enabling autonomous remediation with formal false-positive bounds; (2) Adaptive Retrieval-Augmented Reasoning (ARA), combining dense and sparse retrieval with dynamic context allocation; (3) Auralink Edge Runtime, achieving sub-50ms TTFT on commodity hardware under PREEMPT_RT constraints; and (4) Hierarchical Multi-Agent Orchestration (HMAO). Implementation uses AuralinkLM models fine-tuned via QLoRA on a domain corpus spanning OCPP 1.6/2.0.1, ISO 15118, and operational incident histories. Evaluation on 18,000 labeled incidents in a controlled environment establishes 78% autonomous incident resolution, 87.6% diagnostic accuracy, and 28-48ms TTFT latency (P50). This work presents architecture and implementation patterns for edge-deployed industrial AI systems with safety-critical constraints.
Paper Structure (98 sections, 10 equations, 5 figures, 27 tables, 10 algorithms)

This paper contains 98 sections, 10 equations, 5 figures, 27 tables, 10 algorithms.

Table of Contents

  1. Introduction
  2. The Reliability Crisis in Public Charging
  3. Fundamental Limitations of Cloud-Centric Architectures
  4. The Edge AI Paradigm
  5. Contributions
  6. Paper Organization
  7. Background and Related Work
  8. EV Charging Protocol Ecosystem
  9. Open Charge Point Protocol (OCPP)
  10. ISO 15118: Vehicle-Grid Communication
  11. IEC 61851: Physical Layer Signaling
  12. Edge Computing and Edge AI
  13. Large Language Models for Technical Domains
  14. Autonomous and Self-Managing Systems
  15. Related Work: Positioning and Novelty
  16. ...and 83 more sections

Figures (5)

  • Figure 1: Three-tier Auralink SDC architecture
  • Figure 2: Detailed Auralink SDC system architecture showing component interactions across all three tiers. The edge tier contains the core AI capabilities (DiagnosticEngine, ARA, CCAR) enabling autonomous operation. Latency figures represent P50 inference times on target hardware.
  • Figure 3: QLoRA fine-tuning pipeline: curated domain-specific training corpus processed through three-stage curriculum learning, producing domain-adapted model subsequently quantized to Q4_K_M (8.2 GB) for edge deployment.
  • Figure 4: CCAR decision flow (simplified representation). Actions above $\tau_{auto}=0.90$ execute autonomously without notification; actions between $0.85$ and $0.90$ execute with operator notification; between $\tau_{assist}=0.70$ and $0.85$ require human confirmation; below $\tau_{assist}$ escalate to manual resolution. See Table \ref{['tab:ccar_thresholds']} for the full five-tier decision matrix.
  • Figure 5: HMAO agent hierarchy with intent routing. Dashed lines indicate inter-agent collaboration for complex multi-domain incidents. Model sizes (0.5B/14B) indicate AuralinkLM deployment tier.

Theorems & Definitions (5)

  • Definition 1: Quantized Low-Rank Adaptation
  • Definition 2: K-Quant Mixed Precision
  • Definition 3: Action Confidence Function
  • Definition 4: Safety-Critical Action Set
  • Definition 5: Reciprocal Rank Fusion