Resilient Connectivity Patterns for Electric Vehicle Fleets

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Authors: Sai Dheeraj Guntupalli
Cited by: 0
References: 17

Abstract

Electric vehicle fleet operations face significant connectivity challenges across heterogeneous network environments, requiring innovative solutions for seamless communication maintenance. This article presents a comprehensive connectivity architecture comprising Multi-Network Adaptive Routing (MNAR), Context-Aware Data Prioritization (CADP), and Predictive Connectivity Health Modeling (PCHM) frameworks designed to address critical gaps in current fleet communication systems. The MNAR framework enables dynamic network discovery and intelligent switching across cellular, Wi-Fi, satellite, and mesh technologies through real-time performance optimization algorithms. CADP implements hierarchical data classification and priority assignment mechanisms that optimize bandwidth utilization based on operational context and mission criticality. PCHM employs machine learning algorithms for connectivity degradation prediction and proactive network management strategies. The integrated security architecture addresses multi-network vulnerabilities through end-to-end encryption, zero-trust authentication, and comprehensive intrusion detection systems. Performance evaluation demonstrates substantial improvements in connectivity uptime, latency reduction, bandwidth efficiency, and cost optimization compared to traditional single-network approaches. The proposed architecture exhibits superior scalability across diverse fleet deployments while maintaining consistent performance characteristics. Implementation frameworks include microservices-based deployment, container orchestration, and blockchain integration for secure fleet-to-fleet communication. Validation through simulation environments and real-world pilot implementations confirms system effectiveness across urban, rural, and challenging operational scenarios. Future development opportunities encompass 5G network slicing integration, quantum-resistant encryption protocols, and AI-driven autonomous optimization capabilities with cross-industry applicability to maritime and aerospace domains.

Fields of Study & Subjects

Vehicular Ad Hoc Networks (VANETs) Electric Vehicles and Infrastructure Cybersecurity and Information Systems

Publication details

Journal / source
International Journal of Computational and Experimental Science and Engineering
Volume
12
Issue
1
Publication date
8 March 2026
Language
EN