EVion EdgeX: An AI-Driven Multilayer Architecture for Electric Vehicle Telemetry, Diagnostics, and Smart Charging Optimization
Year: 2025
Type: conference-paper
Abstract: Electric vehicles are becoming increasingly popular in India as people look for cleaner alternatives, benefit from government incentives, and cope with rising fuel costs. However, real-world usage of these vehicles still faces hurdles such as uncertain driving ranges, variable battery performance, limited diagnostic insights, and a fragmented charging infrastructure. To address these challenges, this paper presents EVion EdgeX, an intelligent platform for electric vehicle telemetry and charging management that does not rely on proprietary manufacturer systems. The platform combines simulated vehicle telemetry, standardized diagnostic data parsing, cloudbased digital twin modeling, machine learning-driven range prediction, and a smart charging optimization engine. EVion EdgeX provides real-time monitoring, predictive maintenance, cost-effective charging recommendations, and support for multiple vehicle brands. This paper outlines the system architecture, methodology, data processing pipelines, analytical framework, practical use cases, and future plans for hardware integration. The proposed solution aims to create a scalable foundation for nationwide electric vehicle intelligence and optimized charging networks, helping users achieve more reliable, efficient, and sustainable mobility.
Authors: S, Krishna Rajan, V, Mahalakshmi
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7107979490
FWCI:
Cites:
Cited by:
Related to: