AI-Enabled EV Charging Optimization for Smart Mobility Energy Systems
AIを活用したEV充電最適化：スマートモビリティエネルギーシステムに向けて (AI 翻訳)
Murali Krishna Pasupuleti
🤖 gxceed AI 要約
日本語
本稿は、EV充電最適化を社会技術システムとして捉え、モデリング、因果推論、機械学習予測、信頼できるAI、MLOps、政策分析までを包括する枠組みを提示する。世界各地の事例をもとに、コスト・信頼性・排出削減・公平性等を考慮した充電インフラの設計・運用・ガバナンスを議論する。
English
This paper presents a comprehensive socio-technical framework for AI-enabled EV charging optimization, covering modeling, causal inference, ML prediction, trustworthy AI, MLOps, and policy analytics. It emphasizes balancing cost, reliability, emissions, fairness, and governance across global contexts, providing actionable insights for researchers and practitioners.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本ではEV普及と再生可能エネルギー統合が喫緊の課題であり、本稿のフレームワークはSSBJ開示や企業の脱炭素計画、充電インフラ整備に応用可能。特にV2Gや動的料金の政策設計に示唆を与える。
In the global GX context
Globally, EV charging optimization is critical for grid stability and renewable integration. The paper's socio-technical approach aligns with ISSB and CSRD disclosure requirements for transition plans and offers replicable methods for diverse regulatory environments.
👥 読者別の含意
🔬研究者:Provides a unified framework integrating AI methods with socio-technical governance for EV charging optimization.
🏢実務担当者:Offers practical guidance on deploying intelligent charging systems that balance cost, reliability, and equity.
🏛政策担当者:Highlights governance artifacts and policy analytics for public charging infrastructure and renewable integration.
📄 Abstract（原文）
Abstract AI-enabled EV charging optimization is becoming a central research and policy problem in the transition toward smart mobility energy systems. This manuscript develops a doctoral-level framework for understanding, modeling, governing, and deploying intelligent charging infrastructures across heterogeneous global contexts. It conceptualizes charging optimization as a coupled socio-technical challenge involving vehicles, users, chargers, grids, renewable-energy resources, electricity markets, urban space, data platforms, and institutional accountability. The book advances from foundational theory and uncertainty-aware research design to statistical explanation, causal inference, machine-learning prediction, trustworthy AI, big data engineering, MLOps, and applied policy horizons. It avoids narrow technological determinism by treating optimization as a governed capability that must balance cost, reliability, emissions, accessibility, privacy, cybersecurity, fairness, and public legitimacy. Across South Asia, Europe, Africa, and the Americas, the manuscript emphasizes adaptable research workflows, evaluation metrics, operational protocols, and governance artifacts for public charging corridors, fleets, logistics, renewable integration, and vehicle-to-grid coordination. The resulting monograph is suitable for researchers, doctoral scholars, mobility planners, utilities, charging operators, policymakers, and industry leaders seeking rigorous, reproducible, and globally relevant methods for AI-enabled charging intelligence. Keywords AI-enabled EV charging optimization, smart mobility energy systems, electric vehicles, charging infrastructure, demand forecasting, load management, causal inference, machine learning, reinforcement learning, graph learning, MLOps, data governance, renewable integration, vehicle-to-grid, dynamic pricing, distribution grids, trustworthiness, cybersecurity, fairness, reproducibility, policy analytics, fleet electrification, public charging, energy transition
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.62311/nesx/rb8j-978-81-687514-2-2first seen 2026-07-04 04:34:40
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。