AI-Based Carbon Emissions Monitoring for Electric Vehicles: A Technical Review
電気自動車におけるAIベースの炭素排出モニタリング：技術レビュー (AI 翻訳)
Raghavendra Kurva
🤖 gxceed AI 要約
日本語
本稿は、電気自動車（EV）のカーボンフットプリントをリアルタイムで監視するための人工知能（AI）技術をレビューする。機械学習により、センサーや車両システム、リサイクル施設からのデータを統合し、動的な排出プロファイルを生成する。スマート充電やルート最適化などの介入を支援し、EVのライフサイクル全体での排出削減を可能にする。
English
This technical review examines AI and machine learning approaches for real-time carbon footprint monitoring of electric vehicles (EVs). By integrating data from sensors, vehicle systems, and recycling facilities, dynamic emission profiles are generated. The platform supports interventions such as smart charging and route optimization to reduce lifecycle emissions, transforming environmental monitoring for sustainable transport.
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
日本はEV普及とカーボンニュートラルを推進しており、本レビューはAI活用によるリアルタイム排出監視手法を提供する。これは、SSBJや有価証券報告書での炭素情報開示の高度化に寄与し得る。
In the global GX context
In the global context of TCFD/ISSB disclosure and transport decarbonization, this review highlights how AI can enhance emissions monitoring for EVs, enabling more accurate scope 1/2/3 reporting and operational efficiency, aligning with transition finance goals.
👥 読者別の含意
🔬研究者:This paper provides a comprehensive overview of AI techniques for EV lifecycle carbon accounting, identifying research gaps in real-time data integration and predictive modeling.
🏢実務担当者:Corporate sustainability teams can leverage the described AI frameworks to improve the accuracy and granularity of EV fleet emission reporting and identify reduction levers.
🏛政策担当者:Policymakers should note the potential of AI-based monitoring to enforce emission standards and support EV incentive programs with verifiable real-world data.
📄 Abstract（原文）
Electric Vehicles (EVs) have been identified as a vital part of global transport emissions reductions. Current methods of calculating an electric vehicle's (EV's) carbon footprint do not allow this data to be gathered and analyzed close to real-time. They cannot capture operational variations across vehicle lifecycles effectively. Artificial intelligence offers new possibilities for continuous emissions monitoring. Machine learning processes data from industrial sensors, vehicle systems, and recycling facilities. This integration creates dynamic carbon footprint calculations that reflect actual conditions. Real-time grid data combined with vehicle energy consumption provides accurate emission profiles. Neural networks automatically identify emission hotspots and unusual patterns. The platform supports interventions targeting manufacturing processes, charging behaviors, and maintenance. Predictive modeling techniques enable forecasts of component failure and recommend interventions to extend component life. Smart Charging shifts electricity demand to periods when the electrical grid emits lower emissions. Route optimization accounts for terrain, traffic, and weather to minimize consumption. The convergence of AI with lifecycle assessment enables evidence-based emission reduction decisions. This advancement transforms environmental monitoring and supports sustainable transportation transitions.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.52783/jisem.v11i1s.14305first seen 2026-05-05 23:51:09
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。