AI Integrated Neutrosophic MCDM Framework for Promoting Carbon Neutrality through Li-Ion Battery Selection for Electric Vehicles
AI統合ニュートロソフィックMCDMフレームワークによる電気自動車用リチウムイオン電池選択を通じたカーボンニュートラリティ推進 (AI 翻訳)
Nivetha Martin, Rajkumar S, Said Broumi
🤖 gxceed AI 要約
日本語
本研究は、電気自動車（BEV）用Liイオン電池の選択において、ニュートロソフィックMCDM手法とAI（ランダムフォレスト、SHAP）を統合したフレームワークを提案。従来の単純な加重和法に代わり、複数のMCDM法で基準重みとランキングを決定し、一貫性を検証。製造業者の意思決定を支援し、カーボンニュートラル達成に貢献する。
English
This study proposes an integrated neutrosophic MCDM framework with AI (random forest, SHAP) for selecting Li-ion batteries for BEVs. Unlike simple weighted sum methods, it applies multiple MCDM techniques to determine criterion weights and rankings, validated for consistency. The framework helps manufacturers make optimal battery choices, supporting carbon neutrality goals.
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
日本のEV普及政策（2035年新車販売電動化目標）やGXリーグの取り組みにおいて、電池選定はサプライチェーン排出削減の鍵。本フレームワークは、多基準評価にAIを活用し、日本企業の電池調達判断に役立つ可能性がある。
In the global GX context
Global EV adoption (e.g., EU ban on ICE vehicles, US IRA) makes battery selection critical for decarbonization. This work integrates AI into MCDM, offering a transparent and robust method for manufacturers worldwide to align with carbon neutrality strategies. It complements TCFD/ISSB disclosure by supporting informed procurement decisions.
👥 読者別の含意
🔬研究者:Provides a novel combination of neutrosophic MCDM and AI for battery selection, with validation techniques like SHAP that can be extended to other sustainability decision problems.
🏢実務担当者:Manufacturers can use the framework to systematically evaluate Li-ion batteries, balancing performance, cost, and environmental criteria for better procurement decisions.
🏛政策担当者:Highlights the role of advanced decision-support tools in enabling industry transitions; could inform guidelines for EV battery standards and incentives.
📄 Abstract（原文）
vehicles in the coming decades to promote carbon neutrality. The compatibility and robustness of Li-ion based batteries make them more preferable in BEVs. A recent study has identified five different groups of Li- ion based batteries used in BEVs and applied a very simple multi–criteria decision making (MCDM) method of Weighted sum with equal criterion weights and linguistic matrix to rank the batteries. The present research work considers the same decision making problem and applies different MCDM methods to determine the criterion weights and ranking of the batteries with Triangular Neutrosophic matrix. The methodology proposed in this work works in five phases. The consistency of the neutrosophic ranking results is validated using Random forest technique, Feature importance analysis and SHAP explainability analysis. The comprehensive MCDM framework presented in this work will enable the decision makers of manufacturing company to make ideal selection of the electric vehicle batteries on comparing the ranking scores of the batteries using different methods with different criterion weights.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.63924/jau.v1i2.296first seen 2026-07-09 05:13:59
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