Next‐GenerationBattery Thermal Management: The Material Revolution, Intelligent Control, and Sustainable Development
次世代バッテリー熱管理：材料革命、知的制御、持続可能な発展 (AI 翻訳)
Dingle Zou, Xuelai Zhang, Jun Ji
🤖 gxceed AI 要約
日本語
本レビューは、リチウムイオン電池の熱管理システム（BTMS）を材料、制御、持続可能性の3側面から統合的に分析。PCMの熱伝導率と潜熱のトレードオフ、AI制御のブラックボックス性と解釈可能性の矛盾を指摘。リサイクル可能なバイオベース材料や物理ベンチマークによるAI検証など、カーボンニュートラルに向けた将来方向を示す。
English
This review integrates material, control, and sustainability dimensions of battery thermal management systems (BTMS). It identifies trade-offs in PCMs and AI control, and highlights the need for recyclable bio-based materials and physical validation of AI models to meet 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
日本の蓄電池産業やEV戦略に関連し、電池の安全性と寿命向上はGX投資の重要な要素。本レビューの材料・制御・持続可能性の統合視点は、日本の電池サプライチェーンにおける環境対応や資源循環の検討に示唆を与える。
In the global GX context
This review contributes to global battery research by linking thermal management with sustainability, relevant for EV and energy storage sectors. It underscores the need for recyclable materials and AI validation, aligning with international carbon neutrality targets and circular economy principles.
👥 読者別の含意
🔬研究者:Provides an integrated framework for BTMS research, highlighting trade-offs and future directions for sustainable battery design.
🏢実務担当者:Offers insights into material selection and AI control strategies for improving battery safety and longevity, relevant for EV and storage system manufacturers.
🏛政策担当者:Highlights the importance of supporting research on recyclable battery materials and standardized testing protocols to advance energy transition goals.
📄 Abstract（原文）
Lithium‐ion battery thermal management systems (BTMS) are critical for safety, longevity, and performance, yet existing reviews typically treat material innovations, control algorithms, and sustainability as separate topics. This article presents a systematic review that integrates these three dimensions and critically examines the inherent contradictions within each. Analyzing literature from 2010 to 2024, we identify two key trade‐offs: (i) between thermal conductivity and latent heat in phase change materials (PCMs) and (ii) between “black‐box” speed and physical interpretability in artificial intelligence (AI)‐driven control. We show that while high‐thermal‐conductivity composites and AI‐based predictive models can accelerate thermal runaway prediction by orders of magnitude, practical deployment remains limited by unresolved material incompatibilities, the lack of standardized multiphysics modeling protocols, and insufficient experimental validation of virtual data. Unlike prior reviews that focus on single technologies, this work provides an integrated, critical roadmap that bridges material science, control theory, and environmental sustainability. Future directions emphasize balancing property trade‐offs (e.g., gradient conductivity designs, multifunctional polymer skeletons), validating AI models with physical benchmarks, and designing recyclable, bio‐based BTMS materials to meet carbon neutrality goals.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1002/ente.70580first seen 2026-08-12 04:58:07
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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。