Application of Machine Learning in Low‐Carbon Economy: A Comprehensive Review of Predicting Cycle Life of Lithium/Sodium‐Ion Batteries
低炭素経済における機械学習の応用：リチウム/ナトリウムイオン電池のサイクル寿命予測の包括的レビュー (AI 翻訳)
Bo Zhang, Xiao‐Min Zou, Xin Wen, Jiandi Wu, Jing‐Jing Pan, Xin Tan, Jamie Cross
🤖 gxceed AI 要約
日本語
本レビューは、リチウムイオンおよびナトリウムイオン電池のサイクル寿命予測における機械学習（ML）手法の応用を包括的にまとめた。教師あり学習、教師なし学習、半教師あり学習、深層学習などのアルゴリズムの原理と性能を比較し、EVや再生可能エネルギー貯蔵におけるバッテリー最適化、安全性向上、二次利用の可能性を示す。低炭素移行の加速に貢献する。
English
This review comprehensively synthesizes machine learning (ML) applications for predicting the cycle life of lithium-ion and sodium-ion batteries. It compares supervised, unsupervised, semi-supervised, and deep learning algorithms, highlighting their strengths and limitations for battery degradation modeling. The findings support optimized battery design, improved safety, and second-life applications, accelerating the transition to a low-carbon economy.
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
日本は自動車・蓄電池産業が強く、EVシフトや再生可能エネルギー導入拡大に向けてバッテリー寿命予測は極めて重要。本レビューは、日本企業がMLを活用したバッテリー管理システム（BMS）開発を加速するための知見を提供する。
In the global GX context
Globally, accurate battery cycle life prediction is critical for EV adoption and grid-scale renewable storage. This review provides a structured comparison of ML methods that can guide researchers and industry in developing better battery management systems, reducing waste, and enabling second-life applications.
👥 読者別の含意
🔬研究者:A comprehensive overview of ML approaches for battery life prediction, useful for identifying state-of-the-art methods and research gaps.
🏢実務担当者:Provides a roadmap for selecting ML algorithms to improve battery management systems, reduce testing costs, and optimize second-life use.
🏛政策担当者:Highlights the role of ML in enhancing battery reliability and sustainability, supporting policies for energy storage and EV infrastructure.
📄 Abstract（原文）
ABSTRACT The cycle life of rechargeable batteries, such as lithium‐ion and sodium‐ion systems, is a critical performance metric that determines their suitability for various applications in the context of a low‐carbon economy, such as electric vehicles and grid‐scale renewable energy storage. Accurate prediction of battery cycle life is vital for optimizing battery design, improving safety, and enabling effective battery management systems. Recent advances demonstrate that machine learning (ML) methods are extremely beneficial for extracting insights from experimental and simulated data to model and predict battery degradation. This review provides a comprehensive and up‐to‐date synthesis of ML applications for predicting the cycle life of lithium‐ion and sodium‐ion batteries. We also outline the core principles of widely used algorithms, including supervised, unsupervised, semi‐supervised, and deep learning methods, and discuss their relative strengths and limitations in this context, thereby accelerating the transition to a low‐carbon economy by reducing experimental waste, optimizing battery utilization, and enabling second‐life applications.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1002/cnl2.70180first seen 2026-07-01 05:06:23 · last seen 2026-07-01 05:06:24
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