Unlocking battery insights with interpretable machine learning

IF 35.4 1区材料科学 Q1 CHEMISTRY, PHYSICAL

Joule Pub Date : 2025-09-17 DOI:10.1016/j.joule.2025.102131

Runyang Lian , Weihan Li

引用次数: 0

Abstract

Interpretable machine learning combining data-driven models with physically meaningful features can improve battery health diagnostics. Recent studies in Joule and Energy & Environmental Science show how these approaches enhance transparency across battery production, testing, and application. This preview highlights these developments and their potential to advance understanding of battery processes.

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通过可解释的机器学习解锁电池洞察

可解释的机器学习将数据驱动模型与物理上有意义的特征相结合，可以改善电池健康诊断。《焦耳》和《能源与环境科学》杂志最近的研究表明，这些方法如何提高电池生产、测试和应用的透明度。这个预览强调了这些发展和他们的潜力，以促进对电池过程的理解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Joule Energy-General Energy

CiteScore

53.10

自引率

2.00%

发文量

198

期刊介绍： Joule is a sister journal to Cell that focuses on research, analysis, and ideas related to sustainable energy. It aims to address the global challenge of the need for more sustainable energy solutions. Joule is a forward-looking journal that bridges disciplines and scales of energy research. It connects researchers and analysts working on scientific, technical, economic, policy, and social challenges related to sustainable energy. The journal covers a wide range of energy research, from fundamental laboratory studies on energy conversion and storage to global-level analysis. Joule aims to highlight and amplify the implications, challenges, and opportunities of novel energy research for different groups in the field.