Artificial intelligence-driven real-world battery diagnostics

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Energy and AI Pub Date : 2024-08-29 DOI:10.1016/j.egyai.2024.100419
Jingyuan Zhao , Xudong Qu , Yuyan Wu , Michael Fowler , Andrew F. Burke
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Abstract

Addressing real-world challenges in battery diagnostics, particularly under incomplete or inconsistent boundary conditions, has proven difficult with traditional methodologies such as first-principles and atomistic calculations. Despite advances in data assimilation techniques, the overwhelming volume and diversity of data, coupled with the lack of universally accepted models, underscore the limitations of these traditional approaches. Recently, deep learning has emerged as a highly effective tool in overcoming persistent issues in battery diagnostics by adeptly managing expansive design spaces and discerning intricate, multidimensional correlations. This approach resolves challenges previously deemed insurmountable, especially with lost, irregular, or noisy data through the design of specialized network architectures that adhere to physical invariants. However, gaps remain between academic advancements and their practical applications, including challenges in explainability and the computational costs associated with AI-driven solutions. Emerging technologies such as explainable artificial intelligence (XAI), AI for IT operations (AIOps), lifelong machine learning to mitigate catastrophic forgetting, and cloud-based digital twins open new opportunities for intelligent battery life-cycle assessment. In this perspective, we outline these challenges and opportunities, emphasizing the potential of innovative technologies to transform battery diagnostics, as demonstrated by our recent practice and the progress made in the field. This includes promising achievements in both academic and industry field demonstrations in modeling and forecasting the dynamics of multiphysics and multiscale battery systems. These systems feature inhomogeneous cascades of scales, informed by our physical, electrochemical, observational, empirical, and/or mathematical understanding of the battery system. Through data assimilation efforts, meticulous craftsmanship, and elaborate implementations—and by considering the wealth and spatio-temporal heterogeneity of available data—such AI-based intelligent learning philosophies have great potential to achieve better accuracy, faster training, and improved generalization.

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人工智能驱动的真实世界电池诊断技术
事实证明,采用第一原理和原子计算等传统方法很难解决电池诊断中的实际难题,尤其是在不完整或不一致的边界条件下。尽管数据同化技术不断进步,但数据的巨大数量和多样性,加上缺乏普遍接受的模型,凸显了这些传统方法的局限性。最近,深度学习已成为克服电池诊断领域长期存在问题的一种非常有效的工具,它能巧妙地管理广阔的设计空间并辨别错综复杂的多维相关性。这种方法通过设计符合物理不变性的专用网络架构,解决了以前认为无法解决的难题,尤其是在数据丢失、不规则或嘈杂的情况下。然而,学术进步与实际应用之间仍存在差距,包括人工智能驱动的解决方案在可解释性和计算成本方面面临的挑战。可解释人工智能(XAI)、IT 运营人工智能(AIOps)、减轻灾难性遗忘的终身机器学习以及基于云的数字双胞胎等新兴技术为智能电池生命周期评估带来了新的机遇。在本视角中,我们将概述这些挑战和机遇,强调创新技术改变电池诊断的潜力,我们最近的实践和该领域取得的进展都证明了这一点。这包括在多物理场和多尺度电池系统动态建模和预测方面,学术界和工业界的现场演示都取得了可喜的成就。这些系统的特点是不同尺度的非均质级联,我们通过对电池系统的物理、电化学、观测、经验和/或数学理解来了解这些尺度。通过数据同化工作、精雕细琢和精心实施,并考虑到可用数据的丰富性和时空异质性,这些基于人工智能的智能学习理念在实现更高精度、更快训练和更好的泛化方面具有巨大潜力。
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来源期刊
Energy and AI
Energy and AI Engineering-Engineering (miscellaneous)
CiteScore
16.50
自引率
0.00%
发文量
64
审稿时长
56 days
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