A New Interpretable State-of-Health Assessment Model for Lithium-Ion Batteries With Multidimensional Adaptability Optimization

IF 3.5 3区 工程技术 Q3 ENERGY & FUELS Energy Science & Engineering Pub Date : 2024-11-28 DOI:10.1002/ese3.1982
Zongjun Zhang, Yuanyuan Qu, Yuxi Liu, Mengqi Li, Hongyu Li, Wei He
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Abstract

The state-of-health (SOH) assessment of lithium-ion batteries is critical to the development and optimization of maintenance strategies. To ensure the accuracy of the assessment results, it must not only address a variety of uncertainties but also rationalize and transparently conduct the assessment process, as well as make the results interpretable and traceable. These requirements are necessary to ensure that the battery operates safely and steadily. As an interpretable modeling method, belief rule base (BRB) has been widely used in lithium-ion battery SOH assessment. However, current BRB-based models face two problems: (1) The initial reference values provided by experts often have limited accuracy due to complex internal chemistry. (2) The multidimensionality of the parameters in the interpretable optimization process and the differences in their properties should be fully considered. Therefore, this paper proposes a new SOH assessment model for lithium-ion batteries based on an interpretable BRB with multidimensional adaptability optimization (IBRB-mao). First, an interpretable knowledge and data dual-driven reference value generation method is proposed to address the issue of imprecise reference values. Expert knowledge is maintained when generating reference values using this method. Second, two interpretable multidimensional constraint strategies are proposed to ensure interpretability in the optimization process. Finally, the NASA lithium-ion battery data set is taken as a case study to validate the effectiveness of the proposed method.

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锂离子电池的健康状况(SOH)评估对于维护策略的制定和优化至关重要。为确保评估结果的准确性,不仅要解决各种不确定因素,还要合理、透明地开展评估过程,并使评估结果具有可解释性和可追溯性。这些要求是确保电池安全稳定运行的必要条件。作为一种可解释的建模方法,信念规则库(BRB)已被广泛应用于锂离子电池 SOH 评估。然而,目前基于信念规则库的模型面临两个问题:(1)由于内部化学成分复杂,专家提供的初始参考值通常准确性有限。(2) 在可解释的优化过程中,应充分考虑参数的多维性及其属性差异。因此,本文提出了一种基于多维适应性优化的可解释 BRB(IBRB-mao)的新型锂离子电池 SOH 评估模型。首先,本文提出了一种可解释的知识和数据双驱动参考值生成方法,以解决参考值不精确的问题。使用这种方法生成参考值时,专家知识得以保留。其次,提出了两种可解释的多维约束策略,以确保优化过程中的可解释性。最后,以 NASA 锂离子电池数据集为案例,验证了所提方法的有效性。
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来源期刊
Energy Science & Engineering
Energy Science & Engineering Engineering-Safety, Risk, Reliability and Quality
CiteScore
6.80
自引率
7.90%
发文量
298
审稿时长
11 weeks
期刊介绍: Energy Science & Engineering is a peer reviewed, open access journal dedicated to fundamental and applied research on energy and supply and use. Published as a co-operative venture of Wiley and SCI (Society of Chemical Industry), the journal offers authors a fast route to publication and the ability to share their research with the widest possible audience of scientists, professionals and other interested people across the globe. Securing an affordable and low carbon energy supply is a critical challenge of the 21st century and the solutions will require collaboration between scientists and engineers worldwide. This new journal aims to facilitate collaboration and spark innovation in energy research and development. Due to the importance of this topic to society and economic development the journal will give priority to quality research papers that are accessible to a broad readership and discuss sustainable, state-of-the art approaches to shaping the future of energy. This multidisciplinary journal will appeal to all researchers and professionals working in any area of energy in academia, industry or government, including scientists, engineers, consultants, policy-makers, government officials, economists and corporate organisations.
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