Lithium battery state of health estimation using real-world vehicle data and an interpretable hybrid framework

Wen, Shuang; Lin, Ni; Huang, Shengxu; Li, Xuan; Wang, Zhenpo; Zhang, Zhaosheng

doi:10.1016/j.est.2024.112623

Journal of Energy Storage

2024

DOI: 10.1016/j.est.2024.112623

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Lithium battery state of health estimation using real-world vehicle data and an interpretable hybrid framework

Shuang Wen,

Ni Lin,

Shengxu Huang

et al.

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2024

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Cited by 3 publications

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

Zhang,

Qu,

Liu

et al. 2024

Energy Science & Engineering

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

Zhang,

Qu,

Liu

et al. 2024

Energy Science & Engineering

View full text Add to dashboard Cite

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Lithium-Ion Battery Health Assessment Method Based on Double Optimization Belief Rule Base with Interpretability

Si,

Shen,

2024

Batteries

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Health assessment is necessary to ensure that lithium-ion batteries operate safely and dependably. Nonetheless, there are the following two common problems with the health assessment models for lithium-ion batteries that are currently in use: inability to comprehend the assessment results and the uncertainty around the chemical reactions occurring inside the battery. A rule-based modeling strategy that can handle ambiguous data in health state evaluation is the belief rule base (BRB). In existing BRB studies, experts often provide parameters such as the initial belief degree, but the parameters may not match the current data. In addition, random global optimization methods may undermine the interpretability of expert knowledge. Therefore, this paper proposes a lithium-ion battery health assessment method based on the double optimization belief rule base with interpretability (DO-BRB-I). First, the belief degree is optimized according to the data distribution. Then, to increase accuracy, belief degrees and other parameters are further optimized using the projection covariance matrix adaptive evolution strategy (P-CMA-ES). At the same time, four interpretability constraint strategies are suggested based on the features of lithium-ion batteries to preserve interpretability throughout the optimization process. Finally, to confirm the efficacy of the suggested approach, a sample of the health status assessment of the B0006 lithium-ion battery is provided.

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Electric Vehicle Battery Technologies and Capacity Prediction: A Comprehensive Literature Review of Trends and Influencing Factors

Sang,

Duong,

Zhou

et al. 2024

Batteries

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Electric vehicle (EV) battery technology is at the forefront of the shift towards sustainable transportation. However, maximising the environmental and economic benefits of electric vehicles depends on advances in battery life cycle management. This comprehensive review analyses trends, techniques, and challenges across EV battery development, capacity prediction, and recycling, drawing on a dataset of over 22,000 articles from four major databases. Using Dynamic Topic Modelling (DTM), this study identifies key innovations and evolving research themes in battery-related technologies, capacity degradation factors, and recycling methods. The literature is structured into two primary themes: (1) “Electric Vehicle Battery Technologies, Development & Trends” and (2) “Capacity Prediction and Influencing Factors”. DTM revealed pivotal findings: advancements in lithium-ion and solid-state batteries for higher energy density, improvements in recycling technologies to reduce environmental impact, and the efficacy of machine learning-based models for real-time capacity prediction. Gaps persist in scaling sustainable recycling methods, developing cost-effective manufacturing processes, and creating standards for life cycle impact assessment. Future directions emphasise multidisciplinary research on new battery chemistries, efficient end-of-life management, and policy frameworks that support circular economy practices. This review serves as a resource for stakeholders to address the critical technological and regulatory challenges that will shape the sustainable future of electric vehicles.

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Lithium battery state of health estimation using real-world vehicle data and an interpretable hybrid framework

Cited by 3 publications

References 47 publications

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

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

Lithium-Ion Battery Health Assessment Method Based on Double Optimization Belief Rule Base with Interpretability

Electric Vehicle Battery Technologies and Capacity Prediction: A Comprehensive Literature Review of Trends and Influencing Factors

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