Novel informed deep learning-based prognostics framework for on-board health monitoring of lithium-ion batteries

Kim, Sung Wook; Oh, Ki‐Yong; Lee, Seung−Chul

doi:10.1016/j.apenergy.2022.119011

Cited by 30 publications

(4 citation statements)

References 65 publications

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“…The use of BNN for battery diagnostics/prognostics has been few in number and largely lacked rigorous analysis of its Bayesian uncertainty quantification. For example, Kim et al 137 proposed a knowledge-infused BNN for onboard SOH estimation and RUL prediction of Li-ion batteries in EVs. Their approach incorporated novel domain knowledge by (a) designing impedance-related features based on discharge voltage slopes that have been observed to be correlated with degradation, and (b) introducing into an RNN a knowledge-infused block that uses an empirical double-exponential model for degradation estimation.…”

Section: Soh Estimationmentioning

confidence: 99%

Probabilistic machine learning for battery health diagnostics and prognostics—review and perspectives

Thelen,

Huan,

Paulson

et al. 2024

npj Mater. Sustain.

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Diagnosing lithium-ion battery health and predicting future degradation is essential for driving design improvements in the laboratory and ensuring safe and reliable operation over a product’s expected lifetime. However, accurate battery health diagnostics and prognostics is challenging due to the unavoidable influence of cell-to-cell manufacturing variability and time-varying operating circumstances experienced in the field. Machine learning approaches informed by simulation, experiment, and field data show enormous promise to predict the evolution of battery health with use; however, until recently, the research community has focused on deterministic modeling methods, largely ignoring the cell-to-cell performance and aging variability inherent to all batteries. To truly make informed decisions regarding battery design in the lab or control strategies for the field, it is critical to characterize the uncertainty in a model’s predictions. After providing an overview of lithium-ion battery degradation, this paper reviews the current state-of-the-art probabilistic machine learning models for health diagnostics and prognostics. Details of the various methods, their advantages, and limitations are discussed in detail with a primary focus on probabilistic machine learning and uncertainty quantification. Last, future trends and opportunities for research and development are discussed.

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Section: Soh Estimationmentioning

confidence: 99%

Probabilistic machine learning for battery health diagnostics and prognostics—review and perspectives

Thelen,

Huan,

Paulson

et al. 2024

npj Mater. Sustain.

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“…2) Integration with physics-informed DL models: Relying solely on battery tests or simulation data in a completely data-driven manner to learn the LIB behaviors can be inefficient with the current test setup limitations and the high uncertainties existing in real-world driving profiles [128]. Ensuring the robustness of DL models against uncertainties such as sensor noise, operational variations, and environmental changes remains a challenging task in BPHM [129]. Physics-informed neural networks combine physical laws with data-driven insights to provide comprehensive and reliable BPHM [130].…”

Section: A Research Challengesmentioning

confidence: 99%

Advancing Lithium-Ion Battery Health Prognostics With Deep Learning: A Review and Case Study

Massaoudi,

Abu-Rub,

Ghrayeb

2024

IEEE Open J. Ind. Applicat.

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Lithium-ion battery prognostics and health management (BPHM) systems are vital to the longevity, economy, and environmental friendliness of electric vehicles and energy storage systems. Recent advancements in deep learning (DL) techniques have shown promising results in addressing the challenges faced by the battery research and innovation community. This review paper analyzes the mainstream developments in BPHM using DL techniques. The fundamental concepts of BPHM are discussed, followed by a detailed examination of the emerging DL techniques. A case study using a data-driven DLinear model for state of health estimation is introduced, achieving accurate forecasts with minimal data and high computational efficiency. Finally, the potential future pathways for research and development in BPHM are explored. This review offers a holistic understanding of emerging DL techniques in BPHM and provides valuable insights and guidance for future research endeavors.INDEX TERMS Deep learning, health and life-cycle analysis, Lithium-ion battery management system, prognostics and health management, remaining useful life prediction, state of charge estimation. Nomenclature

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“…Unlike the previous ML models that are explained via averaged feature importance after the prediction of the responses, [105,107] quantify the importance of the features during the training phase of the model as well. In [105], a continuous evolution of the relative feature importance with respect to the training epochs of an empirical knowledgeinfused neural network on 124 cells shows a considerable fluctuation across the range until the final epoch block. However, all feature importance values converge to a steady value at the final epochs of training.…”

Section: State Of Health Estimationmentioning

confidence: 99%

A Review of the Applications of Explainable Machine Learning for Lithium–Ion Batteries: From Production to State and Performance Estimation

Faraji Niri,

Aslansefat,

Haghi

et al. 2023

Energies

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Lithium–ion batteries play a crucial role in clean transportation systems including EVs, aircraft, and electric micromobilities. The design of battery cells and their production process are as important as their characterisation, monitoring, and control techniques for improved energy delivery and sustainability of the industry. In recent decades, the data-driven approaches for addressing all mentioned aspects have developed massively with promising outcomes, especially through artificial intelligence and machine learning. This paper addresses the latest developments in explainable machine learning known as XML and its application to lithium–ion batteries. It includes a critical review of the XML in the manufacturing and production phase, and then later, when the battery is in use, for its state estimation and control. The former focuses on the XML for optimising the battery structure, characteristics, and manufacturing processes, while the latter considers the monitoring aspect related to the states of health, charge, and energy. This paper, through a comprehensive review of theoretical aspects of available techniques and discussing various case studies, is an attempt to inform the stack-holders of the area about the state-of-the-art XML methods and encourage those to move from the ML to XML in transition to a NetZero future. This work has also highlighted the research gaps and potential future research directions for the battery community.

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Novel informed deep learning-based prognostics framework for on-board health monitoring of lithium-ion batteries

Cited by 30 publications

References 65 publications

Probabilistic machine learning for battery health diagnostics and prognostics—review and perspectives

Probabilistic machine learning for battery health diagnostics and prognostics—review and perspectives

Advancing Lithium-Ion Battery Health Prognostics With Deep Learning: A Review and Case Study

A Review of the Applications of Explainable Machine Learning for Lithium–Ion Batteries: From Production to State and Performance Estimation

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