From data to durability: Evaluating conventional and optimized machine learning techniques for battery health assessment

Alwabli, Abdullah

doi:10.1016/j.rineng.2024.102445

Results in Engineering

2024

DOI: 10.1016/j.rineng.2024.102445

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From data to durability: Evaluating conventional and optimized machine learning techniques for battery health assessment

Abdullah Alwabli

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2024

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Cited by 3 publications

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Paradigm shift for predictive maintenance and condition monitoring from Industry 4.0 to Industry 5.0: A systematic review, challenges and case study

Ahmed Murtaza,

Saher,

Hamza Zafar

et al. 2024

Results in Engineering

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Paradigm shift for predictive maintenance and condition monitoring from Industry 4.0 to Industry 5.0: A systematic review, challenges and case study

Ahmed Murtaza,

Saher,

Hamza Zafar

et al. 2024

Results in Engineering

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An efficient deep learning prognostic model for remaining useful life estimation of high speed CNC milling machine cutters

Elminir,

El-Brawany,

Ibrahim

et al. 2024

Results in Engineering

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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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From data to durability: Evaluating conventional and optimized machine learning techniques for battery health assessment

Cited by 3 publications

References 12 publications

Paradigm shift for predictive maintenance and condition monitoring from Industry 4.0 to Industry 5.0: A systematic review, challenges and case study

Paradigm shift for predictive maintenance and condition monitoring from Industry 4.0 to Industry 5.0: A systematic review, challenges and case study

An efficient deep learning prognostic model for remaining useful life estimation of high speed CNC milling machine cutters

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

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