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June 9, 2026International Journal of Power Electronics

Fault identification-based state of charge estimation in lithium ion batteries using Phi-hy-ResNet and SR-KF-ArRsi-ANN

Puja Suresh Deokate Neeta Doshi

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Key Points

This research aims to enhance state of charge estimation in lithium ion batteries by identifying faults effectively.
Utilized phi-hy-resnet for deep learning approaches to model battery behavior.
Applied SR-KF-ArRsi-ANN for advanced artificial neural network analysis.
Evaluated the performance efficacy under realistic operational conditions.
Achieved enhanced fault identification accuracy with a reduction in estimation error by 25%.
Demonstrated a 30% improvement in battery lifespan predictability using the new methods.

Implication

Evaluates state of charge estimation in lithium ion batteries, suggesting improved accuracy using advanced algorithms.

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Cite This Study

Deokate et al. (2026) studied this question.

synapsesocial.com/papers/6a2a50e080c8f91e7f39d43e https://doi.org/10.1504/ijpelec.2027.10079065

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