Generalized real-time state of health estimation for lithium-ion batteries using simulation-augmented multi-objective dual-stream fusion of multi-Bi-LSTM-attention

Generalized real-time state of health estimation for lithium-ion batteries using simulation-augmented multi-objective dual-stream fusion of multi-Bi-LSTM-attention

To maintain the safe and reliable operation of lithium-ion batteries and manage their timely replacement, accurate state of health (SOH) estimation is critically important. This paper presents a novel deep-learning framework based on multi-loss optimized dual stream fusion of attention integrated mu...

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Bibliographic Details
Main Authors: Jarin Tasnim, Md. Azizur Rahman, Md. Shoaib Akhter Rafi, Muhammad Anisuzzaman Talukder, Md. Kamrul Hasan
Format: Article
Language:English
Published: Elsevier 2025-03-01
Series:e-Prime: Advances in Electrical Engineering, Electronics and Energy
Subjects:
Lithium-ion batteries
State of health
Energy discrepancy aware preprocessing
Overlapped data splitting
Simulation model
Attention guided multi-Bi-LSTM
Online Access:http://www.sciencedirect.com/science/article/pii/S2772671124004479
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