Citation

Ibrahim, Md and Ansari, Shaheer and Ayob, Afida and Lipu, M. S. Hossain and Abdolrasol, Maher G. M. and Khawaja, Abdul Waheed and Khalil, Muhammad Amir and Stroe, Daniel Ioan (2025) Hybrid optimized remaining useful life prediction framework for lithium-ion batteries with limited data samples. Scientific Reports, 15 (1). ISSN 2045-2322

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Abstract

This study introduces a Jellyfish optimization technique integrated with a Multi-Layer Perceptron, specifically a Feedforward Neural Network (FNN) model, for remaining useful life (RUL) prediction of lithium-ion batteries (LIBs). A multiple battery with multi-input (MBMI) profile is utilized to create 91-dimensional data features for model training. A systematic sampling approach is employed to extract relevant data features. Results show that the proposed JFO-based FNN model outperforms the traditional FNN model’s accuracy. The Mean Square Error (MSE) is used as the objective function to determine optimal model hyperparameters. The research utilizes the NASA LIB database, which includes four datasets. For LIB cell B5, the proposed model achieved an MSE of 3.9494*10− 4. The model’s accuracy and efficiency are further validated using particle swarm optimization. However, the LIBs B6 and B18 showed higher error results due to capacity regeneration issues. The MIT-Stanford LIB datasets demonstrated high applicability when validating the JFO-FNN model’s outcomes. The novelty of this work lies in using a JFO-optimized FNN model trained on systematically sampled, multi-battery LIB datasets to improve predictive accuracy, generalization, and robustness. Overall, the developed RUL prediction framework appears to be fast, effective, and yields promising results.

Item Type:	Article
Uncontrolled Keywords:	Data-driven, lithium-ion battery, optimization, remaining useful life
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics > TK7871 Electronics--Materials
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Nurin Syazwani Azmi
Date Deposited:	03 Dec 2025 07:59
Last Modified:	03 Dec 2025 08:01
URII:	http://shdl.mmu.edu.my/id/eprint/14945

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