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Volume 16
Issue 11
10.3390/wevj16110639
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

A Particle Swarm Optimized Multi-Model Framework for Remaining Useful Life Prediction of Lithium-Ion Batteries Using Domain-Driven Feature Engineering

by
Farrukh Hafeez
1,*,
Zeeshan Ahmad Arfeen
2,
Gohar Ali
3,*,
Muhammad I. Masud
4,
Muhammad Hamid
5,
Mohammed Aman
6,
Muhammad Salman Saeed
7 and
Touqeer Ahmed
8
1
Department of Electrical Engineering, Jubail Industrial College, Al Jubail 35718, Saudi Arabia
2
Electrical Engineering Department, The Islamia University of Bahawalpur (IUB), Bahawalpur 63100, Pakistan
3
Department of Mechanical Engineering, Al-Fayha College, Al Jubail 35514, Saudi Arabia
4
Department of Electrical Engineering, College of Engineering, University of Business and Technology, Jeddah 21361, Saudi Arabia
5
Engineering Technology Department, Community College of Qatar, Doha P.O. Box 7344, Qatar
6
Department of Industrial Engineering, College of Engineering, University of Business and Technology, Jeddah 21361, Saudi Arabia
7
Multan Electric Power Company, Multan 6000, Pakistan
8
College of Engineering, A’Sharqiyah University, Ibra 400, Oman
*
Authors to whom correspondence should be addressed.
World Electr. Veh. J. 2025, 16(11), 639; https://doi.org/10.3390/wevj16110639 (registering DOI)
Submission received: 16 October 2025 / Revised: 13 November 2025 / Accepted: 18 November 2025 / Published: 20 November 2025
(This article belongs to the Special Issue Material Synthesis, Manufacturing and Electrochemical Modelling for Lithium-Ion Batteries in Electric Vehicle)
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Abstract

With respect to battery management and safe operation and maintenance scheduling of electric vehicles (EVs), it is very important to predict the remaining useful life (RUL) of lithium-ion batteries (LIBs). Accurate prediction of RUL can bring secure working conditions, avert internal and external failure, and, last, avoid any undesirable consequences. However, achieving accurate prediction of RUL is complicated for EV applications due to various reasons such as the complex operational characteristics, dynamic changes in the model parameters during the aging process, extraction of battery parameters, data preparation, and hyper-parameter tuning of the predictive model. This research proposes a novel approach that integrates Particle Swarm Optimization (PSO) with a multi-model technique for RUL prediction. The framework integrates many machine learning (ML) models and deep learning (DL) models. Combining domain knowledge, advanced optimization techniques, and learning models to make high-accuracy RUL predictions reduces maintenance costs and improves battery management systems. This study uses domain-driven feature engineering to extract battery-specific indicators, including voltage drops, charging time, and temperature fluctuations, to increase model accuracy. Among the evaluated models, LSTM demonstrates superior performance, achieving a mean absolute error (MAE) of 0.34, a root mean square error (RMSE) of 0.76, and an R2 of 0.93, providing the best results in RUL prediction. The proposed research uniquely integrates PSO-based optimization with domain-driven feature engineering across multiple machine learning and deep learning models, demonstrating a unified and novel approach that significantly improves the prediction accuracy of RUL in LIBs.
Keywords: deep learning; feature engineering; lithium-ion batteries; machine learning; particle swarm optimization; remaining useful life deep learning; feature engineering; lithium-ion batteries; machine learning; particle swarm optimization; remaining useful life
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MDPI and ACS Style

Hafeez, F.; Arfeen, Z.A.; Ali, G.; Masud, M.I.; Hamid, M.; Aman, M.; Saeed, M.S.; Ahmed, T. A Particle Swarm Optimized Multi-Model Framework for Remaining Useful Life Prediction of Lithium-Ion Batteries Using Domain-Driven Feature Engineering. World Electr. Veh. J. 2025, 16, 639. https://doi.org/10.3390/wevj16110639

AMA Style

Hafeez F, Arfeen ZA, Ali G, Masud MI, Hamid M, Aman M, Saeed MS, Ahmed T. A Particle Swarm Optimized Multi-Model Framework for Remaining Useful Life Prediction of Lithium-Ion Batteries Using Domain-Driven Feature Engineering. World Electric Vehicle Journal. 2025; 16(11):639. https://doi.org/10.3390/wevj16110639

Chicago/Turabian Style

Hafeez, Farrukh, Zeeshan Ahmad Arfeen, Gohar Ali, Muhammad I. Masud, Muhammad Hamid, Mohammed Aman, Muhammad Salman Saeed, and Touqeer Ahmed. 2025. "A Particle Swarm Optimized Multi-Model Framework for Remaining Useful Life Prediction of Lithium-Ion Batteries Using Domain-Driven Feature Engineering" World Electric Vehicle Journal 16, no. 11: 639. https://doi.org/10.3390/wevj16110639

APA Style

Hafeez, F., Arfeen, Z. A., Ali, G., Masud, M. I., Hamid, M., Aman, M., Saeed, M. S., & Ahmed, T. (2025). A Particle Swarm Optimized Multi-Model Framework for Remaining Useful Life Prediction of Lithium-Ion Batteries Using Domain-Driven Feature Engineering. World Electric Vehicle Journal, 16(11), 639. https://doi.org/10.3390/wevj16110639

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