A Machine Learning-based Real-Time Remaining Useful Life Estimation and Fair Pricing Strategy for Electric Vehicle Battery Swapping Stations

Seyit Alperen Celtek; Seda Kul; A. Ozgur Polat; Hamed Zeinoddini-Meymand; Farhad Shahnia

doi:10.1109/ACCESS.2025.3554682

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A Machine Learning-based Real-Time Remaining Useful Life Estimation and Fair Pricing Strategy for Electric Vehicle Battery Swapping Stations

Journal article

Open access

Peer reviewed

A Machine Learning-based Real-Time Remaining Useful Life Estimation and Fair Pricing Strategy for Electric Vehicle Battery Swapping Stations

Seyit Alperen Celtek, Seda Kul, A. Ozgur Polat, Hamed Zeinoddini-Meymand and Farhad Shahnia

IEEE access, Vol.13, pp.62555-62566

2025

DOI: https://doi.org/10.1109/ACCESS.2025.3554682

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Abstract

Accuracy

Analytical models

Batteries

Battery Swapping Station

Computational modeling

Electric Vehicle

Electric vehicles

Estimation

Machine Learning

Mathematical models

Predictive models

Pricing

Real-time systems

Remaining Useful Life

XGBoost

The increasing adoption of electric vehicles (EVs) has led to the widespread implementation of battery swapping stations. However, ensuring fairness in battery pricing remains a significant challenge since variations in battery health and performance among swapped batteries can result in user dissatisfaction and operational inefficiencies. This paper introduces a novel approach to enhance fairness in battery swapping by integrating a machine learning-based real-time prediction model with a pricing strategy based on remaining useful life (RUL) estimation to address this issue. The proposed solution comprises a real-time RUL estimation system and a dynamic pricing mechanism that ensures fair pricing based on battery health and performance. This integrated approach aims to improve user satisfaction and the operational efficiency of swapping stations. The paper evaluates various machine learning algorithms for real-time RUL estimation regarding accuracy, computation time, and memory usage. The results suggest that XGBoost provides the most suitable balance between accuracy and efficiency, making it an effective solution for real-world applications. Comparative analysis shows that the XGBoost model outperforms the second-best method (Random Forest) with a lower error (3.50 vs 3.79) while maintaining competitive computational efficiency (9.75 vs 8.52 seconds) and memory usage (2.12 vs 2.32 MB) when solving a typical numerical case study problem. The proposed approach has the potential to accelerate the adoption of electric vehicles and contribute to sustainability goals by promoting efficient battery utilization and fair pricing mechanisms.

Details

Title: A Machine Learning-based Real-Time Remaining Useful Life Estimation and Fair Pricing Strategy for Electric Vehicle Battery Swapping Stations
Authors/Creators: Seyit Alperen Celtek - Karamanoğlu Mehmetbey University
Seda Kul - Karamanoğlu Mehmetbey University
A. Ozgur Polat - Karamanoğlu Mehmetbey University
Hamed Zeinoddini-Meymand - Graduate University of Advanced Technology
Farhad Shahnia - Murdoch University, Centre for Water, Energy and Waste
Publication Details: IEEE access, Vol.13, pp.62555-62566
Publisher: IEEE; PISCATAWAY
Number of pages: 1
Identifiers: 991005760733507891
Murdoch Affiliation: School of Engineering and Energy
Language: English
Resource Type: Journal article

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Collaboration types: Domestic collaboration; International collaboration
Citation topics: 2 Chemistry; 2.62 Electrochemistry; 2.62.138 Lithium-Ion Battery
Web Of Science research areas: Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications
ESI research areas: Engineering