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CC BY V4.0, Open Access
Journal article
A Machine Learning-based Real-Time Remaining Useful Life Estimation and Fair Pricing Strategy for Electric Vehicle Battery Swapping Stations
IEEE access, Vol.13, pp.62555-62566
2025
Abstract
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 UniversitySeda Kul - Karamanoğlu Mehmetbey UniversityA. Ozgur Polat - Karamanoğlu Mehmetbey UniversityHamed Zeinoddini-Meymand - Graduate University of Advanced TechnologyFarhad 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
- Copyright
- © 2025 The Authors.
- Murdoch Affiliation
- School of Engineering and Energy
- Language
- English
- Resource Type
- Journal article
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- Domestic collaboration
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- 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