Citation :

Neeraja T, Kalpalatha Reddy T, "An Integrated Explainable Machine Learning Framework for Cell-to-Pack Imbalance Prediction in Electric Vehicle Battery Systems," International Journal of Electrical and Electronics Engineering, vol. 13, no. 10, pp. 83-105, 2026. Crossref, https://doi.org/10.14445/23488379/IJEEE-V13I10P107

Abstract

This study presents a machine learning framework for predicting cell-to-pack imbalance in electric vehicle batteries. A set of battery attributes that affect battery performance, including State of Charge (SOC), State of Health (SOH), temperature (including ambient temperature and humidity), and cabin temperature, were used to make this prediction. The collected battery data were pre-processed to improve prediction performance. Three preprocessing methods were used: (i) Variational Mode Decomposition (VMD); (ii) deep denoising autoencoders, and (iii) feature selection using minimum Redundancy Maximum Relevance (mRMR) mutual information. This preprocessing step removes noise from the dataset and identifies useful information for prediction. Cell-to-pack imbalance prediction was performed using three interpretable machine learning models: (i) an Explainable Boosting Machine (EBM); (ii) fuzzy regression; and (iii) RuleFit. These models provide transparent predictions and help in understanding the factors responsible for battery imbalance. Models were evaluated on an independent 20-sample test set held out from the 100-record dataset. Among the three interpretable models, Fuzzy Regression achieved the strongest predictive performance (R² = 0.998), followed by RuleFit (R² = 0.976), while EBM achieved a lower R² of 0.201 on this independent test set despite providing detailed feature-level explanations. These results indicate that combining signal preprocessing, feature selection, and interpretable regression can provide useful and explainable information for cell-to-pack imbalance prediction, while also showing that performance can vary substantially between interpretable models on a small, two-pack dataset; validation on larger and more varied battery datasets is needed to confirm generalizability.

Keywords

Battery Management System, Cell-to-Pack balancing, Deep Denoising Autoencoder, Electric Vehicles, Explainable Machine Learning, Feature selection, Interpretable models, Variational Mode Decomposition.

References

1. Mohammad Waseem et al., “Battery Technologies and Functionality of Battery Management System for EVs: Current Status, Key Challenges, and Future Prospectives,” Journal of Power Sources, vol. 580, pp. 1-17, 2023.
   [CrossRef] [Google Scholar] [Publisher Link]
2. A.K.M. Ahasan Habib et al., “Lithium-Ion Battery Management System for Electric Vehicles: Constraints, Challenges, and Recommendations,” Batteries, vol. 9, no. 3, pp. 1-24, 2023.
   [CrossRef] [Google Scholar] [Publisher Link]
3. Long Zhou et al., “State Estimation Models of Lithium-Ion Batteries for Battery Management System: Status, Challenges, and Future Trends,” Batteries, vol. 9, no. 2, pp. 1-23, 2023.
   [CrossRef] [Google Scholar] [Publisher Link]
4. Wei Liu, Tobias Placke, and Kwok Tong Chau, “Overview of Batteries and Battery Management for Electric Vehicles,” Energy Reports, vol. 8, pp. 4058-4084, 2022.
   [CrossRef] [Google Scholar] [Publisher Link]
5. Gopal Krishna et al., “Advanced Battery Management System Enhancement using IoT and ML for Predicting Remaining useful Life in Li-Ion Batteries,” Scientific Reports, vol. 14, no. 1, pp. 1-18, 2024.
   [CrossRef] [Google Scholar] [Publisher Link]
6. Shaik Nyamathulla, and Chittathuru Dhanamjayulu, “A Review of Battery Energy Storage Systems and Advanced Battery Management System for Different Applications: Challenges and Recommendations,” Journal of Energy Storage, vol. 86, pp. 1-27, 2024.
   [CrossRef] [Google Scholar] [Publisher Link]
7. Jinpeng Tian et al., “Deep Learning Framework for Lithium-Ion Battery State of Charge Estimation: Recent Advances and Future Perspectives,” Energy Storage Materials, vol. 61, pp. 1-17, 2023.
   [CrossRef] [Google Scholar] [Publisher Link]
8. Fujin Wang et al., “Explainability-Driven Model Improvement for SOH Estimation of Lithium-Ion Battery,” Reliability Engineering and System Safety, vol. 232, pp. 1-12, 2023.
   [CrossRef] [Google Scholar] [Publisher Link]
9. Yang Ge, Fusheng Zhang, and Yong Ren, “Lithium Ion Battery Health Prediction Via Variable Mode Decomposition and Deep Learning Network with Self-Attention Mechanism,” Frontiers in Energy Research, vol. 10, pp. 1-10, 2022.
   [CrossRef] [Google Scholar] [Publisher Link]
10. Riya Sharma et al., “State of Health Estimation for Li-Ion Battery using Machine Learning,” 2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, Milano, Italy, pp. 406-411, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
11. Nan Xu et al., “A Data-Driven Approach to State of Health Estimation and Prediction for a Lithium-Ion Battery Pack of Electric Buses based on Real-World Data,” Sensors, vol. 22, no. 15, pp. 1-22, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
12. P. Reshma, and V. Joshi Manohar, “Collaborative Evaluation of Soc, Sop and Soh of Lithium-Ion Battery in an Electric Bus through Improved Remora Optimization Algorithm and Dual Adaptive Kalman Filtering Algorithm,” Journal of Energy Storage, vol. 68, pp. 1-16, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
13. Qianqian Wu, Baolin Ma, and Lulu Guo, “Cabin Temperature and Humidity Comfort Control for Electric Vehicles in High Temperature and Humidity Environment,” 2024 China Automation Congress, Qingdao, China, pp. 6143-6148, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
14. Qingzhi Lai et al., “New Data Optimization Framework for Parameter Estimation under Uncertainties with Application to Lithium-Ion Battery,” Applied Energy, vol. 295, pp. 1-8, 2021.
    [CrossRef] [Google Scholar] [Publisher Link]
15. Vedhanayaki Selvaraj et al., “Implementation of Discrete Wavelet Combined Bayesian-Optimized Gaussian Process Regression-based SOC Prediction of EV Battery,” IEEE Access, vol. 12, pp. 184052-184070, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
16. MD Shoriat Ullah, and Kangwon Seo, “Prediction of Lithium-Ion Battery Capacity by Functional Principal Component Analysis of Monitoring Data,” Applied Sciences, vol. 12, no. 9, pp. 1-10, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
17. Kaiquan Li, Yujie Wang, and Zonghai Chen, “A Comparative Study of Battery State-of-Health Estimation based on Empirical Mode Decomposition and Neural Network,” Journal of Energy Storage, vol. 54, pp. 1-12, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
18. Yuan Chen et al., “Battery Life Prediction based on a Hybrid Support Vector Regression Model,” Frontiers in Energy Research, vol. 10, pp. 1-13, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
19. Kailong Liu et al., “Feature Analyses and Modeling of Lithium-Ion Battery Manufacturing based on Random Forest Classification,” IEEE/ASME Transactions on Mechatronics, vol. 26, no. 6, pp. 2944-2955, 2021.
    [CrossRef] [Google Scholar] [Publisher Link]
20. Benvolence Chinomona et al., “Long Short-Term Memory Approach to Estimate Battery Remaining useful Life using Partial Data,” IEEE Access, vol. 8, pp. 165419-165431, 2020.
    [CrossRef] [Google Scholar] [Publisher Link]
21. Zhongwei Deng et al., “Data-Driven State of Charge Estimation for Lithium-Ion Battery Packs based on Gaussian Process Regression,” Energy, vol. 205, pp. 1-11, 2020.
    [CrossRef] [Google Scholar] [Publisher Link]
22. M. Venkata Sai Pavan et al., “Optimizing Battery Performance-Active and Passive Cell Balancing,” 2024 Third International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, Krishnankoil, Virudhunagar district, Tamil Nadu, India, pp. 1-6, 2024. 
    [CrossRef] [Google Scholar] [Publisher Link]
23. Chunsheng Wang, Mengke Cao, and Yuan Cao, “Battery Aging Estimation Algorithm with Active Balancing Control in Battery System,” Journal of Energy Storage, vol. 108, pp. 1-11, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
24. Yuqin Weng, and Cristinel Ababei, “AI-Assisted Reconfiguration of Battery Packs for Cell Balancing to Extend Driving Runtime,” Journal of Energy Storage, vol. 84, pp. 1-10, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
25. Changfu Zou, Chris Manzie, and Dragan Nešić, “Model Predictive Control for Lithium-Ion Battery Optimal Charging,” IEEE/ASME Transactions on Mechatronics, vol. 23, no. 2, pp. 947-957, 2018.
    [CrossRef] [Google Scholar] [Publisher Link]
26. Ziya, EV Battery Charging Data, Kaggle, 2024. [Online]. Available: https://www.kaggle.com/datasets/ziya07/ev-battery-charging-data
27. Thiruvonasundari Duraisamy, and Deepa Kaliyaperumal, “Machine Learning-based Optimal Cell Balancing Mechanism for Electric Vehicle Battery Management System,” IEEE Access, vol. 9, pp. 132846-132861, 2021. 
    [CrossRef] [Google Scholar] [Publisher Link]
28. Haifeng Dai et al., “Advanced Battery Management Strategies for a Sustainable Energy Future: Multilayer Design Concepts and Research Trends,” Renewable and Sustainable Energy Reviews, vol. 138, pp. 1-25, 2021.
    [CrossRef] [Google Scholar] [Publisher Link]
29. Carlos Vidal et al., “Machine Learning Applied to Electrified Vehicle Battery State of Charge and State of Health Estimation: State-of-the-Art,” IEEE Access, vol. 8, pp. 52796-52814, 2020.
    [CrossRef] [Google Scholar] [Publisher Link]
30. Trishna Raj, “Path Dependent Battery Degradation Dataset Part 1,” University of Oxford, 2020.
    [Google Scholar] [Publisher Link]
31. Kristen A. Severson et al., “Data-Driven Prediction of Battery Cycle Life Before Capacity Degradation,” Nature Energy, vol. 4, no. 5, pp. 383-391, 2019.
    [CrossRef] [Google Scholar] [Publisher Link]
32. Peiyao Guo, Ze Cheng, and Lei Yang, “A Data-Driven Remaining Capacity Estimation Approach for Lithium-Ion Batteries based on Charging Health Feature Extraction,” Journal of Power Sources, vol. 412, pp. 442-450, 2019.
    [CrossRef] [Google Scholar] [Publisher Link]
33. Yuxuan Deng, Changqing Du, and Zhong Ren, “A Novel Method for Estimating the State of Health of Lithium-Ion Batteries based on Physics-Informed Neural Network,” Batteries, vol. 11, no. 2, pp. 1-18, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
34. Taesuk Mun et al., “1D Convolutional Neural Network Combined with Long-Short Term Memory-based Cell-to-Cell Inconsistency Estimation Considering Recursive Least Squares and Euclidean Distance,” Journal of Energy Storage, vol. 72, pp. 1-17, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
35. Alexander Reiter et al., “Electrical Cell-to-Cell Variations within Large-Scale Battery Systems-A Novel Characterization and Modeling Approach,” Journal of Energy Storage, vol. 57, pp. 1-13, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
36. Mona Faraji Niri et al., “A Review of the Applications of Explainable Machine Learning for Lithium-Ion Batteries: From Production to State and Performance Estimation,” Energies, vol. 16, no. 17, pp. 1-38, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
37. Adam Thelen et al., “Probabilistic Machine Learning for Battery Health Diagnostics and Prognostics-Review and Perspectives,” Materials Sustainability, vol. 2, no. 1, pp. 1-33, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
38. Arunesh Kumar Singh et al., “Applications of Artificial Intelligence and Cell Balancing Techniques for Battery Management System (BMS) in Electric Vehicles: A Comprehensive Review,” Process Safety and Environmental Protection, vol. 191, pp. 2247-2265, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
39. Prognostics Center of Excellence Data Set Repository, NASA, 2022. [Online]. Available: https://www.nasa.gov/intelligent-systems-division/discovery-and-systems-health/pcoe/pcoe-data-set-repository/