Referential Integrity Framework for Lithium Battery Characterization and State of Charge Estimation
Source abstract
Abstract
The global rise of electric vehicles (EVs) is reshaping the automotive industry, driven by a 25% increase in EV sales in 2024 and mounting regulatory pressure from European countries aiming to phase out thermal and hybrid vehicle production. In this context, the development of advanced battery technologies has become a critical priority. However, progress in electrochemical storage systems remains limited due to persistent technological barriers such as gaps in data, inadequate modeling tools, and difficulties in system integration, such as thermal management and interface instability. Safety concerns like thermal runaway and the lack of long-term performance data also hinder large-scale adoption. This study presents an in-depth analysis of lithium–ion (Li–ion) batteries, with a particular focus on evaluating their charging and discharging behaviors. To facilitate this, a series of automated experiments was conducted using a custom-built test bench equipped with MATLAB (2024b) programming and dSPACE data acquisition cards, enabling precise current and voltage measurements. The acquired data were analyzed to derive mathematical models that capture the operational characteristics of Li–ion batteries. Furthermore, various state-of-charge (SoC) estimation techniques were investigated to enhance battery efficiency and improve range management in EVs. This paper contributes to the advancement of energy storage technologies and supports global ecological goals by proposing safer and more efficient solutions for the electric mobility sector.
Source: crossref · Quality: baseline
Research context
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Institut FEMTO-ST, CNRS, Universite Marie et Louis Pasteur, 90000 Belfort, France
School of Business and Engineering, ESTA-UMLP, 90000 Belfort, France
Electrical Engineering Department, Faculty of Engineering at Shoubra, Benha University, Cairo 13511, Egypt
LGEB Laboratory, Department of Electrical Engineering, University Mohamed Khider, Biskra 07000, Algeria
Center for Environmental Research and Technology (CE-CERT), Bourns College of Engineering, University of California, Riverside (UCR), Riverside, CA 92507, USA
References (40)
Show source-provided references
1. A Comprehensive Review of State-of-Charge Estimation in lithium-ion batteries used in electric vehicles10.1016/j.est.2023.108777 ↗
2. A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems10.1016/j.rser.2020.110015 ↗
3. State-of-charge estimation of Lithium-ion batteries using an adaptive dual unscented Kalman filter based on a reduced-order model10.1016/j.est.2023.109011 ↗
4. State of charge estimation for lithium-ion batteries based on an adaptive fractional-order cubature Kalman filter with initial value compensation10.1016/j.est.2023.107544 ↗
5. Co-estimating the state of charge and health of lithium batteries through combining a minimalist electrochemical model and an equivalent circuit model10.1016/j.energy.2021.122815 ↗
6. State-of-charge estimation for lithium-ion batteries using a hybrid model combining equivalent circuit and electrochemical models
7. A novel fractional-order extended Kalman filtering method for on-line joint state estimation and parameter identification of high power Li-ion batteries10.20964/2021.05.64 ↗
8. State-of-charge estimation of lithium-ion batteries based on fractional-order modeling and adaptive square-root cubature Kalman filter10.1016/j.energy.2023.127007 ↗
9. A review of machine learning state-of-charge and state-of-health estimation algorithms for lithium-ion batteries10.1016/j.egyr.2023.01.108 ↗
10. Real-Time State-of-Charge Estimation for Lithium-Ion Batteries Using a Deep Neural Network
11. Zhang, Y., Wang, A., Zhang, C., He, P., Shao, K., Cheng, K., and Zhou, Y. (2025). State-of-Health Estimation for Lithium-Ion Batteries via Incremental Energy Analysis and Hybrid Deep Learning Model. Batteries, 11.10.3390/batteries11060217 ↗
12. Estimation of state of charge for lithium-ion batteries—A Review10.3934/energy.2019.2.186 ↗
13. State-of-Charge Estimation for Lithium-Ion Batteries Using a Neural Network-Based Observer
14. Adaptive particle filter for state estimation with application to non-linear system10.1049/sil2.12147 ↗
15. A Novel Adaptive Square Root UKF with Forgetting Factor for the Time-Variant Parameter Identification10.1155/2023/4160146 ↗
16. Adaptive sliding mode fault-tolerant fuzzy tracking control with application to unmanned marine vehicles10.1109/TSMC.2020.2964808 ↗
17. Optimized multi-hidden layer long short-term memory modeling and suboptimal fading extended Kalman filtering strategies for the synthetic state of charge estimation of lithium-ion batteries10.1016/j.apenergy.2023.120866 ↗
18. Insights and reviews on battery lifetime prediction from research to practice10.1016/j.jechem.2024.03.013 ↗
19. Jawaad, H.M., Saeed, M., Malik, A.N., Khalatbarisoltani, A., Elahi, H., and Khan, U.S. (2024, January 18–19). Physics-Informed Machine Learning for State of Charge Estimation of Lithium-ion Batteries. Proceedings of the 2024 International Conference on Robotics and Automation in Industry (ICRAI), Rawalpindi, Pakistan.10.1109/ICRAI62391.2024.10894277 ↗
20. Deep learning framework for lithium-ion battery state of charge estimation: Recent advances and future perspectives10.1016/j.ensm.2023.102883 ↗
21. Tao, J., Wang, S., Cao, W., Fernandez, C., and Blaabjerg, F. (2024). A Comprehensive Review of Multiple Physical and Data-Driven Model Fusion Methods for Accurate Lithium-Ion Battery Inner State Factor Estimation. Batteries, 10.10.3390/batteries10120442 ↗
22. A generic fusion framework integrating deep learning and Kalman filter for state of charge estimation of lithium-ion batteries: Analysis and comparison10.1016/j.jpowsour.2024.235493 ↗
23. Verma, P. (2024). An In-Depth Introduction to State of Health Estimation Methods of Li-Ion Batteries. Heat Transfer Enhancement Techniques: Thermal Performance 2025, Optimization and Applications, Wiley-Scrivener.10.1002/9781394270996.ch13 ↗
24. A comparative study on state-of-charge estimation for lithium-rich manganese-based battery based on Bayesian filtering and machine learning methods10.1016/j.energy.2024.132349 ↗
25. Analysis of real-time estimation method based on hidden Markov models for battery system states of health10.6113/JPE.2016.16.1.217 ↗
26. Fusion of Local and Global Features Based Explainable Diagnostic Method for Lithium-Ion Battery Degradation Modes10.1109/TTE.2025.3540409 ↗
27. Data-Driven Approaches for Estimation of EV Battery SoC and SoH: A Review10.1109/ACCESS.2025.3539528 ↗
28. A Data-Driven Reinforcement Learning Enabled Battery Fast Charging Optimization Using Real-World Experimental Data10.1109/TIE.2024.3398687 ↗
29. Ant Colony Optimized Extended Kalman Filter for State of Charge Estimation of Lithium-Ion Batteries
30. State of charge estimation combining physics-based and artificial intelligence models for Lithium-ion batteries10.1016/j.est.2023.108883 ↗
31. Mangunkusumo, K.G.H., Lian, K.L., Wijaya, F.D., Chang, Y.R., Lee, Y.D., and Ho, Y.H. (2014, January 7–8). Quantum neural network for state of charge estimation. Proceedings of the 2014 6th International Conference on Information Technology and Electrical Engineering (ICITEE), Yogyakarta, Indonesia.
32. FPGA-based design of advanced BMS implementing SoC/SoH estimators10.1016/j.microrel.2018.03.015 ↗
33. A federated transfer learning approach for lithium-ion battery lifespan early prediction considering privacy preservation10.1016/j.est.2024.114153 ↗
34. Privacy-aware energy consumption modeling of connected battery electric vehicles using federated learning10.1109/TTE.2023.3343106 ↗
35. Online lithium battery SOC estimation based on adversarial domain adaptation under a small sample dilemma10.1007/s43236-023-00747-4 ↗
36. Hybrid State of Charge Estimation of Lithium-Ion Batteries Using a Physics-Informed Neural Network
37. Plett, G.L. (2015). Battery Management Systems, Volume I: Battery Modeling, Artech House.
38. A Hybrid Approach to SoC Estimation for Lithium-Ion Batteries Based on an Adaptive EKF and ANN
39. State-of-Charge Estimation of the Lithium-Ion Battery Using an Adaptive Extended Kalman Filter Based on an Improved Thevenin Model
40. Critical review of state of health estimation methods of Li-ion batteries for real applications10.1016/j.rser.2015.11.042 ↗