Variability in initial battery cell characteristics and its implications for manufacturing quality control
Corresponding Author(s) : Coskun Firat
Future Energy,
Vol. 4 No. 3 (2025): August 2025 Issue
Abstract
Ensuring manufacturing consistency in lithium-ion batteries is critical for reliable performance, safety, and longevity. This study examines the variability in initial pouch cell characteristics, including voltage, current, charge capacity, and discharge capacity, across 192 samples from 24 batches. Statistical analysis reveals that voltage remains relatively stable (mean = 3.951V, CV ≈ 6.45%), while charge and discharge capacities exhibit moderate variability (mean = 2.286Ah, CV ≈ 47.99% and mean = 2.350Ah, CV ≈ 52.53%, respectively). Current demonstrates the highest variability, with a mean of 0.280A and a CV of 195.25%, suggesting significant fluctuations possibly due to non-constant current and also likely influenced by process inconsistencies, operational conditions, or measurement sensitivity. Box plot and control chart analyses link many outliers to specific production factors, such as raw material lot changes and equipment maintenance cycles, pinpointing electrode preparation and formation as critical stages for mitigating variability. By integrating statistical insights with practical manufacturing considerations, this work provides a framework for proactive quality control, ultimately supporting scalable and high-quality lithium-ion battery production. While this study focuses on pouch cells, the underlying principles of variability analysis and targeted process improvements remain broadly relevant to other battery formats.
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- C. Comanescu, (2025). Ensuring Safety and Reliability: An Overview of Lithium-Ion Battery Service Assessment, Batteries, 11, 6. doi.org/10.3390/batteries11010006
- Y. Liu, R. Zhang, J. Wang, and Y. Wang, (2021). Current and future lithium-ion battery manufacturing, iScience, 24, 102332. doi.org/10.1016/j.isci.2021.102332
- X. Cui, A. Garg, N. T. Thao, N. T. Trung, (2020). Machine learning approach for solving inconsistency problems of Li-ion batteries during the manufacturing stage, Int. J Energy Res., 44, 9194–9204. doi.org/10.1002/er.5574
- Y. Han, H. Yuan, J. Li, J. Du, Y. Hu, and X. Huang, (2021). Study on Influencing Factors of Consistency in Manufacturing Process of Vehicle Lithium-Ion Battery Based on Correlation Coefficient and Multivariate Linear Regression Model, Adv. Theory Simul., 4, 2100070. doi.org/10.1002/adts.202100070
- L. Kong, R. Aalund, M. Alipour, S. I. Stoliarov, and M. Pecht, (2022). Evaluating the Manufacturing Quality of Lithium Ion Pouch Batteries, J. Electrochem. Soc., 169, 040541. doi.org/10.1149/1945-7111/ac6539
- J. Tian, Y. Wang, C. Liu, Z. Chen, (2020). Consistency evaluation and cluster analysis for lithium-ion battery pack in electric vehicles, Energy, 194, 116944. doi.org/10.1016/j.energy.2020.116944
- B. Y. Liaw, F. Q. Wang, Y.M. Wei, (2018). Managing Safety Risk by Cell Manufacturers, Idaho National Laboratory, and NL/JOU-17-41515-Revision-1.
- J. Omakor, MD. S. Miah, H. Chaoui, (2024). Battery Reliability Assessment in Electric Vehicles: A State of the Art, IEEE Access, 12, 77903. doi.org/10.1109/ACCESS.2024.3406424
- B. Duan, Z. Li, P. Gu, Z. Zhou, C. Zhang, (2018). Evaluation of battery inconsistency based on information entropy, J. of Energy Storage, 16, 60–166. doi.org/10.1016/j.est.2018.01.010
- A. Ö. Aydin, F. Zajonz, T. Günther, K. B. Dermenci, M. Berecibar and L. Urrutia, (2023). Lithium-Ion Battery Manufacturing: Industrial View on Processing Challenges, Possible Solutions and Recent Advances, Batteries, 9, 555. doi.org/10.3390/batteries9110555
- P. S Grant, D. Greenwood, K. Pardikar et al, (2022). Roadmap on Li-ion battery manufacturing research, J. Phys. Energy, 4, 042006. doi.org/10.1088/2515-7655/ac8e30
- D. Beck, P. Dechent, M. Junker, D. U. Sauer, & M. Dubarry, (2021). Inhomogeneities and Cell-to-Cell Variations in Lithium-Ion Batteries, a Review. Energies, 14:11, 3276. doi.org/10.3390/en14113276
- J. Zhang, J. Lee, (2011). A review on prognostics and health monitoring of Li-ion battery, Journal of Power Sources, 196:15, 6007-6014, doi.org/10.1016/j.jpowsour.2011.03.101.
- J. Zhao, X. Feng, M.-K. Tran, M. Fowler, M. Ouyang, A. F. Burke, (2024). Battery safety: Fault diagnosis from laboratory to real world, J. of Power Sources, 598, 234111, doi.org/10.1016/j.jpowsour.2024.234111.
- D. Deng, (2015). Li-ion batteries: basics, progress, and challenges, Energy Sci Eng, 3, 385-418. doi.org/10.1002/ese3.95
- CALCE, (2025). Battery Accelerated Cycle Life Testing Data, https://calce.umd.edu/battery-accelerated-cycle-life-testing-data, Accessed on 25.03.2025.
- C. Zeng, J. Liang, C. Cui, T. Zhai, H. Li, (2022). Dynamic Investigation of Battery Materials via Advanced Visualization: From Particle, Electrode to Cell Level, Adv. Mater., 34, 2200777. doi.org/10.1002/adma.202200777
- J. Li, J. Fleetwood, W. B. Hawley, W. Kays, (2021). From Materials to Cell: State-of-the-Art and Prospective Technologies for Lithium-Ion Battery Electrode Processing, Chemical Reviews, 122:1, 903-956. doi.org/10.1021/acs.chemrev.1c00565
- D. Shin, M. Poncino, E. Macii and N. Chang, (2015). A Statistical Model-Based Cell-to-Cell Variability Management of Li-ion Battery Pack, in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 34:2, 252-265, doi.org/10.1109/TCAD.2014.2384506.
- C. D. Reynolds, S. D. Hare, P. R. Slater, M. J. H. Simmons, and E. Kendrick, (2022). Rheology and Structure of Lithium-Ion Battery Electrode Slurries, Energy Technol., 10, 2200545. doi.org/10.1002/ente.202200545
- T.-J. Liu, C. Tiu, L.-C. Chen and D. Liu, (2018). The Influence of Slurry Rheology on Lithium-ion Electrode Processing. In Printed Batteries (Eds S. Lanceros-Méndez and C.M. Costa). doi.org/10.1002/9781119287902.ch3
- K. Zhang, D. Li, X. Wang, J. Gao, H. Shen, H. Zhang, C. Rong, & Z. Chen, (2024). Dry Electrode Processing Technology and Binders, Materials, 17:10, 2349. doi.org/10.3390/ma17102349
- Z. Deng, Z. Huang, Y. Shen, Y. Huang, H. Ding, A. Luscombe, M. Johnson, J. E. Harlow, R. Gauthier, J. R. Dahn, (2020). Ultrasonic Scanning to Observe Wetting and “Unwetting” in Li-Ion Pouch Cells, Joule, 4:9, 2017-2029. doi.org/10.1016/j.joule.2020.07.014
- A. Turetskyy, J. Wessel, C. Herrmann, S. Thiede, (2021). Battery production design using multi-output machine learning models, Energy Storage Materials, 38, 93-112, doi.org/10.1016/j.ensm.2021.03.002.
- E. Braco, I. S. Martín, A. Berrueta, P. Sanchis and A. Ursúa, (2021). Experimental Assessment of First- and Second-Life Electric Vehicle Batteries: Performance, Capacity Dispersion, and Aging, in IEEE Transactions on Industry Applications, 57:4, 4107-4117, doi.org/10.1109/TIA.2021.3075180.
- O. Demirci, S. Taskin, E. Schaltz, B. A. Demirci, (2024). Review of battery state estimation methods for electric vehicles-Part II: SOH estimation, Journal of Energy Storage, 96, 112703, doi.org/10.1016/j.est.2024.112703
- N. Kaden, R. Schlimbach, Á. Rohde García, & K. Dröder, (2023). A Systematic Literature Analysis on Electrolyte Filling and Wetting in Lithium-Ion Battery Production, Batteries, 9:3, 164. doi.org/10.3390/batteries9030164
- E. Martinez-Laserna, E.Martinez-Laserna, E. Sarasketa-Zabala, I. V. Sarria; D.-I. Stroe, M. Swierczynski, A. Warnecke, (2018). Technical Viability of Battery Second Life: A Study from the Ageing Perspective, in IEEE Transactions on Industry Applications, 54:3, 2703-2713, doi.org/10.1109/TIA.2018.2801262
- N. Yao, L. Yu, Dr. Z.-H. Fu, Dr. X. Shen, Dr. T.-Z. Hou, Dr. X. Liu, Y.-C. Gao, Dr. R. Zhang, Dr. C.-Z. Zhao, Dr. X. Chen, Prof. Q. Zhang, (2023). Probing the Origin of Viscosity of Liquid Electrolytes for Lithium Batteries, Angewandte Chemie, 135:41, e202305331, doi.org/10.1002/ange.202305331
References
C. Comanescu, (2025). Ensuring Safety and Reliability: An Overview of Lithium-Ion Battery Service Assessment, Batteries, 11, 6. doi.org/10.3390/batteries11010006
Y. Liu, R. Zhang, J. Wang, and Y. Wang, (2021). Current and future lithium-ion battery manufacturing, iScience, 24, 102332. doi.org/10.1016/j.isci.2021.102332
X. Cui, A. Garg, N. T. Thao, N. T. Trung, (2020). Machine learning approach for solving inconsistency problems of Li-ion batteries during the manufacturing stage, Int. J Energy Res., 44, 9194–9204. doi.org/10.1002/er.5574
Y. Han, H. Yuan, J. Li, J. Du, Y. Hu, and X. Huang, (2021). Study on Influencing Factors of Consistency in Manufacturing Process of Vehicle Lithium-Ion Battery Based on Correlation Coefficient and Multivariate Linear Regression Model, Adv. Theory Simul., 4, 2100070. doi.org/10.1002/adts.202100070
L. Kong, R. Aalund, M. Alipour, S. I. Stoliarov, and M. Pecht, (2022). Evaluating the Manufacturing Quality of Lithium Ion Pouch Batteries, J. Electrochem. Soc., 169, 040541. doi.org/10.1149/1945-7111/ac6539
J. Tian, Y. Wang, C. Liu, Z. Chen, (2020). Consistency evaluation and cluster analysis for lithium-ion battery pack in electric vehicles, Energy, 194, 116944. doi.org/10.1016/j.energy.2020.116944
B. Y. Liaw, F. Q. Wang, Y.M. Wei, (2018). Managing Safety Risk by Cell Manufacturers, Idaho National Laboratory, and NL/JOU-17-41515-Revision-1.
J. Omakor, MD. S. Miah, H. Chaoui, (2024). Battery Reliability Assessment in Electric Vehicles: A State of the Art, IEEE Access, 12, 77903. doi.org/10.1109/ACCESS.2024.3406424
B. Duan, Z. Li, P. Gu, Z. Zhou, C. Zhang, (2018). Evaluation of battery inconsistency based on information entropy, J. of Energy Storage, 16, 60–166. doi.org/10.1016/j.est.2018.01.010
A. Ö. Aydin, F. Zajonz, T. Günther, K. B. Dermenci, M. Berecibar and L. Urrutia, (2023). Lithium-Ion Battery Manufacturing: Industrial View on Processing Challenges, Possible Solutions and Recent Advances, Batteries, 9, 555. doi.org/10.3390/batteries9110555
P. S Grant, D. Greenwood, K. Pardikar et al, (2022). Roadmap on Li-ion battery manufacturing research, J. Phys. Energy, 4, 042006. doi.org/10.1088/2515-7655/ac8e30
D. Beck, P. Dechent, M. Junker, D. U. Sauer, & M. Dubarry, (2021). Inhomogeneities and Cell-to-Cell Variations in Lithium-Ion Batteries, a Review. Energies, 14:11, 3276. doi.org/10.3390/en14113276
J. Zhang, J. Lee, (2011). A review on prognostics and health monitoring of Li-ion battery, Journal of Power Sources, 196:15, 6007-6014, doi.org/10.1016/j.jpowsour.2011.03.101.
J. Zhao, X. Feng, M.-K. Tran, M. Fowler, M. Ouyang, A. F. Burke, (2024). Battery safety: Fault diagnosis from laboratory to real world, J. of Power Sources, 598, 234111, doi.org/10.1016/j.jpowsour.2024.234111.
D. Deng, (2015). Li-ion batteries: basics, progress, and challenges, Energy Sci Eng, 3, 385-418. doi.org/10.1002/ese3.95
CALCE, (2025). Battery Accelerated Cycle Life Testing Data, https://calce.umd.edu/battery-accelerated-cycle-life-testing-data, Accessed on 25.03.2025.
C. Zeng, J. Liang, C. Cui, T. Zhai, H. Li, (2022). Dynamic Investigation of Battery Materials via Advanced Visualization: From Particle, Electrode to Cell Level, Adv. Mater., 34, 2200777. doi.org/10.1002/adma.202200777
J. Li, J. Fleetwood, W. B. Hawley, W. Kays, (2021). From Materials to Cell: State-of-the-Art and Prospective Technologies for Lithium-Ion Battery Electrode Processing, Chemical Reviews, 122:1, 903-956. doi.org/10.1021/acs.chemrev.1c00565
D. Shin, M. Poncino, E. Macii and N. Chang, (2015). A Statistical Model-Based Cell-to-Cell Variability Management of Li-ion Battery Pack, in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 34:2, 252-265, doi.org/10.1109/TCAD.2014.2384506.
C. D. Reynolds, S. D. Hare, P. R. Slater, M. J. H. Simmons, and E. Kendrick, (2022). Rheology and Structure of Lithium-Ion Battery Electrode Slurries, Energy Technol., 10, 2200545. doi.org/10.1002/ente.202200545
T.-J. Liu, C. Tiu, L.-C. Chen and D. Liu, (2018). The Influence of Slurry Rheology on Lithium-ion Electrode Processing. In Printed Batteries (Eds S. Lanceros-Méndez and C.M. Costa). doi.org/10.1002/9781119287902.ch3
K. Zhang, D. Li, X. Wang, J. Gao, H. Shen, H. Zhang, C. Rong, & Z. Chen, (2024). Dry Electrode Processing Technology and Binders, Materials, 17:10, 2349. doi.org/10.3390/ma17102349
Z. Deng, Z. Huang, Y. Shen, Y. Huang, H. Ding, A. Luscombe, M. Johnson, J. E. Harlow, R. Gauthier, J. R. Dahn, (2020). Ultrasonic Scanning to Observe Wetting and “Unwetting” in Li-Ion Pouch Cells, Joule, 4:9, 2017-2029. doi.org/10.1016/j.joule.2020.07.014
A. Turetskyy, J. Wessel, C. Herrmann, S. Thiede, (2021). Battery production design using multi-output machine learning models, Energy Storage Materials, 38, 93-112, doi.org/10.1016/j.ensm.2021.03.002.
E. Braco, I. S. Martín, A. Berrueta, P. Sanchis and A. Ursúa, (2021). Experimental Assessment of First- and Second-Life Electric Vehicle Batteries: Performance, Capacity Dispersion, and Aging, in IEEE Transactions on Industry Applications, 57:4, 4107-4117, doi.org/10.1109/TIA.2021.3075180.
O. Demirci, S. Taskin, E. Schaltz, B. A. Demirci, (2024). Review of battery state estimation methods for electric vehicles-Part II: SOH estimation, Journal of Energy Storage, 96, 112703, doi.org/10.1016/j.est.2024.112703
N. Kaden, R. Schlimbach, Á. Rohde García, & K. Dröder, (2023). A Systematic Literature Analysis on Electrolyte Filling and Wetting in Lithium-Ion Battery Production, Batteries, 9:3, 164. doi.org/10.3390/batteries9030164
E. Martinez-Laserna, E.Martinez-Laserna, E. Sarasketa-Zabala, I. V. Sarria; D.-I. Stroe, M. Swierczynski, A. Warnecke, (2018). Technical Viability of Battery Second Life: A Study from the Ageing Perspective, in IEEE Transactions on Industry Applications, 54:3, 2703-2713, doi.org/10.1109/TIA.2018.2801262
N. Yao, L. Yu, Dr. Z.-H. Fu, Dr. X. Shen, Dr. T.-Z. Hou, Dr. X. Liu, Y.-C. Gao, Dr. R. Zhang, Dr. C.-Z. Zhao, Dr. X. Chen, Prof. Q. Zhang, (2023). Probing the Origin of Viscosity of Liquid Electrolytes for Lithium Batteries, Angewandte Chemie, 135:41, e202305331, doi.org/10.1002/ange.202305331