A Cell Screening Algorithm Integrating Genetic and Numerical Differentiation
Download PDF
$currentUrl="http://$_SERVER[HTTP_HOST]$_SERVER[REQUEST_URI]"

Keywords

Genetic differentiation method
Battery consistency
Voltage fluctuation
Fast charging technology
Battery cell screening

DOI

10.26689/jera.v8i4.7938

Submitted : 2024-07-14
Accepted : 2024-07-29
Published : 2024-08-13

Abstract

The consistency of the cell has a significant impact on battery capacity, endurance, overall performance, safety, and service life extension. However, it is challenging to identify cells with high consistency and no loss of battery energy. This paper presents a cell screening algorithm that integrates genetic and numerical differentiation techniques. Initially, a mathematical model for battery consistency is established, and a multi-step charging strategy is proposed to satisfy the demands of fast charging technology. Subsequently, the genetic algorithm simulates biological evolution to efficiently search for superior cell combinations within a short time while evaluating capacity, voltage consistency, and charge/discharge efficiency. Finally, through experimental validation and comparative analysis with similar algorithms, our proposed method demonstrates notable advantages in terms of both search efficiency and performance.

References

Demir-Cakan R, Palacin MR, Croguennec L, 2019, Rechargeable Aqueous Electrolyte Batteries: From Univalent to Multivalent Cation Chemistry. Journal of Materials Chemistry A, 7(36): 20519–20539.

Wu F, Maier J, Yu Y, 2020, Guidelines and Trends for Next-Generation Rechargeable Lithium and Lithium-Ion Batteries. Chemical Society Reviews, 49(5): 1569–1614.

Dai H, Jiang B, Hu X, et al., 2021, Advanced Battery Management Strategies for a Sustainable Energy Future: Multilayer Design Concepts and Research Trends. Renewable and Sustainable Energy Reviews, 138: 110480.

Ronanki D, Kelkar A, Williamson SS, 2019, Extreme Fast Charging Technology—Prospects to Enhance Sustainable Electric Transportation. Energies, 12(19): 3721.

Zhang C, Jiang Y, Jiang J, et al., 2017, Study on Battery Pack Consistency Evolutions and Equilibrium Diagnosis for Series-Connected Lithium-Ion Batteries. Applied Energy, 207(C): 510–519.

Lyu C, Song Y, Wang L, et al., 2019, A New Method for Lithium-Ion Battery Uniformity Sorting Based on Internal Criteria. Journal of Energy Storage, 25: 100885.

Hong JC, Wang ZP, Liu P, 2017, Big Data-Based Thermal Runaway Prognosis of Battery Systems for Electric Vehicles. Energies, 7: 919.

Wang QS, Mao BB, Stoliarov SI, et al., 2019, A Review of Lithium-Ion Battery Failure Mechanisms and Fire Prevention Strategies. Progress in Energy and Combustion Science, 73: 95–131.

Yang N, Zhang X, Shang BB, et al., 2016, Unbalanced Discharging and Aging due to Temperature Differences Among the Cells in a Lithium-Ion Battery Pack with Parallel Combination. Journal of Power Sources, 306: 733–741.

Yoon T, Kim Y, Cho D, 2019, On Battery Cell Selection using a Decision Tree-Based Algorithm. Energies, 12(5): 863.

Hashemi SE, Gholian-Jouybari F, Hajiaghaei-Keshteli M, 2023, A Fuzzy C-Means Algorithm For Optimizing Data Clustering. Expert Systems with Applications, 227: 120377.

Ikotun AM, Ezugwu AE, Abualigah L, et al., 2023, K-Means Clustering Algorithms: A Comprehensive Review, Variants Analysis, and Advances in the Era of Big Data. Information Sciences, 622: 178210.

Ahmed M, Seraj R, Islam SMS, 2020, The K-Means Algorithm: A Comprehensive Survey and Performance Evaluation. Electronics, 9(8): 1295.

Sinaga KP, Yang MS, 2020, Unsupervised K-Means Clustering Algorithm. IEEE Access, 8: 80716–80727.

Fahim A, 2021, K and Starting Means for K-Means Algorithm. Journal of Computational Science, 55: 101445.

Barletta V, Caivano D, Nannavecchia A, et al., 2020, A Kohonen SOM Architecture for Intrusion Detection on In-Vehicle Communication Networks. Applied Sciences, 10: 5062.

Ren X, Liu S, Yu X, et al., 2021, A Method for State-of-Charge Estimation of Lithium-Ion Batteries based on PSO-LSTM. Energy, 234: 121236.

Hossain LMS, Miah MS, Ansari S, et al., 2022, Smart Battery Management Technology in Electric Vehicle Applications: Analytical and Technical Assessment Toward Emerging Future Directions. Batteries, 8(11): 219.

Khodapanah M, Ghanbari T, Moshksar E, et al., 2023, Partial Shading Detection and Hotspot Prediction in Photovoltaic Systems based on Numerical Differentiation and Integration of the P-V Curves. IET Renewable Power Generation, 17(2): 279–295.

Jiang Y, Jiang J, Zhang C, et al., 2019, A Copula-Based Battery Pack Consistency Modeling Method and its Application on the Energy Utilization Efficiency Estimation. Energy, 189: 116219.

Yu Y, Nduka OS, Nazir FU, et al., 2022, A Three-Stage Stochastic Framework for Smart Electric Vehicle Charging. IEEE Access, 11: 655–666.

Wang SL, Shi JY, Fernandez C, et al., 2019, An Improved Packing Equivalent Circuit Modeling Method with the Cell-to-Cell Consistency State Evaluation of the Internal Connected Lithium-Ion Batteries. Energy Science and Engineering, 7(2): 546–556.

Huang S, Wu Q, Liao W, et al., 2021, Adaptive Droop-Based Hierarchical Optimal Voltage Control Scheme for VSC-HVDC Connected Offshore Wind Farm. IEEE Transactions on Industrial Informatics, 17(12): 8165–8176.

Kim C, Batra R, Chen L, et al., 2021, Polymer Design using Genetic Algorithm and Machine Learning. Computational Materials Science, 186: 110067.

Hamdia KM, Zhuang X, Rabczuk T, 2021, An Efficient Optimization Approach for Designing Machine Learning Models based on Genetic Algorithm. Neural Computing and Applications, 33(6): 1923–1933.

Solodky SG, Stasyuk SA, 2022, Optimization of the Methods of Numerical Differentiation for Bivariate Functions. Ukrainian Mathematical Journal, 74(2): 289–313.

Zhao Z, 2021, A Hermite Extension Method for Numerical Differentiation. Applied Numerical Mathematics, 159: 46–60.