Е-Публикации
Технически университет - София

Abstract: Evolutionary Computing and Genetic Algorithms have gained popularity as a Machine Learning technique for multi-objective optimization. Within this work we investigate their various application in data-driven battery modeling. Presented are specifics regarding the underlying mechanisms governing battery degradation and the main numerical health metrics. Discussed is the topic of physics-based constriction of Machine Learning models and methods for inference acceleration.

References

1. Lombardo, Teo, et al. "Artificial intelligence applied to battery research: Hype or reality?." Chemical reviews 122.12 (2021): 10899-10969.
2. Aykol, Muratahan, et al. "Perspective-combining physics and machine learning to predict battery lifetime." Journal of The Electrochemical Society 168.3 (2021): 030525.
3. Ali, Muhammad Umair, et al. "Kernel recursive least square tracker and long-short term memory ensemble based battery health prognostic model." Iscience 24.11 (2021).
4. Chen, Zheng, et al. "Online battery state of health estimation based on Genetic Algorithm for electric and hybrid vehicle applications." Journal of Power Sources 240 (2013): 184-192.
5. Chang, Chun, et al. "Lithium-ion battery state of health estimation using the incremental capacity and wavelet neural networks with genetic algorithm." Journal of Energy Storage 38 (2021): 102570.
6. Shah, Aryan, et al. "State of charge, remaining useful life and knee point estimation based on artificial intelligence and Machine learning in lithium-ion EV batteries: A comprehensive review." Renewable Energy Focus 42 (2022): 146-164.
7. Fan, Zhang, Xing Zi-xuan, and Wu Ming-hu. "State of health estimation for Li-ion battery using characteristic voltage intervals and genetic algorithm optimized back propagation neural network." Journal of Energy Storage 57 (2023): 106277.
8. Dou, Jiaming, et al. "Extreme learning machine model for state-of-charge estimation of lithium-ion battery using salp swarm algorithm." Journal of Energy Storage 52 (2022): 104996.
9. Elmahdi, F., Ismail, L., & Noureddine, M. (2021). Fitting the OCV-SOC relationship of a battery lithium-ion using genetic algorithm method. In E3S Web of Conferences (Vol. 234, p. 00097). EDP Sciences.
10. Zhang, Guanyong, et al. "Intelligent state of charge estimation of battery pack based on particle swarm optimization algorithm improved radical basis function neural network." Journal of Energy Storage 50 (2022): 104211.
11. Chen, Kui, et al. "Capacity degradation prediction of lithium-ion battery based on artificial bee colony and multi-kernel support vector regression." Journal of Energy Storage 72 (2023): 108160.
12. Kim, Jungsoo, et al. "Parameter identification of lithiumion battery pseudo-2-dimensional models using genetic algorithm and neural network cooperative optimization." Journal of Energy Storage 45 (2022): 103571.
13. Zhang, Liqiang, et al. "Multi-objective optimization of lithium-ion battery model using genetic algorithm approach." Journal of Power Sources 270 (2014): 367-378.
14. Stephen, Samantha S., et al. "Parameter estimation of valve regulated lead acid batteries using metaheuristic evolutionary algorithm." 2016 IEEE 59th International Midwest Symposium on Circuits and Systems (MWSCAS). IEEE, 2016.
15. Tamilselvi, S., and N. Karuppiah. "Capacity fade modeling of Li-ion battery using evolutionary algorithm." E3S Web of Conferences. Vol. 87. EDP Sciences, 2019.
16. Wang, Chuan, et al. "Cooperative co-evolutionary differential evolution algorithm applied for parameters identification of lithium-ion batteries." Expert Systems with Applications 200 (2022): 117192.
17. Malik, A., Zhang, Z., & Agarwal, R. K. (2014). Extraction of battery parameters using a multi-objective genetic algorithm with a non-linear circuit model. Journal of Power Sources, 259, 76-86.
18. Brand, J., Zhang, Z., & Agarwal, R. K. (2014). Extraction of battery parameters of the equivalent circuit model using a multi-objective genetic algorithm. Journal of Power Sources, 247, 729-737.
19. Bao, Q., Qin, W., & Yun, Z. (2023). A multi-stage adaptive method for remaining useful life prediction of lithium-ion batteries based on swarm intelligence optimization. Batteries, 9(4), 224.
20. Hu, Chao, et al. "Data-driven method based on particle swarm optimization and k-nearest neighbor regression for estimating capacity of lithium-ion battery." Applied Energy 129 (2014): 49-55.
21. Hu, X., Li, S. E., & Yang, Y. (2015). Advanced machine learning approach for lithium-ion battery state estimation in electric vehicles. IEEE Transactions on Transportation electrification, 2(2), 140-149.
22. Ariza Chacon, H. Eduardo, et al. "Modelling, parameter identification, and experimental validation of a lead acid battery bank using evolutionary algorithms." Energies 11.9 (2018): 2361.
23. Cao, Hongqing, et al. "Modeling and prediction for discharge lifetime of battery systems using hybrid evolutionary algorithms." Computers & chemistry 25.3 (2001): 251-259.
24. Thirugnanam, Kannan, et al. "Mathematical modeling of Liion battery using genetic algorithm approach for V2G applications." IEEE transactions on Energy conversion 29.2 (2014): 332-343.
25. Yang, G. (2014). Battery parameterisation based on differential evolution via a boundary evolution strategy. Journal of Power Sources, 245, 583-593.

Issue