RUL Prediction of Lithium-ion Batteries using a Federated and Homomorphically Encrypted Learning Method
Authors: 
Víctor López, Oscar Fontenla-Romero, Elena Hernández-Pereira, Bertha Guijarro-Berdiñas, Carlos Blanco-Seijo, Samuel Fernandez-PazAuthors Info & Claims
Pages 565 - 571
Abstract
The increasing demand for lithium-ion batteries (LIB) across various industries has accentuated the importance of accurately predicting the Remaining Useful Life (RUL) of these energy storage devices. This article introduces a novel approach to RUL prediction by leveraging a federated learning (FL) and homomorphic encryption (HE) model, called FedHEONN. Traditional RUL prediction models often face challenges not only related to the accuracy and reliability of estimations but also to data privacy and security when dealing with sensitive information in Internet of Things (IoT) environments. In response, our approach employs FL, allowing multiple distributed nodes to collaboratively train a predictive model without sharing private data. This ensures data privacy and security while harnessing the collective knowledge from diverse edge computing devices. Furthermore, to address the issue of secure computation over encrypted data, FedHEONN has the capacity to incorporate HE into the learning process. This enables the model to operate directly on encrypted data, providing an additional layer of protection to that of the federated model itself.
Our experimental results test the efficacy of this FL method in accurately predicting the RUL of LIB real data against benchmark models, including linear regression with regularisation techniques such as Lasso, Ridge and Elastic-net, and non-linear models such as multilayer perceptron and support vector machine for regression. The experiment demonstrates the preservation of prediction outcomes, upholding an additional level of data safety through encrypted transmission of model information.
References
[1]
Li, M., Lu, J., Chen, Z., and Amine, K. (2018). 30 years of lithium-ion batteries. Advanced Materials, 30(33), 1800561.
[2]
Nzereogu, P. U., Omah, A. D., Ezema, F. I., Iwuoha, E. I., and Nwanya, A. C. (2022). Anode materials for lithium-ion batteries: A review. Applied Surface Science Advances, 9, 100233.
[3]
Lu, L., Han, X., Li, J., Hua, J., and Ouyang, M. (2013). A review on the key issues for lithium-ion battery management in electric vehicles. Journal of power sources, 226, 272--288.
[4]
Diouf, B., and Pode, R. (2015). Potential of lithium-ion batteries in renewable energy. Renewable Energy, 76, 375--380.
[5]
Yousaf, M., Naseer, U., Li, Y., Ali, Z., Mahmood, N., Wang, L., and Guo, S. (2021). A mechanistic study of electrode materials for rechargeable batteries beyond lithium ions by in situ transmission electron microscopy. Energy & Environmental Science, 14(5), 2670--2707.
[6]
Liu, H., Wei, Z., He, W., and Zhao, J. (2017). Thermal issues about Li-ion batteries and recent progress in battery thermal management systems: A review. Energy conversion and management, 150, 304--330.
[7]
Lin, Q., Wang, J., Xiong, R., Shen, W., and He, H. (2019). Towards a smarter battery management system: A critical review on optimal charging methods of lithium ion batteries. Energy, 183, 220--234.
[8]
Zhou, Y., Huang, M., Chen, Y., and Tao, Y. (2016). A novel health indicator for on-line lithium-ion batteries remaining useful life prediction. Journal of Power Sources, 321, 1--10.
[9]
Lipu, M. H., Hannan, M. A., Hussain, A., Hoque, M. M., Ker, P. J., Saad, M. H. M., and Ayob, A. (2018). A review of state of health and remaining useful life estimation methods for lithium-ion battery in electric vehicles: Challenges and recommendations. Journal of cleaner production, 205, 115--133.
[10]
Li, T., Sahu, A. K., Talwalkar, A., and Smith, V. (2020). Federated learning: Challenges, methods, and future directions. IEEE signal processing magazine, 37(3), 50--60.
[11]
Rieke, N., Hancox, J., Li, W., Milletari, F., Roth, H. R., Albarqouni, S., and Cardoso, M. J. (2020). The future of digital health with federated learning. NPJ digital medicine, 3(1), 119.
[12]
Zhang, C., Xie, Y., Bai, H., Yu, B., Li, W., and Gao, Y. (2021). A survey on federated learning. Knowledge-Based Systems, 216, 106775.
[13]
Fontenla-Romero, O., Guijarro-Berdiñas, B., Hernández-Pereira, E., and Pérez-Sánchez, B. (2023). FedHEONN: Federated and homomorphically encrypted learning method for one-layer neural networks. Future Generation Computer Systems, 149, 200--211.
[14]
Manthiram, A., Yu, X., and Wang, S. (2017). Lithium battery chemistries enabled by solid-state electrolytes. Nature Reviews Materials, 2(4), 1--16.
[15]
Lin, Y., Wang, X., Liu, J., and Miller, J. D. (2017). Natural halloysite nano-clay electrolyte for advanced all-solid-state lithium-sulfur batteries. Nano Energy, 31, 478--485.
[16]
Wang, Y., Gao, Q., Wang, G., Lu, P., Zhao, M., and Bao, W. (2018). A review on research status and key technologies of battery thermal management and its enhanced safety. International Journal of Energy Research, 42(13), 4008--4033.
[17]
Friansa, K., Haq, I. N., Santi, B. M., Kurniadi, D., Leksono, E., and Yuliarto, B. (2017). Development of battery monitoring system in smart microgrid based on internet of things (IoT). Procedia engineering, 170, 482--487.
[18]
Asaleye, D. A., Breen, M., and Murphy, M. D. (2017). A decision support tool for building integrated renewable energy microgrids connected to a smart grid. Energies, 10(11), 1765.
[19]
Yao, L. P., Zeng, Q., Qi, T., and Li, J. (2020). An environmentally friendly discharge technology to pretreat spent lithium-ion batteries. Journal of Cleaner Production, 245, 118820.
[20]
Yoon, Y. H. (2021). A Framework For the Software Security Analysis of Mobile-power Systems (Doctoral dissertation, Purdue University).
[21]
Samanta, A., and Williamson, S. S. (2021). A survey of wireless battery management system: Topology, emerging trends, and challenges. Electronics, 10(18), 2193.
[22]
Din, E., Schaef, C., Moffat, K., and Stauth, J. T. (2016). A scalable active battery management system with embedded real-time electrochemical impedance spectroscopy. IEEE Transactions on Power Electronics, 32(7), 5688--5698.
[23]
Tran, M. K., Panchal, S., Khang, T. D., Panchal, K., Fraser, R., and Fowler, M. (2022). Concept review of a cloud-based smart battery management system for lithium-ion batteries: Feasibility, logistics, and functionality. Batteries, 8(2), 19.
[24]
Li, S., and Zhao, P. (2021). Big data driven vehicle battery management method: A novel cyber-physical system perspective. Journal of Energy Storage, 33, 102064.
[25]
Naseri, F., Kazemi, Z., Larsen, P. G., Arefi, M. M., and Schaltz, E. (2023). Cyber-Physical Cloud Battery Management Systems: Review of Security Aspects. Batteries, 9(7), 382.
[26]
Wang, Y., Tian, J., Sun, Z., Wang, L., Xu, R., Li, M., and Chen, Z. (2020). A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems. Renewable and Sustainable Energy Reviews, 131, 110015.
[27]
Zhang, Y., Xiong, R., He, H., and Pecht, M. G. (2018). Long short-term memory recurrent neural network for remaining useful life prediction of lithium-ion batteries. IEEE Transactions on Vehicular Technology, 67(7), 5695--5705.
[28]
Qu, J., Liu, F., Ma, Y., and Fan, J. (2019). A neural-network-based method for RUL prediction and SOH monitoring of lithium-ion battery. IEEE access, 7, 87178--87191.
[29]
Ren, L., Zhao, L., Hong, S., Zhao, S., Wang, H., and Zhang, L. (2018). Remaining useful life prediction for lithium-ion battery: A deep learning approach. IEEE Access, 6, 50587--50598.
[30]
Wang, S., Jin, S., Bai, D., Fan, Y., Shi, H., and Fernandez, C. (2021). A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries. Energy Reports, 7, 5562--5574.
[31]
Sharma, P., and Bora, B. J. (2022). A Review of Modern Machine Learning Techniques in the Prediction of Remaining Useful Life of Lithium-Ion Batteries. Batteries, 9(1), 13.
[32]
Rieke, N., Hancox, J., Li, W., Milletari, F., Roth, H. R., Albarqouni, S., and Cardoso, M. J. (2020). The future of digital health with federated learning. NPJ digital medicine, 3(1), 119.
[33]
Li, L., Fan, Y., Tse, M., and Lin, K. Y. (2020). A review of applications in federated learning. Computers & Industrial Engineering, 149, 106854.
[34]
Li, T., Sahu, A. K., Talwalkar, A., and Smith, V. (2020). Federated learning: Challenges, methods, and future directions. IEEE signal processing magazine, 37(3), 50--60.
[35]
Zhang, C., Xie, Y., Bai, H., Yu, B., Li, W., and Gao, Y. (2021). A survey on federated learning. Knowledge-Based Systems, 216, 106775.
[36]
Wei, K., Li, J., Ding, M., Ma, C., Yang, H. H., Farokhi, F., and Poor, H. V. (2020). Federated learning with differential privacy: Algorithms and performance analysis. IEEE Transactions on Information Forensics and Security, 15, 3454--3469.
[37]
Qu, Y., Uddin, M. P., Gan, C., Xiang, Y., Gao, L., and Yearwood, J. (2022). Blockchain-enabled federated learning: A survey. ACM Computing Surveys, 55(4), 1--35.
[38]
Cheon, J. H., Kim, A., Kim, M., and Song, Y. (2017). Homomorphic encryption for arithmetic of approximate numbers. In Advances in Cryptology-ASIACRYPT 2017: 23rd International Conference on the Theory and Applications of Cryptology and Information Security, Hong Kong, China, December 3--7, 2017, Proceedings, Part I 23 (pp. 409--437). Springer International Publishing.
[39]
Iwen, M. A., and Ong, B. W. (2016). A distributed and incremental SVD algorithm for agglomerative data analysis on large networks. SIAM Journal on Matrix Analysis and Applications, 37(4), 1699--1718.
[40]
Severson, K. A., Attia, P. M., Jin, N., Perkins, N., Jiang, B., Yang, Z., and Braatz, R. D. (2019). Data-driven prediction of battery cycle life before capacity degradation. Nature Energy, 4(5), 383--391.
[41]
Anseán, D., Dubarry, M., Devie, A., Liaw, B. Y., García, V. M., Viera, J. C., and González, M. (2016). Fast charging technique for high power LiFePO4 batteries: A mechanistic analysis of aging. Journal of Power Sources, 321, 201--209.
[42]
Anseán, D., Dubarry, M., Devie, A., Liaw, B. Y., García, V. M., Viera, J. C., and González, M. (2017). Operando lithium plating quantification and early detection of a commercial LiFePO4 cell cycled under dynamic driving schedule. Journal of Power Sources, 356, 36--46.
[43]
Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society Series B: Statistical Methodology, 58(1), 267--288.
[44]
Hoerl, A., and Kennard, R. (1988). Ridge regression, in 'encyclopedia of statistical sciences', vol. 8.
[45]
Zou, H., and Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society Series B: Statistical Methodology, 67(2), 301--320.
Index Terms
- RUL Prediction of Lithium-ion Batteries using a Federated and Homomorphically Encrypted Learning Method
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