Abstract
Drones require high-performance lithium batteries, and conventional battery replacement standards are not applicable in the context of drones. To address these issues, this paper introduces the State of Health (SoH) as an indicator to assess battery condition and proposes the concept of Battery Replacement Life (BRL) for Unmanned Aerial Vehicles (UAVs). To predict the SoH and BRL of UAV lithium batteries, this study employs models such as recurrent neural networks (RNN) to address the problem, including long short-term memory (LSTM) and gated recurrent units (GRU) designed for time-series problems. The research shows that the 3–4 layer LSTM and GRU models exhibit promising outcomes in predicting the SoH and BRL of the UAV lithium battery.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Han, X., Lu, L., Zheng, Y., Feng, X., Li, Z., Li, J., Ouyang, M.: A review on the key issues of the lithium ion battery degradation among the whole life cycle. ETransportation 1, 100005 (2019)
Tran, M.-K., Cunanan, C., Panchal, S., Fraser, R., Fowler, M.: Investigation of individual cells replacement concept in lithium-ion battery packs with analysis on economic feasibility and pack design requirements. Processes 9(12), 2263 (2021)
Shahjalal, M., Roy, P.K., Shams, T., Fly, A., Chowdhury, J.I., Rishad Ahmed, Md., Liu, K.: A review on second-life of li-ion batteries: prospects, challenges, and issues. Energy 241, 122881 (2022)
Lee, J., Kwon, D., Pecht, M.G.: Reduction of li-ion battery qualification time based on prognostics and health management. IEEE Trans. Indus. Electron. 66(9), 7310–7315 (2018)
Abeywickrama, H.V., Jayawickrama, B.A., He, Y., Dutkiewicz, E.: Comprehensive energy consumption model for unmanned aerial vehicles, based on empirical studies of battery performance. IEEE Access 6, 58383–58394 (2018)
Berecibar, M., Gandiaga, I., Villarreal, I., Omar, N., Van Mierlo, J., Van den Bossche, P.: Critical review of state of health estimation methods of li-ion batteries for real applications. Renew. Sustain. Energy Rev. 56, 572–587 (2016)
Sun, H., Wen, X., Liu, W., Wang, Z., Liao, Q.: State-of-health estimation of retired lithium-ion battery module aged at 1c-rate. J. Energy Storage 50, 104618 (2022)
Wang, H.-K., Zhang, Y., Huang, M.: A conditional random field based feature learning framework for battery capacity prediction. Sci. Rep. 12(1), 13221 (2022)
Chemali, E., Kollmeyer, P.J., Preindl, M., Ahmed, R., Emadi, A.: Long short-term memory networks for accurate state-of-charge estimation of li-ion batteries. IEEE Trans. Indus. Electron. 65(8), 6730–6739 (2017)
Hoque, Md.A., Hassan, M.K., Hajjo, A., Tokhi, M.O.: Neural network-based li-ion battery aging model at accelerated c-rate. Batteries 9(2), 93 (2023)
Ardeshiri, R.R., Ma, C.: Multivariate gated recurrent unit for battery remaining useful life prediction: a deep learning approach. Int. J. Energy Res. 45(11), 16633–16648 (2021)
Singh, M., Bansal, S., Panigrahi, B.K., Garg, A.: A genetic algorithm and rnn-lstm model for remaining battery capacity prediction. J. Comput. Inform. Sci. Eng. 22(4), 041009 (2022)
Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Sci. Rep. 8(1), 6085 (2018)
Gers, F.A., Schmidhuber, J., Cummins, F.: Learning to forget: continual prediction with lstm. Neural Comput. 12(10), 2451–2471 (2000)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Yu, F., Wang, J., Chen, X. (2023). Evaluating RNN and Its Improved Models for Lithium Battery SoH and BRL Prediction. In: Jia, Y., Zhang, W., Fu, Y., Wang, J. (eds) Proceedings of 2023 Chinese Intelligent Systems Conference. CISC 2023. Lecture Notes in Electrical Engineering, vol 1090. Springer, Singapore. https://doi.org/10.1007/978-981-99-6882-4_18
Download citation
DOI: https://doi.org/10.1007/978-981-99-6882-4_18
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-6881-7
Online ISBN: 978-981-99-6882-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)