Fusion model basedRUL prediction method of lithium-ion battery under working conditions

• Autorzy: Fang, Pengya , Sui, Xiaoxiao , Zhang, Anhao , Wang, Di , Yin, Liping
• Języki publikacji: EN

Abstrakty

EN

Under working conditions, since the remaining useful life (RUL) prediction of lithium-ion battery is subject to uncertainties of random charging and discharging, and infeasibility of battery capacity test, a fusion model based RUL prediction method was proposed. First, the feature learning method of lithium-ion batteries was developed by synthesizing manual extraction and one-dimensional convolutional neural network (1DCNN) extraction. Then, a fused method was proposed to estimate the historical available capacity through exploring the spatial and temporal relationship of features, and the long short-term memory (LSTM) network model was adopted for predicting the RUL of lithium-ion battery. The proposed method was verified through the comparison of differentmethods, and the results show that it can realize highly precise and stable capacity estimation and RUL prediction under working conditions.

Słowa kluczowe

EN

lithium-ion battery; available capacity estimation; remaining useful life; RUL; health feature extraction; data-driven approach

• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2024
• Tom: Vol. 26, no. 3
• Strony: art. no. 186537
• Opis fizyczny: Bibliogr. 22 poz., rys., tab., wykr.

Twórcy

autor

Fang, Pengya

• School of Aero Engine, Zhengzhou University of Aeronautics, China,

autor

Sui, Xiaoxiao

• Chongqing Research Institute of Harbin Institute of Technology, China,

autor

Zhang, Anhao

• School of Materials Science and Engineering, Zhengzhou University of Aeronautics, China,

autor

Wang, Di

• School of Materials Science and Engineering, Zhengzhou University of Aeronautics, China,

autor

Yin, Liping

• School of Aero Engine, Zhengzhou University of Aeronautics, China,

Bibliografia

• 1. Zheng Wenfang, Fu Chunliu, Zhang Jianhua, et al. Review of the remaining life prediction methods for lithium-ion battery. Computer Measurement & Control 28.12(2020):1-6.
• 2. Chen Wan, Cai Yanping, Su Yanzhao, et al. Research on indirect prediction method of remaining useful life of lithium-ion battery. Chinese Journal of Power Sources 45.06(2021):719-722+813.
• 3. Zhang, Yongzhi, et al. Long short-term memory recurrent neural network for remaining useful life prediction of lithium-ion batteries. IEEE Transactions on Vehicular Technology 67.7 (2018): 5695-5705.https://doi.org/10.1109/TVT.2018.2805189
• 4. Zheng Jili. Research on electrochemical behavior and optimization of lithium-ion batteries based on mechanism model. 2019. Harbin Institute of Technology, MA thesis.
• 5. Gambhire, Priya , et al. A physics based reduced order aging model for lithium-ion cells with phase change. Journal of Power Sources 270.dec.15(2014):281-291.https://doi.org/10.1016/j.jpowsour.2014.07.127
• 6. Waag, Wladislaw , et al. Application-specific parameterization of reduced order equivalent circuit battery models for improved accuracy at dynamic load. Measurement 46.10(2013):4085-4093.https://doi.org/10.1016/j.measurement.2013.07.025
• 7. Fotouhi, Abbas, et al. A review on electric vehicle battery modelling: From Lithium-ion toward Lithium–Sulphur. Renewable and Sustainable Energy Reviews 56 (2016): 1008-1021.https://doi.org/10.1016/j.rser.2015.12.009
• 8. Sun Daoming. Research on SOC and capacity estimation methods of power lithium-ion batteries. 2021. Zhejiang University, PhD dissertation.
• 9. Sun Dong, et al. State of Health Prediction of Second-Use Lithium-Ion Battery. Transactions of China Electrotechnical Society 33. 09 (2018): 2121-2129.
• 10. Li, Yi, et al. Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review. Renewable and sustainable energy reviews 113 (2019): https://doi.org/10.1016/j.rser.2019.109254.
• 11. Tang, Xuliang, et al. Lithium-Ion Battery Remaining Useful Life Prediction Based on Hybrid Model. Sustainability 15.7 (2023): https://doi.org/10.3390/su15076261.
• 12. Liu, Yanshuo, et al. State-of-health estimation of lithium-ion batteries based on electrochemical impedance spectroscopy: a review. Protection and Control of Modern Power Systems 8.3 (2023): 1-17.https://doi.org/10.1186/s41601-023-00314-w
• 13. Long, Bing, et al. An improved autoregressive model by particle swarm optimization for prognostics of lithium-ion batteries. Microelectronics Reliability 53.6 (2013): 821-831.https://doi.org/10.1016/j.microrel.2013.01.006
• 14. Wei, Jingwen, Guangzhong Dong, and Zonghai Chen. Remaining useful life prediction and state of health diagnosis for lithium-ion batteries using particle filter and support vector regression. IEEE Transactions on Industrial Electronics 65.7 (2018): 5634-5643.https://doi.org/10.1109/TIE.2017.2782224
• 15. Liu, Datong, et al. Lithium-ion battery remaining useful life estimation with an optimized relevance vector machine algorithm with incremental learning. Measurement 63 (2015): 143-151.https://doi.org/10.1016/j.measurement.2014.11.031
• 16. Hu, Yang, and Pengcheng Luob. Performance data prognostics based on relevance vector machine and particle filter. Chemical Engineering 33 (2013): 349-354.
• 17. Cao Mengda, et al. Remaining Useful Life Estimation for Lithium-ion Battery Using Deep Learning Method. RADIO ENGINEERING 51. 07 (2021): 641-648.
• 18. Wang Mingyan. Research on Remaining Useful Life Prediction Method of Lithium-ion Battery Based on Machine Learning.2023. Anhui University Of Science & Technology, MA thesis.
• 19. Li Chaoran, et al. An Approach to Lithium-Ion Battery SOH Estimation Based on Convolutional Neural Network. Transactions of China Electrotechnical Society 35.19(2020): 4106-4119.
• 20. GAO De-xin, LIU Xin, YANG Qing. Remaining Useful Life Prediction of Lithium-Ion Battery Based on CNN and BiLSTM Fusion. INFORMATION AND CONTROL 51. 03 (2022): 318-329+360.
• 21. Ma, Ning, et al. Prediction of the remaining useful life of supercapacitors at different temperatures based on improved long short-term memory. Energies 16.14 (2023): 5240.https://doi.org/10.3390/en16145240
• 22. Yi, Zhenxiao, et al. Sensing as the key to the safety and sustainability of new energy storage devices. Protection and Control of Modern Power Systems 8.1 (2023): 1-22.https://doi.org/10.1186/s41601-023-00300-2

Identyfikatory

DOI

10.17531/ein/186537