Multi-Scale Prediction of RUL and SOH for Lithium-Ion Batteries Based on WNN-UPF Combined Model

JIA Jianfang; WANG Keke; PANG Xiaoqiong; SHI Yuanhao; WEN Jie; ZENG Jianchao

doi:10.1049/cje.2020.10.012

Volume 30 Issue 1

Jan. 2021

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Article Navigation > Chinese Journal of Electronics > 2021 > 30(1): 26-35

JIA Jianfang, WANG Keke, PANG Xiaoqiong, et al., “Multi-Scale Prediction of RUL and SOH for Lithium-Ion Batteries Based on WNN-UPF Combined Model,” Chinese Journal of Electronics, vol. 30, no. 1, pp. 26-35, 2021, doi: 10.1049/cje.2020.10.012

Citation:

JIA Jianfang, WANG Keke, PANG Xiaoqiong, et al., “Multi-Scale Prediction of RUL and SOH for Lithium-Ion Batteries Based on WNN-UPF Combined Model,” Chinese Journal of Electronics, vol. 30, no. 1, pp. 26-35, 2021, doi: 10.1049/cje.2020.10.012

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Multi-Scale Prediction of RUL and SOH for Lithium-Ion Batteries Based on WNN-UPF Combined Model

doi: 10.1049/cje.2020.10.012

1.
School of Electrical and Control Engineering, North University of China, Taiyuan 030051, China
2.
School of Data Science and Technology, North University of China, Taiyuan 030051, China

Funds:

the Key Program of Research and Development of Shanxi Province 201703D111011

the Research Project Supported by Shanxi Scholarship Council of China 2020-114

the Natural Science Foundation of Shanxi Province 201801D121159

the Natural Science Foundation of Shanxi Province 201801D221208

the Natural Science Foundation of Shanxi Province 201801D121188

the Natural Science Foundation of Shanxi Province 201901D111164

the Graduate Science and Technology Project of NUC 20191666

More Information

Author Bio:
WANG Keke   is an M.E. candidate with the School of Electrical and Control Engineering, NUC. His research interests include remaining useful life prediction of lithium-ion battery and modeling and optimization of complex systems

PANG Xiaoqiong   received the M.E. degree from NUC, and the Ph.D. degree from South China Agricultural University. She is an associate professor with the School of Data Science and Technology, NUC. Her research interests include control of complex networks, network security, and prediction algorithm

SHI Yuanhao   received the M.E. degree from the Taiyuan University of Science and Technology, and the Ph.D. degree from Shanghai Jiao Tong University, China. He is a lecturer with the School of Electrical and Control Engineering, NUC. His research interests include modeling and soot blowing of boiler, and prediction of remaining useful life

WEN Jie   received the B.E. and Ph.D. degrees from the University of Science and Technology of China. He is a lecturer with the School of Electrical and Control Engineering, NUC, China. His research interests include optimization of boiler soot blowing, intelligent control, and deep learning

ZENG Jianchao   received the B.E. degree from Taiyuan Heavy Machinery Institute, and M.E. and Ph.D. degrees from Xi'an Jiaotong University, China. He is a Professor with the School of Data Science and Technology, NUC, China. His research interests include modeling and control of complex systems, intelligent computation, swarm intelligence, and swarm robotics. He is the Vice President of NUC and the Technical Committee on System Simulation of the Chinese Association of Automation and the Director of the China Simulation Federation
Corresponding author: JIA Jianfang (corresponding author) received the B.E. and M.E. degrees from North University of China (NUC), China, and the Ph.D. degree from Institute of Automation, Chinese Academy of Sciences, China. He is an associate professor with the School of Electrical and Control Engineering, NUC. His research interests include prognostic and health management, intelligent control of wind power generation systems, and modeling and optimization of complex systems. (Email: jiajianfang@nuc.edu.cn)
Received Date: 2019-11-29
Accepted Date: 2020-07-15
Publish Date: 2021-01-01

Abstract

Abstract

The prediction of Remaining useful life (RUL) and the estimation of State of health (SOH) are extremely important issues for operating performance of Lithium-ion (Li-ion) batteries in the Battery management system (BMS). A multi-scale prediction approach of RUL and SOH is presented, which combines Wavelet neural network (WNN) with Unscented particle filter (UPF) model. The capacity degradation data of Li-ion batteries are decomposed into the low-frequency degradation trend and high-frequency fluctuation components by Discrete wavelet transform (DWT). Based on the WNN-UPF model, the long-term RUL of Li-ion batteries is predicted with the low-frequency degradation trend data. The high-frequency fluctuation data and RUL prediction results are integrated effectively to estimate the short-term SOH of Li-ion batteries. The experimental results show that the proposed method achieves high accuracy and strong robustness, even if the prediction starting point is set to the early stage of Li-ion batteries' lifespan.
- Lithium-ion batteries; Multi-scale prediction,
- Wavelet neural network,
- Unscented particle filter,
- Remaining useful life,
- State of health

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References(27)

References

[1]	D.F. Frost and D.A. Howey, "Completely decentralized active balancing battery management system", IEEE Transactions on Power Electronics, Vol. 33, No. 1, pp. 729–738, 2018. doi: 10.1109/TPEL.2017.2664922
[2]	T. Morstyn, M. Momayyezan, B. Hredzak, et al., "Distributed control for state-of-charge balancing between the modules of a reconfigurable battery energy storage system", IEEE Trans. on Power Electronics, Vol. 31, No. 11, pp. 7986–7995, 2016. doi: 10.1109/TPEL.2015.2513777
[3]	F. Altaf, B. Egardt and L.J. Mardh, "Load management of modular battery using model predictive control: Thermal and state-of-charge balancing", IEEE Transactions on Control Systems Technology, Vol. 25, No. 1, pp. 47–62, 2017. doi: 10.1109/TCST.2016.2547980
[4]	Z. Zhang, Y.Y. Cai, Y. Zhang, et al., "A distributed architecture based on microbank modules with self-reconfiguration control to improve the energy efficiency in the battery energy storage system", IEEE Transactions on Power Electronics, Vol. 31, No. 1, pp. 304–317, 2016. doi: 10.1109/TPEL.2015.2406773
[5]	Y. Merla, B. Wu, V. Yufit, et al., "An easy-to-parameterise physics-informed battery model and its application towards lithium-ion battery cell design, diagnosis, and degradation", Journal of Power Sources, Vol. 384, pp. 66–79, 2018. doi: 10.1016/j.jpowsour.2018.02.065
[6]	C.F. Zou, L. Zhang, X.S. Hu, et al., "A review of fractional-order techniques applied to lithium-ion batteries, lead-acid batteries, and supercapacitors", Journal of Power Sources, Vol. 390, pp. 286–296, 2018. doi: 10.1016/j.jpowsour.2018.04.033
[7]	M. Galeotti, L. Cinà, C. Giammanco, et al., "Performance analysis and SOH (state of health) evaluation of lithium polymer batteries through electrochemical impedance spectroscopy", Energy, Vol. 89, No. 15, pp. 678–686, 2015. http://www.sciencedirect.com/science/article/pii/s0360544215007756
[8]	A. Cordoba-Arenas, S. Onori, Y. Guezennec, et al., "Capacity and power fade cycle-life model for plug-in hybrid electric vehicle lithium-ion battery cells containing blended spinel and layered-oxide positive electrodes", Journal of Power Sources, Vol. 278, pp. 473–483, 2015. doi: 10.1016/j.jpowsour.2014.12.047
[9]	J.F. Jia, J.Y. Liang, Y.H. Shi, et al., "SOH and RUL prediction of lithium-ion batteries based on gaussian process regression with indirect health indicators", Energies, Vol. 13, No. 2, pp. 375–395, 2020. doi: 10.3390/en13020375
[10]	J.B. Yu, "State of health prediction of lithium-ion batteries: Multiscale logic regression and gaussian process regression ensemble", Reliability Engineering & System Safety, Vol. 174, pp. 82–95, 2018. http://www.sciencedirect.com/science/article/pii/S095183201730652X
[11]	J. Li, K. Adewuyi, N. Lotfi, et al., "A single particle model with chemical/mechanical degradation physics for lithium ion battery state of health (SOH) estimation", Applied Energy, Vol. 212, No. 9, pp. 1178–1190, 2018. http://www.sciencedirect.com/science/article/pii/S0306261918300114
[12]	G. Dong, Z. Chen, J. Wei, et al., "Battery health prognosis using Brownian motion modeling and particle filtering", IEEE Transactions on Industrial Electronics, Vol. 65, No. 11, pp. 8646–8655, 2018. doi: 10.1109/TIE.2018.2813964
[13]	P.L.T. Duong and N. Raghavan, "Heuristic kalman optimized particle filter for remaining useful life prediction of lithium-ion battery", Microelectronics Reliability, Vol. 81, pp. 232–243, 2018. doi: 10.1016/j.microrel.2017.12.028
[14]	C. Vidal, P. Malysz, P. Kollmeyer, et al., "Machine learning applied to electrified vehicle battery state of charge and state of health estimation: State-of-the-art", IEEE Access, Vol. 8, pp. 52796–52814, 2020. doi: 10.1109/ACCESS.2020.2980961
[15]	D.T. Liu, W. Xie, H.T. Liao, et al., "An integrated probabilistic approach to lithium-ion battery remaining useful life estimation", IEEE Transactions on Instrumentation and Measurement, Vol. 64, No. 3, pp. 660–670, 2015. doi: 10.1109/TIM.2014.2348613
[16]	Z.W. He, M.Y. Gao, G.J. Ma, et al., "Online state-of-health estimation of lithium-ion batteries using dynamic Bayesian networks", Journal of Power Sources, Vol. 267, pp. 576–583, 2014. doi: 10.1016/j.jpowsour.2014.05.100
[17]	Y. Zhang, R. Xiong, H. He, et al., "Long short-term memory recurrent neural network for remaining useful life prediction of lithium-ion batteries", IEEE Transactions on Vehicular Technology, Vol. 67, No. 7, pp. 5695–5705, 2018. doi: 10.1109/TVT.2018.2805189
[18]	M. Berecibar, I. Gandiaga, I. Villarreal, et al., "Critical review of state of health estimation methods of Li-ion batteries for real applications", Renewable and Sustainable Energy Reviews, Vol. 56, pp. 572–587, 2016. doi: 10.1016/j.rser.2015.11.042
[19]	F. Cadini, F. Cancelliere, M. Giglio, et al., "State-of-life prognosis and diagnosis of lithium-ion batteries by data-driven particle filters", Applied Energy, Vol. 235, pp. 661–672, 2019. doi: 10.1016/j.apenergy.2018.10.095
[20]	J. Kim, S. Lee and B.H. Cho, "Complementary cooperation algorithm based on DEKF combined with pattern recognition for SOC/capacity estimation and SOH prediction", IEEE Trans. on Power Electronics, Vol. 27, No. 1, pp. 436–451, 2012. doi: 10.1109/TPEL.2011.2158554
[21]	J.B. Yu, "State-of-health monitoring and prediction of lithium-ion battery using probabilistic indication and state-space model", IEEE Transactions on Instrumentation and Measurement, Vol. 64, No. 11, pp. 2937–2949, 2015. doi: 10.1109/TIM.2015.2444237
[22]	J. Liu, W. Wang, F. Ma, et al., "A data-model-fusion prognostic framework for dynamic system state forecasting", Engineering Applications of Artificial Intelligence, Vol. 25, No. 4, pp. 814–823, 2012. doi: 10.1016/j.engappai.2012.02.015
[23]	Y.P. Zhou and M.H. Huang, "Lithium-ion batteries remaining useful life prediction based on a mixture of empirical mode decomposition and ARIMA model", Microelectronics Reliability, Vol. 65, pp. 265–273, 2016. doi: 10.1016/j.microrel.2016.07.151
[24]	X. Li, L. Zhang, Z. Wang, et al., "Remaining useful life prediction for lithium-ion batteries based on a hybrid model combining the long short-term memory and Elman neural networks", Journal of Energy Storage, Vol. 21, pp. 510–518, 2019. doi: 10.1016/j.est.2018.12.011
[25]	X.Q. Pang, R. Huang, J. Wen, et al., "A lithium-ion battery RUL prediction method considering the capacity regeneration phenomenon", Energies, Vol. 12, No. 12, pp. 1–14, 2019. http://www.researchgate.net/publication/333737223_A_Lithium-ion_Battery_RUL_Prediction_Method_Considering_the_Capacity_Regeneration_Phenomenon
[26]	Y. Lei, Z. He and Y. Zi, "EEMD method and WNN for fault diagnosis of locomotive roller bearings", Expert Systems with Applications, Vol. 38, No. 6, pp. 7334–7341, 2011. doi: 10.1016/j.eswa.2010.12.095
[27]	H. Zhang, Q. Miao, X. Zhang, et al., "An improved unscented particle filter approach for lithium-ion battery remaining useful life prediction", Microelectronics Reliability, Vol. 81, pp. 288–298, 2018. doi: 10.1016/j.microrel.2017.12.036