Login Register Cart 0
Generic placeholder image

Recent Advances in Computer Science and Communications

Editor-in-Chief

ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

Back
Journal Home About Journal Editorial Board Current Issue Volumes/Issues
Subscribe
Research Article

Remaining Useful Life Prediction of Lithium-ion Batteries Using Multiple Kernel Extreme Learning Machine

Author(s): Renxiong Liu*

Volume 15, Issue 5, 2022

Published on: 02 October, 2020

Article ID: e060422186535 Pages: 7

DOI: 10.2174/2666255813999201002152742

Price: $65

Abstract

Objective: Lithium-ion batteries are important components used in electric automobiles (EVs), fuel cell EVs and other hybrid EVs. Therefore, it is greatly important to discover its remaining useful life (RUL).

Methods: In this paper, a battery RUL prediction approach using multiple kernel extreme learning machine (MKELM) is presented. The MKELM’s kernel keeps diversified by consisting of multiple kernel functions including Gaussian kernel function, Polynomial kernel function and Sigmoid kernel function, and every kernel function’s weight and parameter are optimized through differential evolution (DE) algorithm.

Results: Battery capacity data measured from NASA Ames Prognostics Center are used to demonstrate the prediction procedure of the proposed approach, and the MKELM is compared with other commonly used prediction methods in terms of absolute error, relative accuracy and mean square error.

Conclusion: The prediction results prove that the MKELM approach can accurately predict the battery RUL. Furthermore, a compare experiment is executed to validate that the MKELM method is better than other prediction methods in terms of prediction accuracy.

Keywords: Lithium-ion battery, RUL prediction, MKELM, multiple kernel, DE algorithm, mean square error

Graphical Abstract

[1]
X. Hu, C.M. Martinez, and Y. Yang, "Charging, power management, and battery degradation mitigation in plug-in hybrid electric vehicles: A unified cost-optimal approach", Mech. Syst. Signal Process., vol. 87, pp. 4-16, 2017.
[http://dx.doi.org/10.1016/j.ymssp.2016.03.004]
[2]
R. Hidalgo-León, D. Siguenza, and C. Sanchez, "A survey of battery energy storage system (BESS), applications and environmental impacts in power systems", Proc. 2017 IEEE Second Ecuador Technical Chapters Meeting (ETCM), 2017
[http://dx.doi.org/10.1109/ETCM.2017.8247485]
[3]
R. Hidalgo, and J. Urquizo, "Pablo Jácome, et al. “Modeling Battery under Discharge using Improved Thévenin-Shepherd Electrical Battery Model", Proc. IEEE Vehicle Power and Propulsion Conference (VPPC), 2018
[4]
R. Hidalgo-Leon, J. Urquizo, and J. Litardo, "Simulation of battery discharge emulator using power electronics device with cascaded P-I control", Proc. IEEE International Conference on Industrial Technology (ICIT), 2020
[5]
J. Wu, S. Guo, and H. Huang, "Information and Communications Technologies for Sustainable Development Goals: State-of-the-Art, Needs and Perspectives", IEEE Commun. Surv. Tutor., vol. 20, no. 3, pp. 2389-2406, 2018.
[http://dx.doi.org/10.1109/COMST.2018.2812301]
[6]
J. Wu, S. Guo, and J. Li, Big Data Meet Green Challenges: Big Data toward Green Applications IEEE Syst. J., vol. 10, no. 3, p., 2016.
[http://dx.doi.org/10.1109/JSYST.2016.2550530]
[7]
J. Wu, S. Guo, and J. Li, Big Data Meet Green Challenges: Greening Big Data IEEE Syst. J., vol. 10, no. 3, p., 2016.
[http://dx.doi.org/10.1109/JSYST.2016.2550538]
[8]
C. Zou, C. Manzie, D. Nesic, and A.G. Kallapur, "Multi-time-scale observer design for state-of-charge and state-of-health of a lithium-ion battery", J. Power Sources, vol. 335, pp. 121-130, 2016.
[http://dx.doi.org/10.1016/j.jpowsour.2016.10.040]
[9]
S. Yuan, and H. Wu, "andC.Yin, “State of Charge Estimation Using the Extended Kalman Filter for Battery Management Systems Based on the ARX Battery Model”", Energies, vol. 6, no. 1, pp. 444-470, 2013.
[http://dx.doi.org/10.3390/en6010444]
[10]
Z. Chen, Y. Fu, and C.C. Mi, "State of Charge Estimation of Lithium-Ion Batteries in Electric Drive Vehicles Using Extended Kalman Filtering", IEEE T. Veh. Technol., vol. 62, no. 3, pp. 1020-1030, 2013.
[http://dx.doi.org/10.1109/TVT.2012.2235474]
[11]
X. Zheng, and H. Fang, "An integrated unscented kalman filter and relevance vector regression approach for lithium-ion battery remaining useful life and short-term capacity prediction", Reliab. Eng. Syst. Saf., vol. 144, pp. 74-82, 2015.
[http://dx.doi.org/10.1016/j.ress.2015.07.013]
[12]
S. Tang, C. Yu, X. Wang, X. Guo, and X. Si, "Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Wiener Process with Measurement Error", Energies, vol. 7, no. 2, pp. 520-547, 2014.
[http://dx.doi.org/10.3390/en7020520]
[13]
W. Xian, B. Long, M. Li, and H. Wang, "Prognostics of lithium-ion batteries based on the Verhulst model, particle swarm optimization and particle filter", IEEE Trans. Instrum. Meas., vol. 63, no. 1, pp. 2-17, 2014.
[http://dx.doi.org/10.1109/TIM.2013.2276473]
[14]
B. Saha, K. Goebel, S. Poll, and J. Christophersen, "Prognostics methods for battery health monitoring using a Bayesian framework", IEEE Trans. Instrum. Meas., vol. 58, no. 2, pp. 291-296, 2009.
[http://dx.doi.org/10.1109/TIM.2008.2005965]
[15]
L. Chao, Q. Lai, T. Ge, H. Yu, L. Wang, and N. Ma, "A lead-acid battery’s remaining useful life prediction by using electrochemical model in the Particle Filtering framework", Energy, vol. 120, no. 1, pp. 975-984, 2017.
[16]
Z. Liu, G. Sun, S. Bu, J. Han, X. Tang, and M. Pecht, "Particle Learning Framework for Estimating the Remaining Useful Life of Lithium-Ion Batteries", IEEE Trans. Instrum. Meas., vol. 66, no. 2, pp. 280-293, 2017.
[http://dx.doi.org/10.1109/TIM.2016.2622838]
[17]
W. Yan, B. Zhang, W. Dou, and D. Liu, "Low-Cost Adaptive Lebesgue Sampling Particle Filtering Approach for Real-Time Li-Ion Battery Diagnosis and Prognosis", IEEE T.Autom. Sci. Eng., pp. 1-11, 2017.
[18]
W.X. Shen, C.C. Chan, E.W.C. Lo, and K.T. Chau, "A new battery available capacity indicator for electric vehicles using neural network", Energy Convers. Manage., vol. 43, no. 6, pp. 817-826, 2002.
[http://dx.doi.org/10.1016/S0196-8904(01)00078-4]
[19]
J. Wu, C. Zhang, and Z. Chen, "An online method for lithium-ion battery remaining useful life estimation using importance sampling and neural networks", Appl. Energy, vol. 173, no. 1, pp. 134-140, 2016.
[http://dx.doi.org/10.1016/j.apenergy.2016.04.057]
[20]
M.A. Patil, P. Tagade, K.S. Hariharan, S.M. Kolake, T. Song, T. Yeo, and S. Doo, "A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimation", Appl. Energy, vol. 159, no. 1, pp. 285-297, 2015.
[http://dx.doi.org/10.1016/j.apenergy.2015.08.119]
[21]
C. Zhang, Y. He, L. Yuan, S. Xiang, and J. Wang, "Prognostics of lithium-ion batteries based on wavelet denoising and DE-RVM", Comput. Intell. Neurosci., 2015.
[http://dx.doi.org/10.1155/2015/918305]
[22]
N.Y. Liang, G.B. Huang, P. Saratchandran, and N. Sundararajan, "A fast and accurate online sequential learning algorithm for feedforward networks", IEEE Trans. Neural Netw., vol. 17, no. 6, pp. 1411-1423, 2006.
[http://dx.doi.org/10.1109/TNN.2006.880583] [PMID: 17131657]
[23]
M-B. Li, G.B. Huang, P. Saratchandran, and N. Sundararajan, "Fully Complex Extreme Learning Machine", Neurocomputing, vol. 68, pp. 306-314, 2005.
[http://dx.doi.org/10.1016/j.neucom.2005.03.002]
[24]
W. Zong, G.B. Huang, and Y. Chen, "Weighted extreme learning machine for imbalance learning", Neurocomputing, vol. 101, pp. 229-242, 2013.
[http://dx.doi.org/10.1016/j.neucom.2012.08.010]
[25]
C. Zhang, Y. He, L. Yuan, and S. Xiang, "Capacity Prognostics of Lithium-Ion Batteries using EMD Denoising and Multiple Kernel RVM", IEEE Access, vol. 5, pp. 12061-12070, 2017.
[http://dx.doi.org/10.1109/ACCESS.2017.2716353]
[26]
H. Yang, Z. Xu, J. Ye, I. King, and M.R. Lyu, "Efficient sparse generalized multiple kernel learning", IEEE Trans. Neural Netw., vol. 22, no. 3, pp. 433-446, 2011.
[http://dx.doi.org/10.1109/TNN.2010.2103571] [PMID: 21257374]
[27]
K. Blekas, and A. Likas, "Sparse regression mixture modeling with the multi-kernel relevance vector machine", Knowl. Inf. Syst., vol. 39, no. 2, pp. 241-264, 2014.
[http://dx.doi.org/10.1007/s10115-013-0704-0]
[28]
B. Peng, "B,Liu,F. Y. Zhang, and L. Wang. “Differential evolution algorithm-based parameter estimation for chaotic systems Chaos”, Chaos", Soliton.Fract., vol. 39, no. 5, pp. 110-2118, 2009.
[29]
B. Saha, and K. Goebel, Battery Data Set., NASA Ames Prognostics Data Repository, 2007. ti.arc.nasa.gov/project/prognostic-data-repositoryz
[30]
Y. Xu, Z. Zhou, and Q. Zhou, "A New Feedback DE-ELM with Time Delay-Based EFSM Approach for Fault Prediction of Non-Linear Processes", Can. J. Chem. Eng., vol. 93, no. 9, pp. 1603-1612, 2015.
[http://dx.doi.org/10.1002/cjce.22246]
[31]
J. Han, Q. Li, H. Wu, H. Zhu, and Y. Song, "Prediction of cooling efficiency of forced-air precooling systems based on optimized differential evolution and improved BP neural network., vol. Vol. 84", Appl. Soft Comput., 2019.

Rights & Permissions Print Cite
Article Metrics
21
© 2024 Bentham Science Publishers | Privacy Policy