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Open Access

State of health prediction for lithium-ion batteries based on ensemble Gaussian process regression

Zhouli HUIRuijie WANGNana FENGMing YANG( )
School of Mathematics, North University of China, Taiyuan 030051, China
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Abstract

The performance of lithium-ion batteries(LIBs) gradually declines over time, making it critical to predict the battery’s state of health(SOH) in real-time. This paper presents a model that incorporates health indicators and ensemble Gaussian process regression(EGPR) to predict the SOH of LIBs. Firstly, the degradation process of an LIB is analyzed through indirect health indicators(HIs) derived from voltage and temperature during discharge. Next, the parameters in the EGPR model are optimized using the gannet optimization algorithm(GOA), and the EGPR is employed to estimate the SOH of LIBs. Finally, the proposed model is tested under various experimental scenarios and compared with other machine learning models. The effectiveness of EGPR model is demonstrated using the National Aeronautics and Space Administration (NASA) LIB. The root mean square error(RMSE) is maintained within 0.20%, and the mean absolute error(MAE) is below 0.16%, illustrating the proposed approach’s excellent predictive accuracy and wide applicability.

References

[1]

LIN M Q, WU D G, MENG J H, et al. A multi-feature-based multi-model fusion method for state of health estimation of lithium-ion batteries. Journal of Power Sources, 2022, 518: 230774.

[2]

TIAN H X, QIN P L, LI K, et al. A review of the state of health for lithium-ion batteries: Research status and suggestions. Journal of Cleaner Production, 2020, 261: 120813.

[3]

SHEN S Q, LIU B C, ZHANG K, et al. Toward fast and accurate SOH prediction for lithium-ion batteries. IEEE Transactions on Energy Conversion, 2021, 36(3): 2036-2046.

[4]

LIN C P, CABRERA J, YANG F F, et al. Battery state of health modeling and remaining useful life prediction through time series model. Applied Energy, 2020, 275: 115338.

[5]

CHEN D, MENG J H, HUANG H Y, et al. An empirical-data hybrid driven approach for remaining useful life prediction of lithium-ion batteries considering capacity diving. Energy, 2022, 245: 123222.

[6]

GE M F, LIU Y B, JIANG X X, et al. A review on state of health estimations and remaining useful life prognostics of lithium-ion batteries. Measurement, 2021, 174: 109057.

[7]

ZHANG M, YANG D F, DU J X, et al. A review of SOH prediction of Li-ion batteries based on data-driven algorithms. Energies, 2023, 16(7): 3167.

[8]

NG K S, MOO C S, CHEN Y P, et al. Enhanced coulomb counting method for estimating state-of-charge and state of health of lithium-ion batteries. Applied Energy, 2009, 86(9): 1506-1511.

[9]
KIEL M, SAUER D U, TURPIN P, et al. Validation of single frequency z measurement for standby battery state of health determination //2008 IEEE 30th International Telecommunications Energy Conference, September 14-18, 2008, San Diego, CA, USA.New York: IEEE, 2008: 1-7.
[10]

XIONG R, TIAN J P, MU H, et al. A systematic model-based degradation behavior recognition and health monitoring method for lithium-ion batteries. Applied Energy, 2017, 207: 372-383.

[11]
ZENATI A, DESPREZ P, RAZIK H. Estimation of the SOC and the SOH of Li-ion batteries by combining impedance measurements with the fuzzy logic inference//36th Annual Conference on IEEE Industrial Electronics Society, November 7-10, 2010, Glendale, AZ, USA. New York: IEEE, 2010: 1773-1778.
[12]

LOVE C T, VIRJI M B V, ROCHELEAU R E, et al. State-of-health monitoring of 18650 4S packs with a single-point impedance diagnostic. Journal of Power Sources, 2014, 266: 512-519.

[13]

BRESSEL M, HILAIRET M, HISSEL D, et al. Extended Kalman filter for prognostic of proton exchange membrane fuel cell. Applied Energy, 2016, 164: 220-227.

[14]

DONG G Z, CHEN Z H, WEI J W, et al. Battery health prognosis using Brownian motion modeling and particle filtering. IEEE Transactions on Industrial Electronics, 2018, 65(11): 8646-8655.

[15]

YAN W Z, ZHANG B, ZHAO G Q, et al. A battery management system with a lebesgue sampling-based extended Kalman filter. IEEE Transactions on Industrial Electronics, 2019, 66(4): 3227-3236.

[16]

QIU X H, WU W X, Wang S F. Remaining useful life prediction of lithium-ion battery based on improved cuckoo search particle filter and a novel state of charge estimation method. Journal of Power Sources, 2020, 450: 227700.

[17]

HE J, WEI Z, BIAN X, et al. State-of-health estimation of lithium-ion batteries using incremental capacity analysis based on voltage-capacity model. IEEE Transactions on Transportation Electrification, 2020, 6(2): 417-426.

[18]

RUAN H K, HE H W, WEI Z B, et al. State of health estimation of lithium-ion battery based on constant-voltage charging reconstruction. IEEE Journal of Emerging and Selected Topics in Power Electronics, 2023, 11(4): 4393-4402.

[19]

BIAN X, WEI Z, LI W, et al. State-of health estimation of lithium-ion batteries by fusing an open circuit voltage model and incremental capacity analysis. IEEE Transactions on Power Electronics, 2022, 37(2): 2226-2236.

[20]

SUN H L, YANG D F, DU J X, et al. Prediction of Li-ion battery state of health based on data-driven algorithm. Energy Reports, 2022, 8: 442-449.

[21]

XIA F, WANG K G, CHEN J J. State of health and remaining useful life prediction of lithium-ion batteries based on a disturbance-free incremental capacity and differential voltage analysis method. Journal of Energy Storage, 2023, 64: 107161.

[22]

TIAN Y K, WEN J, YANG Y R, et al. State-of-health prediction of lithium-ion batteries based on CNN-BiLSTM-AM. Batteries, 2022, 8(10): 155.

[23]

FU P Y, CHU L, LI J H, et al. State of health prediction of lithium-ion battery based on deep dilated convolution. Sensors, 2022, 22(23): 9435.

[24]

SHEN S, SADOUGHI M, CHEN X Y, et al. A deep learning method for online capacity estimation of lithium-ion batteries. The Journal of Energy Storage, 2019, 25: 100817.

[25]

HU C, JAIN G, SCHMIDT C, et al. Online estimation of lithium-ion battery capacity using sparse Bayesian learning. Journal of Power Sources, 2015, 289: 105-113.

[26]

GAO D, HUANG M. Prediction of remaining useful life of lithium-ion battery based on multi-kernel support vector machine with particle swarm optimization. Journal of Power Electronics, 2017, 17(5): 1288-1297.

[27]

LI Y, ZOU C F, BERECIBAR M, et al. Random forest regression for online capacity estimation of lithium-ion batteries. Applied Energy, 2018, 232: 197-210.

[28]

LI X Y, YUAN C, LI X, et al. State of health estimation for li-ion battery using incremental capacity analysis and gaussian process regression. Energy, 2020, 190: 116467.

[29]

WU Y T, XUE Q, SHEN J W, et al. State of health estimation for lithium-ion batteries based on healthy features and long short-term memory. IEEE Access, 2020, 8: 28533-28547.

[30]

XIA Z Y, ABU QAHOUQ J A. Lithium-ion battery ageing behavior pattern characterization and state-of-health estimation using data-driven method. IEEE Access, 2021, 9: 98287-98304.

[31]

WU T Z, HUANG Y H, XU Y H, et al. SOH prediction for lithium-ion battery based on improved support vector regression. International Journal of Green Energy, 2023, 20(3): 227-236.

[32]

CUI S M, JOE I. A dynamic spatial-temporal attention-based GRU model with healthy features for state-of-health estimation of lithium-ion batteries. IEEE Access, 2021, 9: 27374-27388.

[33]

MAWONOU K S R, EDDAHECH A, DUMUR D, et al. State-of-health estimators coupled to a random forest approach for lithium-ion battery aging factor ranking. Journal of Power Sources, 2021, 484: 229154.

[34]

HU X S, CHE Y H, LIN X K, et al. Battery health prediction using fusion-based feature selection and machine learning. IEEE Transactions on Transportation Electrification, 2021, 7(2): 382-398.

[35]

XING J, ZHANG H L, ZHANG J P. Remaining useful life prediction of lithium batteries based on principal component analysis and improved Gaussian process regression. International Journal of Electrochemical Science, 2023, 18(4): 100048.

[36]

GONG D, GAO Y L, KOU Y L, et al. State of health estimation for lithium-ion battery based on energy features. Energy, 2022, 257: 124812.

[37]

FENG H L, SHI G L. SOH and RUL prediction of li-ion batteries based on improved Gaussian process regression. Journal of Power Electronics, 2021, 21: 1845-1854.

[38]

ZHENG X Y, DENG X G. State-of-health prediction for lithium-ion batteries with multiple gaussian process regression model. IEEE Access, 2020, 7: 150383-150394.

[39]

LIU K L, HU X S, WEI Z B, et al. Modified gaussian process regression models for cyclic capacity prediction of lithium-ion batteries. IEEE Transactions on Transportation Electrification, 2020, 5(4): 1225-1236.

[40]

GOEBEL K, SAHA B, SAXENA A, et al. Prognostics in battery health management. IEEE Instrumentation & Measurement Magazine, 2008, 11(4): 33-40.

[41]

YU Z, LIU N, ZHANG Y, et al. Battery SOH prediction based on multi-dimensional health indicators. Batteries, 2023, 9(2): 80.

[42]
RASMUSSEN C E, WILLIAMS C K I. Gaussian processes for machine learning. Cambridge, Mass.: MIT Press, 2006.
[43]

LI G Z, LI B, LI C, et al. State-of-health rapid estimation for lithium-ion battery based on an interpretable stacking ensemble model with short-term voltage profiles. Energy, 2023, 263: 126064.

[44]

WU H, LEVINSON D. The ensemble approach to forecasting: A review and synthesis. Transportation Research Part C: Emerging Technologies, 2021, 132:103357.

[45]

SHARMA R S, SINGH B, KAUR M. A novel approach of ensemble methods using the stacked generalization for high-dimensional datasets. IETE Journal of Research, 2022, 69(10): 6802-6817.

[46]

JIN Z, LI X B, YU D W, et al. Lithium-ion battery state of health estimation using meta-heuristic optimization and Gaussian process regression. Journal of Energy Storage, 2023, 58: 106319.

[47]

RICHARDSON R R, OSBORNE M A, HOWEY D A. Gaussian process regression for forecasting battery state of health. Journal of Power Sources, 2017, 357: 209–219.

[48]

PAN J S, ZHANG L G, WANG R B, et al. Gannet optimization algorithm: A new metaheuristic algorithm for solving engineering optimization problems. Mathematics and Computers in Simulation, 2022, 202: 343-373.

[49]

MA Y, SHAN C, GAO J W, et al. A novel method for state of health estimation of lithium-ion batteries based on improved LSTM and health indicators extraction. Energy, 2022, 251: 123973.

[50]

YANG Y R, WEN J, SHI Y H, et al. State of health prediction of lithium-ion batteries based on the discharge voltage and temperature. Electronics, 2021, 10(12): 1497.

[51]

ZHANG S, ZHAI B, GUO X, et al. Synchronous estimation of the state of health and remaining useful lifetime for lithium-ion battery using the incremental capacity and artificial neural networks. Journal of Energy Storage, 2019, 26: 100951.

[52]

GOU B, XU Y, FENG X. An ensemble learning-based data-driven method for online state-of-health estimation of lithium-ion batteries. IEEE Transactions on Transportation Electrification, 2021, 7(2): 422-436.

[53]

LIU G F, ZHANG X W, LIU Z M. State of health estimation of power batteries based on multi-feature fusion models using stacking algorithm. Energy, 2022, 259:124851.

[54]

GUO Y, HUANG K, YU X, et al. State-of-health estimation for lithium-ion batteries based on historical dependency of charging data and ensemble SVR. Electrochimica Acta, 2022, 428: 140940.

[55]

ZHANG B F, XU G, LIU J Y, et al. State-of-health estimation for lithium-ion batteries based on partial charging segment and stacking model fusion. Energy Science Engineering, 2023, 11(1): 383–397.

Journal of Measurement Science and Instrumentation
Pages 397-407
Cite this article:
HUI Z, WANG R, FENG N, et al. State of health prediction for lithium-ion batteries based on ensemble Gaussian process regression. Journal of Measurement Science and Instrumentation, 2024, 15(3): 397-407. https://doi.org/10.62756/jmsi.1674-8042.2024041

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Received: 16 May 2024
Revised: 02 September 2024
Accepted: 10 September 2024
Published: 30 September 2024
© The Author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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