Abstract
The quick and accurate estimation of the state of health (SOH) of Li-ion battery is a technical difficulty in battery management system research. For the low accuracy of Li-ion battery SOH estimation under complex stress conditions, an estimation method of SOH for Li-ion battery using the adaptive dual extended Kalman filter-based fuzzy inference system (ADEKF-FIS) is proposed. First, Li-ion battery SOH is online estimated by dual extended Kalman filter. Then the Sage–Husa adaptive algorithm and the fuzzy controller are used to correct the state noise covariance and the observed noise covariance, respectively. The algorithm is flat on the state variance and the noise variance. The recursive estimation of the square root ensures the symmetry and nonnegative nature of the state and noise variance. In the end, this paper performing the dynamic stress test condition experiment for confirmation. Experimental results show that, compared with the EKF algorithm, ADEKF-FIS algorithm can obtain state of charge estimation with higher accuracy, which further improves the prediction accuracy of SOH and makes this algorithm have higher accuracy and better convergence.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Andre D, Appel C, Soczka-Guth T, Sauer DU (2013) Advanced mathematical methods of SOC and SOH estimation for lithium-ion batteries. J Power Sour 224:20–27
Bressel M (2015) Fuel cells remaining useful life estimation using an extended Kalman Filter. In: Industrial electronics society conference of the IEEE IEEE, pp 000469–000474
Castano S, Gauchia L, Voncila E, Sanz J (2015) Dynamical modeling procedure of a Li-ion battery pack suitable for real-time applications. Energy Convers Manage 92:396–405
Chaoui H, Ibe-Ekeocha CC, Gualous H (2017) Aging prediction and state of charge estimation of a LiFePO4 battery using input time-delayed neural networks. Electr Power Syst Res 146:189–197
Chengcheng X, Shunli W (2015) Airborne lithium battery health assessment and management approach and technology study. Chinese Journal of Power Sources 39(10):2110–2112+2330
Dai H, Xu T, Zhu L, Wei X, Sun Z (2016) Adaptive model parameter identification for large capacity Li-ion batteries on separated time scales. Appl Energy 184:119–131
Einhorn M, Conte FV, Kral C, Fleig J (2013) Comparison, selection, and parameterization of electrical battery models for automotive applications. IEEE Trans Power Electron 28(3):1429–1437
Gang S, Wei Z, Zhonghua H, Shanshan L (2016) Estimation of battery SOC based on improved EKF algorithm. In: Information technology, networking electronic and automation control conference, IEEE, pp 151–154
Hua Y, Cordoba-Arenas A, Warner N (2015) A multi time-scale state-of-charge andstate-of-health estimation framework using nonlinear predictive filter for lithium-ion battery pack with passive balance control. J Power Sour 280:293–312
Jaguemont J, Boulon L, Dube Y (2016) Characterization and modeling of a hybrid-electric-vehicle lithium-ion battery pack at low temperatures. IEEE Trans Veh Technol 65(1):1–14
Jia B, Guan Y, Wu L (2019) A state of health estimation framework for lithium-ion batteries using transfer components analysis. Control Energ 12(13):2524
Kim J, Lee S, Cho BH (2012) Complementary cooperation algorithm based on DEKF combined with pattern recognition for SOC/capacity estimation and SOH prediction. IEEE Trans Power Electron 27(1):436–451
Lal N, Kumar S, Chaurasiya VK (2018) An adaptive neuro-fuzzy inference system-based caching scheme for content-centric networking. Soft Comput 1:1–12
Liao L (2016) A hybrid framework combining data-driven and model-based methods for system remaining useful life prediction. Appl Soft Comput 44(C):191–199
Ramesh P, Nanda SR, Kulkarni V (2019) Application of neural-networks and neuro-fuzzy systems for the prediction of short-duration forces acting on the blunt bodies. Soft Comput 23(14):5725–5738
Shahriari M, Farrokhi M (2013) Online state-of-health estimation of VRLA batteries using state of charge. IEEE Trans Industr Electron 60(1):191–202
Venugopal P (2019) State-of-health estimation of li-ion batteries in electric vehicle using IndRNN under variable load condition. Energies 12(22):4338
Wladislaw A, Christian F, Uwe SD (2014) Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles. J Power Sour 258:321–339
Xiaotian W, Zhijia Y, Yingnan W (2013) Application of dual extended Kalman filtering algorithm in the state-of-charge estimation of lithium-ion battery. Chin J Sci Instrum 34(8):1732–1738
Yu K, Yang X, Cheng Y, Li C (2014) Thermal analysis and two-directional air flow thermal management for lithium-ion battery pack. J Power Sour 270:193–200
Yuan HF, Dung LR (2017) Offline state-of-health estimation for high-power lithium-ion batteries using three-point impedance extraction method. IEEE Trans Veh Technol 66(3):2019–2032
Yuan X, Liu Z, Lee ES (2011) Center-of-gravity fuzzy systems based on normal fuzzy implications. Comput Math Appl 61(9):2879–2898
Zadeh LA (1975) The concept of a linguistic variable and its application to approximate reasoning-I. Inf Sci 8(3):199–249
Acknowledgements
This work was supported by the Aviation Science Foundation of China (20183352030). It is also partly supported by the Foundation of the National Engineering and Research Center for Commercial Aircraft Manufacturing (SAMC14-JS-15-051).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Ethical approval
All applicable international, national, and/or institutional guidelines for the care and use of animals were followed.
Informed consent
Informed consent was obtained from all individual participants included in the study.
Additional information
Communicated by V. Loia.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Yang, K., Chen, Z., He, Z. et al. Online estimation of state of health for the airborne Li-ion battery using adaptive DEKF-based fuzzy inference system. Soft Comput 24, 18661–18670 (2020). https://doi.org/10.1007/s00500-020-05101-5
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-020-05101-5