Online estimation of state of health for the airborne Li-ion battery using adaptive DEKF-based fuzzy inference system

Yang, Ke; Chen, Zewang; He, Zhijia; Wang, Youren; Zhou, Zhaihe

doi:10.1007/s00500-020-05101-5

Cited by 26 publications

(13 citation statements)

References 19 publications

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“…Random Forest (RF) algorithm presented by Kodjo S.R. et al, 12 adaptive dual EKF‐based fuzzy inference system (ADEKF‐FIS) algorithm presented by Yang Ke et al, 51 the result under the two operating conditions is shown in Table 4 MET (s) and convergence time (s) of the algorithms.…”

Section: Resultsmentioning

confidence: 99%

Novel co‐estimation strategy based on forgetting factor dual particle filter algorithm for the state of charge and state of health of the lithium‐ion battery

Ren

Wang

Huang

et al. 2021

Intl J of Energy Research

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Section: Resultsmentioning

confidence: 99%

Novel co‐estimation strategy based on forgetting factor dual particle filter algorithm for the state of charge and state of health of the lithium‐ion battery

Ren

Wang

Huang

et al. 2021

Intl J of Energy Research

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“…The data-driven approach includes methods, where the previously collected information about operation of the LIB is used to find the dependency between the collected information and C loss . Different algorithms such as Support Vector Regression algorithm , Fuzzy logic (Yang et al, 2020) and Neural Networks (Naha et al, 2020) were investigated for C loss estimation. These methods allows to estimate the C loss with good accuracy in case of the availability of a sufficient amount of previously collected data.…”

Section: Related Workmentioning

confidence: 99%

ECMS 2021 Proceedings edited by Khalid Al-Begain, Mauro Iacono, Lelio Campanile, Andrzej Bargiela

2021

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This research paper discusses the application of multiagent simulation model of production processes by reference to milk receiving and storage. The paper describes basic parameters for general models, and presents the experiment results obtained during the model processing, as well as the approach towards implementation of the multiagent system employing AnyLogic simulation environment. The introduction of general technique to plot multiagent simulation models allows to use digital tools to create a virtual copy of the true-life processes with the possibility to provide forecast and identification of the food production industries. The employment of simulation modeling enables refinement of the production process under study, identification of its weaknesses, and provision of the expert opinion on improvement of production processes and as regards the results of virtual testing thereof.

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“…Lithium-ion batteries are highly electrochemically non-linear, susceptible to many factors such as operating temperature, charge/discharge current and charge/discharge depth during operation [3]. With no real-time condition monitoring and behavior control, batteries will deteriorate rapidly and more seriously, and even explode, causing unexpected hazards [4][5][6]. As a result, the key technology in doing lithium-ion battery research covers effectively improving the efficiency of lithium-ion batteries, expanding the driving range within a charging cycle, extending the service life of the lithium-ion battery pack, avoiding the battery failures and some other problems [7].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive State of Charge Estimation Method of Lithium-ion Battery Based on Residual Constraint Fading Factor Unscented Kalman Filter

et al. 2022

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It is crucial to conduct highly accurate estimation of the state of charge (SOC) of lithium-ion batteries during the real-time monitoring and safety control. Based on residual constraint fading factor unscented Kalman filter, the paper proposes an SOC estimation method to improve the accuracy of online estimating SOC. A priori values of terminal voltage were fitted using cubic Hermite interpolation. In combination with the Thevenin equivalent circuit model, the method of adaptive forgetting factor recursive least squares is used to identify the model parameters. To address the problem that the UKF method is strongly influenced by system noise and observation noise, the paper proposes an improved method of residual constrained fading factor. Finally, the effectiveness of this method was verified by the test of Hybrid Pulse Power Characteristic and Beijing Bus Dynamic Stress Test. Results show that under HPPC conditions, compared with other methods, the algorithm in the paper estimates that the SOC error of the battery remains between -0.38% and 0.948%, reducing the absolute maximum error by 51.5% at least and the average error by 62.7% at least. Moreover, under the condition of Beijing Bus Dynamic Stress Test the algorithm estimates the SOC error of the battery stays between -0.811% and 0.526%, and the SOC estimation errors are all within 0.2% after ten seconds of operation. Compared with other methods, the absolute maximum error can be reduced by 42.7% at least and the average error is reduced by 95% at least. And the test proves that the method is of higher accuracy, better convergence and stronger robustness.INDEX TERMS lithium-ion battery, state of charge estimation, Residual constraint fading factorunscented Kalman filter, adaptive forgetting factor recursive least square, cubic Hermite interpolation.

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Online estimation of state of health for the airborne Li-ion battery using adaptive DEKF-based fuzzy inference system

Cited by 26 publications

References 19 publications

Novel co‐estimation strategy based on forgetting factor dual particle filter algorithm for the state of charge and state of health of the lithium‐ion battery

Novel co‐estimation strategy based on forgetting factor dual particle filter algorithm for the state of charge and state of health of the lithium‐ion battery

ECMS 2021 Proceedings edited by Khalid Al-Begain, Mauro Iacono, Lelio Campanile, Andrzej Bargiela

An Adaptive State of Charge Estimation Method of Lithium-ion Battery Based on Residual Constraint Fading Factor Unscented Kalman Filter

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