Battery state-of-charge estimation using fractional extended Kalman filter with Mittag-Leffler memory

Hidalgo‐Reyes, J.I.; Gómez‐Aguilar, J.F.; Alvarado-Martínez, V.M.; López, G.; Escobar-Jiménez, R.F.

doi:10.1016/j.aej.2019.12.006

Cited by 21 publications

(10 citation statements)

References 23 publications

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“…The approach taken is very similar to that of SoC estimation in lithium-ion batteries. The SoC estimation is also tackled in [ 138 ] through an FOEKF filter. The study uses the Atangana–Baleanu fractional derivative to develop the fractional-order digital filter, which is further validated on a laboratory prototype with uplifting results.…”

Section: Applications Of Fractional-order Filtersmentioning

confidence: 99%

A Review of Recent Advances in Fractional-Order Sensing and Filtering Techniques

Muresan

Birs

Dulf

et al. 2021

Sensors

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The present manuscript aims at raising awareness of the endless possibilities of fractional calculus applied not only to system identification and control engineering, but also into sensing and filtering domains. The creation of the fractance device has enabled the physical realization of a new array of sensors capable of gathering more information. The same fractional-order electronic component has led to the possibility of exploring analog filtering techniques from a practical perspective, enlarging the horizon to a wider frequency range, with increased robustness to component variation, stability and noise reduction. Furthermore, fractional-order digital filters have developed to provide an alternative solution to higher-order integer-order filters, with increased design flexibility and better performance. The present study is a comprehensive review of the latest advances in fractional-order sensors and filters, with a focus on design methodologies and their real-life applicability reported in the last decade. The potential enhancements brought by the use of fractional calculus have been exploited as well in sensing and filtering techniques. Several extensions of the classical sensing and filtering methods have been proposed to date. The basics of fractional-order filters are reviewed, with a focus on the popular fractional-order Kalman filter, as well as those related to sensing. A detailed presentation of fractional-order filters is included in applications such as data transmission and networking, electrical and chemical engineering, biomedicine and various industrial fields.

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Section: Applications Of Fractional-order Filtersmentioning

confidence: 99%

A Review of Recent Advances in Fractional-Order Sensing and Filtering Techniques

Muresan

Birs

Dulf

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Furthermore, estimation and prediction of transmissibility of any disease can be investigated in a more generalized way using the fractional-order differential equations due to its property of huge global memory. Recently, fractional-order calculus theory has been used in the mathematical modeling of biological systems [35] , [36] , [37] , [38] , [39] , [40] , [41] , [42] , [43] as shown in Table 2 . Given the several advantages of FOC-based models in terms of the generalized and flexible solution with high accuracy, it is important to investigate the accuracy of these models for the COVID-19 case.…”

Section: Introductionmentioning

confidence: 99%

“… Epidemic model Rihan et al [37] , Rihan et al [38] , Latha et al [39] 3. State/parameter estimation Mawonou et al [40] , Hidalgo et al [41] , Wang et al [42] , Huang et al [43] …”

Section: Introductionmentioning

confidence: 99%

Stochastic filtering based transmissibility estimation of novel coronavirus

Bansal

Kumar

Singh

et al. 2021

Digital Signal Processing

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In this study, the transmissibility estimation of novel coronavirus (COVID-19) has been presented using the generalized fractional-order calculus (FOC) based extended Kalman filter (EKF) and wavelet transform (WT) methods. Initially, the state-space representation for the bats-hosts-reservoir-people (BHRP) model is obtained using a set of fractional order differential equations for the susceptible-exposed-infectious-recovered (SEIR) model. Afterward, the EKF and Kronecker product based WT methods have been applied to the discrete vector representation of the BHRP model. The main advantage of using EKF in this system is that it considers both the process and the measurement noise, which gives better accuracy and probable states, which are the Markovian (processes). The importance of proposed models lies in the fact that these models can accommodate conventional EKF and WT methods as their special cases. Further, we have compared the estimated number of contagious people and recovered people with the actual number of infectious people and recovered people in India and China.

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“…Compared to the integer-order EKF (IO-EKF), the FO-EKF shows higher accuracy under dynamic conditions. Similar studies for SOC estimation with FO-EKF are performed (Hidalgo-Reyes et al, 2020;Kupper et al, 2018;Mawonou et al, 2019); however, the (state and measurement) noise covariance remains a fixed value in the process of iterative calculation, which may cause significant estimation error or even divergence. To overcome the abovementioned issues, adaptive EKF (Zhu et al, 2019) is applied, and besides, the Fractional-Order UKF (FO-UKF) is employed for SOC estimation (Cai et al, 2017;Chen et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

State-of-charge estimation for Lithium-Ion batteries using Kalman filters based on fractional-order models

et al. 2021

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The accuracy of state of charge estimation results will directly affect the performance of battery management system. Due to such, we focus in this article on the SOC estimation of Lithium-Ion batteries based on a fractional second-order RC model with free noninteger differentiation orders. For such an estimation, three Kalman filters are employed: the adaptive extended Kalman filter (AEKF), extended Kalman filter (EKF), and Unscented Kalman Filter (UKF). The Fractional-Order Model (FOM) parameters and differentiation orders are identified by the Particle Swarm Optimization (PSO) algorithm, and a pulsed-discharge test is implemented to verify the accuracy of parameter identification. The output voltage error of the FOM model is much less than that of the Integer-Order Model (IOM). The FOM model has lower root-mean square error (RMSE), the mean absolute error (MAE), and the maximum absolute error (MAXAE) of SOC estimation than the IOM model during the SOC estimation regardless of AEKF, EKF or UKF. Experimental results show that the FOM can simulate the polarisation on effect and charge-discharge characteristics of the battery more realistically, demonstrating that the SOC estimation based on FOM is more accurate and promising than the one based on the IOM when using the same Kalman filters.

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Battery state-of-charge estimation using fractional extended Kalman filter with Mittag-Leffler memory

Cited by 21 publications

References 23 publications

A Review of Recent Advances in Fractional-Order Sensing and Filtering Techniques

A Review of Recent Advances in Fractional-Order Sensing and Filtering Techniques

Stochastic filtering based transmissibility estimation of novel coronavirus

State-of-charge estimation for Lithium-Ion batteries using Kalman filters based on fractional-order models

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