Improved state of charge estimation for Li-ion batteries using fractional order extended Kalman filter

Mawonou, Kodjo Senou Rodolphe; Eddahech, Akram; Dumur, Didier; Beauvois, Dominique; Godoy, Emmanuel

doi:10.1016/j.jpowsour.2019.226710

Cited by 79 publications

(33 citation statements)

References 36 publications

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“…The overall research idea of calculating SOC of the battery on the basis of reduced‐order electrochemical model is shown in Figure . The ambient temperature, voltage, and current measurement signals are inputs, and the EKF algorithm is compiled based on the simplified electrochemical‐thermal coupling model . The algorithm estimates the concentration of lithium ion in the cathode solid phase, and then the SOC of the battery is calculated.…”

Section: Lithium Battery Modeling and Soc Estimationmentioning

confidence: 99%

Active equalization control strategy of Li‐ion battery based on state of charge estimation of an electrochemical‐thermal coupling model

Lin

Wang

et al. 2020

Int J Energy Res

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Summary Aiming at solving the problem of poor battery cell consistency caused by excessive decay of cell capacity or increased internal resistance during the operation of lithium‐ion battery packs for vehicles, the paper proposes an active equalization control with 12‐V power supply as an equalization energy source, which achieves efficient energy replenishment of individual cells with low power. The electrochemical‐thermal coupling model of lithium‐ion battery is built, and the order reduction of large‐scale system theory ensures that the model had higher accuracy and lower amount of calculation, which is suitable for vehicle battery management system (BMS). Then the extended Kalman filter algorithm is used to calculate the real‐time state of charge (SOC) of each cell and set as an equalization variable. The equalization simulation circuit is built with MATLAB/Simulink, the experimental platform of active equalization system for battery packs is constructed, and the battery packs are tested for equalization in static state. The simulation and experimental results show that the proposed active equalization control strategy can rapidly improve the voltage inconsistency between single cells, and the energy transfer efficiency can reach about 85% during the equalization process.

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Section: Lithium Battery Modeling and Soc Estimationmentioning

confidence: 99%

Active equalization control strategy of Li‐ion battery based on state of charge estimation of an electrochemical‐thermal coupling model

Lin

Wang

et al. 2020

Int J Energy Res

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“…Based on the PNGV model of Equation (16) and the Coulomb integral SoC calculation method, the state Equation of the power battery is obtained as follows [24]:…”

Section: Application Of Combination Algorithm In Battery Soc Estimationmentioning

confidence: 99%

Research on Parameter Self-Learning Unscented Kalman Filtering Algorithm and Its Application in Battery Charge of State Estimation

Liu

et al. 2020

Energies

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A novel state estimation algorithm based on the parameters of a self-learning unscented Kalman filter (UKF) with a model parameter identification method based on a collaborative optimization mechanism is proposed in this paper. This algorithm can realize the dynamic self-learning and self-adjustment of the parameters in the UKF algorithm and the automatic optimization setting Sigma points without human participation. In addition, the multi-algorithm collaborative optimization mechanism unifies a variety of algorithms, so that the identification method has the advantages of member algorithms while avoiding the disadvantages of them. We apply the combination algorithm proposed in this paper for state of charge (SoC) estimation of power batteries and compare it with other model parameter identification algorithms and SoC estimation methods. The results showed that the proposed algorithm outperformed the other model parameter identification algorithms in terms of estimation accuracy and robustness.

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“…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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Improved state of charge estimation for Li-ion batteries using fractional order extended Kalman filter

Cited by 79 publications

References 36 publications

Active equalization control strategy of Li‐ion battery based on state of charge estimation of an electrochemical‐thermal coupling model

Active equalization control strategy of Li‐ion battery based on state of charge estimation of an electrochemical‐thermal coupling model

Research on Parameter Self-Learning Unscented Kalman Filtering Algorithm and Its Application in Battery Charge of State Estimation

Stochastic filtering based transmissibility estimation of novel coronavirus

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