Fractional Extended and Unscented Kalman Filtering for State of Charge Estimation of Lithium-Ion Batteries

Kupper, Martin; Funk, Christopher; Eckert, Marius; Hohmann, Sören

doi:10.23919/acc.2018.8430859

Cited by 6 publications

(17 citation statements)

References 28 publications

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“…The upper limit of the sum is denoted by the buffer length z = min(k + 1, l) due to the SMP. Note that the matrix T α k is often included inside of f k and w k [6].…”

Section: Fractional Calculusmentioning

confidence: 99%

“…As discussed in [25], a fractional order Kalman filter can compensate a missing or wrong initialization of the system. Furthermore, it is discussed in [6] that the initialization of a fractional order state-space model has only a small influence on the SOC estimation of a single cell. Therefore, an exact initialization will be neglected in this paper and the buffer of the cascaded fractional order Kalman filter (CFKF) will be initialized with zeros.…”

Section: Fractional Calculusmentioning

confidence: 99%

“…In recent times, the use of fractional order models was proposed for higher accuracy and a better representation of the physical phenomena of Li-ion cells [4][5][6]. Using noninteger order differential equations, fractional models describe reality better than conventional models of the same order as they model accurately the internal impedance and the electrochemical dynamics of a battery cell.…”

Section: Introductionmentioning

confidence: 99%

“…Using noninteger order differential equations, fractional models describe reality better than conventional models of the same order as they model accurately the internal impedance and the electrochemical dynamics of a battery cell. Hence, they are used more and more frequently in recent time [6][7][8][9][10][11][12]. As this model is easy to implement, the additional effort compared to integer order models is negligible and the results are convincing, the application for battery modeling is suitable.…”

Section: Introductionmentioning

confidence: 99%

“…As this model is easy to implement, the additional effort compared to integer order models is negligible and the results are convincing, the application for battery modeling is suitable. Corresponding estimation algorithms have also been proposed with fractional Luenberger-type observers [7] and fractional Kalman Filters [4,6].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Cascaded Fractional Kalman Filtering for State and Current Estimation of Large-Scale Lithium-Ion Battery Packs

Kupper

Brenneisen

Stark

et al. 2018

2018 Chinese Control and Decision Conference (CCDC)

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In this paper, a cascaded fractional Kalman filter for state of charge and branch current estimation of largescale battery systems is proposed. As a centralized approach for the estimation of a large-scale system is costly in terms of effort and time, a partition into smaller and, therefore, simpler subsystems is applied. Since the overall system is divided into smaller units, a local computation is allowed and complexity reduced. In these distributed systems, usually, the subsystems communicate with each other to exchange relevant data. Using a model based on mesh currents, we receive a cascaded system structure which results in a hierarchical arrangement of all subsystems. This concludes in a one-directional information flow and, therefore, reduces the overall communication effort. Using this proposed approach, it is not only possible to estimate the states of each branch locally but also to calculate the branch currents when the total current is known. Finally, a practical test with real measurement data is presented.

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“…The upper limit of the sum is denoted by the buffer length z = min(k + 1, l) due to the SMP. Note that the matrix T α k is often included inside of f k and w k [6].…”

Section: Fractional Calculusmentioning

confidence: 99%

Section: Fractional Calculusmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Cascaded Fractional Kalman Filtering for State and Current Estimation of Large-Scale Lithium-Ion Battery Packs

Kupper

Brenneisen

Stark

et al. 2018

2018 Chinese Control and Decision Conference (CCDC)

Self Cite

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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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Design of a Robust State Estimator for a Discrete-Time Nonlinear Fractional-Order System With Incomplete Measurements and Stochastic Nonlinearities

Zheng

2020

IEEE Access

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In the application of navigation system, networked system, and manufacturing process, incomplete data is unavoidable, which may reduce the performance and stability of the systems. It is a crucial and challenging task when the nonlinear fractional-order system is under incomplete data. As a kind of incomplete data, missing measurements assume that the missing rates of multiple sensors are independent of each other. In order to provide a more reliable and robust state estimation algorithm, a nonlinear fractional-order Kalman filtering algorithm considering both the missing measurements and stochastic nonlinearities is proposed in this paper. Then, the convergence and stability of the proposed filter are analyzed. In addition, sufficient conditions have been investigated to guarantee the stochastic stability. Finally, the effectiveness of the state estimator is verified by two numerical examples.

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Fractional Extended and Unscented Kalman Filtering for State of Charge Estimation of Lithium-Ion Batteries

Cited by 6 publications

References 28 publications

Cascaded Fractional Kalman Filtering for State and Current Estimation of Large-Scale Lithium-Ion Battery Packs

Cascaded Fractional Kalman Filtering for State and Current Estimation of Large-Scale Lithium-Ion Battery Packs

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

Design of a Robust State Estimator for a Discrete-Time Nonlinear Fractional-Order System With Incomplete Measurements and Stochastic Nonlinearities

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