Battery state-of-charge estimation using approximate least squares

Unterrieder, Christoph; Zhang, Cheng; Lunglmayr, Michael; Priewasser, Robert; Marsili, S.; Huemer, Mario

doi:10.1016/j.jpowsour.2014.12.035

Cited by 45 publications

(17 citation statements)

References 23 publications

(35 reference statements)

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“…The second type of constraints are given by (3). For each i ∈ {1, 2, ..., N x } the variance ofx i serves as a cost function which is a function of e i given as…”

Section: Derivationmentioning

confidence: 99%

Constrained Best Linear and Widely Linear Unbiased Estimation

Lang

Onic

Steindl

et al. 2018

2018 52nd Asilomar Conference on Signals, Systems, and Computers

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The least squares (LS) estimator and the best linear unbiased estimator (BLUE) are two well-studied approaches for the estimation of a deterministic but unknown parameter vector. In many applications it is known that the parameter vector fulfills some constraints, e.g., linear constraints. For such situations the constrained LS estimator, which is a simple extension of the LS estimator, can be employed. In this paper we derive the constrained version of the BLUE. It will turn out that the incorporation of the linear constraints into the derivation of the BLUE is not straight forward as for the constrained LS estimator, but the final expression for the constrained BLUE is closely related to that of the constrained LS estimator.

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“…The second type of constraints are given by (3). For each i ∈ {1, 2, ..., N x } the variance ofx i serves as a cost function which is a function of e i given as…”

Section: Derivationmentioning

confidence: 99%

Constrained Best Linear and Widely Linear Unbiased Estimation

Lang

Onic

Steindl

et al. 2018

2018 52nd Asilomar Conference on Signals, Systems, and Computers

View full text Add to dashboard Cite

show abstract

“…thus the least square method [36] is used to fit the terminal voltage U(t) of discharging process curve. With the exponential fitting, the time constants τ 1 and τ 2 can be determined.…”

Section: Rc Loop Identificationmentioning

confidence: 99%

A New State of Charge Estimation Algorithm for Lithium-Ion Batteries Based on the Fractional Unscented Kalman Filter

et al. 2017

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An accurate state of charge (SOC) estimation is the basis of the Battery Management System (BMS). In this paper, a new estimation method which considers fractional calculus is proposed to estimate the lithium battery state of charge. Firstly, a modified second-order RC model based on fractional calculus theory is developed to model the lithium battery characteristics. After that, a pulse characterization test is implemented to obtain the battery terminal voltage and current, in which the parameter identification is completed based on least square method. Furthermore, the proposed method based on Fractional Unscented Kalman Filter (FUKF) algorithm is applied to estimate the battery state of charge value in both static and dynamic battery discharging experiment. The experimental results have demonstrated that the proposed method shows high accuracy and efficiency for state of charge estimation and the fractional calculus contributes to the battery state of charge estimation.

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“…The most important index for a BMS is state of charge (SOC). Because of the inherently time-varying and non-linearity characteristics of LIB under working conditions, accurate estimation of SOC remains a challenge [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

A New Method for State of Charge Estimation of Lithium-Ion Batteries Using Square Root Cubature Kalman Filter

et al. 2018

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State of charge (SOC) is a key parameter for lithium-ion battery management systems. The square root cubature Kalman filter (SRCKF) algorithm has been developed to estimate the SOC of batteries. SRCKF calculates 2n points that have the same weights according to cubature transform to approximate the mean of state variables. After these points are propagated by nonlinear functions, the mean and the variance of the capture can achieve third-order precision of the real values of the nonlinear functions. SRCKF directly propagates and updates the square root of the state covariance matrix in the form of Cholesky decomposition, guarantees the nonnegative quality of the covariance matrix, and avoids the divergence of the filter. Simulink models and the test bench of extended Kalman filter (EKF), Unscented Kalman filter (UKF), cubature Kalman filter (CKF) and SRCKF are built. Three experiments have been carried out to evaluate the performances of the proposed methods. The results of the comparison of accuracy, robustness, and convergence rate with EKF, UKF, CKF and SRCKF are presented. Compared with the traditional EKF, UKF and CKF algorithms, the SRCKF algorithm is found to yield better SOC estimation accuracy, higher robustness and better convergence rate.

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Battery state-of-charge estimation using approximate least squares

Cited by 45 publications

References 23 publications

Constrained Best Linear and Widely Linear Unbiased Estimation

Constrained Best Linear and Widely Linear Unbiased Estimation

A New State of Charge Estimation Algorithm for Lithium-Ion Batteries Based on the Fractional Unscented Kalman Filter

A New Method for State of Charge Estimation of Lithium-Ion Batteries Using Square Root Cubature Kalman Filter

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