Online estimation of lithium-ion battery equivalent circuit model parameters and state of charge using time-domain assisted decoupled recursive least squares technique

Pai, Hung Yu; Liu, Yi Hua; Ye, Song Pei

doi:10.1016/j.est.2023.106901

Cited by 9 publications

(4 citation statements)

References 29 publications

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“…The accuracy advantage of the n-RC model is proved, the accuracy of the extended Kalman filte(EKF) and AUKF algorithms were compared, experimental results showed that the AUKF algorithm achieved higher accuracy. In [14] , the combination of Decoupled Recursive Least Squares (RLS) technique and Time-Domain Parameter Extraction enables both ECM parameter estimation, In addition, a compensation term for DC resistance is proposed, which greatly improves the accuracy of SOC estimation. In [15] , a novel Adaptive Extended Kalman Filter (AEKF) combined with a parameter identification algorithm RLS is introduced as another SOC estimation method, the tests demonstrate the accurate estimation of battery SOC and the robustness.…”

Section: Introductionmentioning

confidence: 99%

Jitter solution in parameter identification based on cross-time scale fusion algorithm of lithium-ion batteries

Su,

Ge,

Qiao

2024

Heliyon

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Section: Introductionmentioning

confidence: 99%

Jitter solution in parameter identification based on cross-time scale fusion algorithm of lithium-ion batteries

Su,

Ge,

Qiao

2024

Heliyon

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“…The model selection is essential since the correctness of the model significantly affects SOC estimation. Incorporating electrical components like resistors and capacitors, the equivalent-circuit (EC) model [22,23] reveals the characteristics of LIBs. Since the fractional-order (FO) models [24,25] of LIBs are more precise than integer-order models in characterizing concentration polarization, the FO models are frequently employed for model-based SOC estimations.…”

Section: Introductionmentioning

confidence: 99%

An Initial Value Problem for State of Charge Estimation of Lithium‐Ion Batteries with an Adaptive Fractional‐Order Unscented Particle Filter

Jiao,

Gao,

Chai

et al. 2023

Advcd Theory and Sims

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This paper proposes an adaptive fractional‐order unscented particle filter (AFOUPF) with the initial value compensation (IVC) to enhance the estimation accuracy of the state of charge (SOC) for lithium‐ion batteries (LIBs). First, to correctly reflect the dynamic properties of LIBs, a fractional‐order system (FOS) with a constant phase component is constructed. Second, to discretize the FOS equation of LIBs, the Grünwald–Letnikov difference, Caputo derivative and Rieman–Liouville difference are employed to establish the corresponding difference equation. Third, a map function is applied to keep the order and SOC of the FOS within an appropriate interval. Further, an approach of IVC is provided for the AFOUPF to increase the accuracy of SOC estimation, taking into account that the accuracy of SOC estimation is impacted if the order of the FOS is relatively small in (0, 1). By utilizing the augmented vector approach, the simultaneous estimations of order, parameters, initial value, and SOC are resolved. Besides, an iterative approach that accommodates the noise covariance matrices is proposed to improve the estimation accuracy. The AFOUPF conducts an unscented transform and resampling on each particle, resulting in a high SOC estimation accuracy in complicated situations. Finally, the availability of AFOUPF is tested by several experiments.

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“…A classical RC branch is usually used in ECM, which are integer-order models, to simulate battery polarization effects. 27,28 Still, fractional-order model (FOM) 29,30 is more accurate than integer-order model at describing the concentration polarization of LIBs. Numerous academics have successfully used FOM to estimate the SOC of LIBs in recent years.…”

Section: Introductionmentioning

confidence: 99%

“…The electrical components of the equivalent circuit model (ECM) 24–26 imitate the transient and steady‐state properties of LIBs. A classical RC branch is usually used in ECM, which are integer‐order models, to simulate battery polarization effects 27,28 . Still, fractional‐order model (FOM) 29,30 is more accurate than integer‐order model at describing the concentration polarization of LIBs.…”

Section: Introductionmentioning

confidence: 99%

An improved fractional‐order state estimation algorithm based on an unscented particle filter for state of charge estimation of lithium‐ion batteries with adaptive estimations of unknown parameters

Jiao,

Gao,

Chai

et al. 2023

Circuit Theory & Apps

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SummaryAn adaptive fractional‐order unscented particle filter (FOUPF) and an adaptive FOUPF with the estimations of the noise covariance matrices (NCMs) are proposed to estimate the state of charge (SOC) of lithium‐ion batteries (LIBs) in this study. The Grünwald–Letnikov difference is employed to establish the corresponding discrete‐time equation of the fractional‐order system (FOS). The order and SOC of FOS of LIBs are controlled within an appropriate interval by a mapping function. In order to achieve a joint estimation of order and unknown parameters for a FOS, an augmented vector method is applied in this study. Compared with the adaptive fractional‐order unscented Kalman filter and adaptive fractional‐order cubature Kalman filter, the proposed adaptive FOUPF has a higher accuracy for estimating SOC. Besides, an iterative approach that accommodates the NCMs is proposed to improve the estimation accuracy of SOC. Finally, the availability of the proposed algorithms is tested by several experiments.

show abstract

Online estimation of lithium-ion battery equivalent circuit model parameters and state of charge using time-domain assisted decoupled recursive least squares technique

Cited by 9 publications

References 29 publications

Jitter solution in parameter identification based on cross-time scale fusion algorithm of lithium-ion batteries

Jitter solution in parameter identification based on cross-time scale fusion algorithm of lithium-ion batteries

An Initial Value Problem for State of Charge Estimation of Lithium‐Ion Batteries with an Adaptive Fractional‐Order Unscented Particle Filter

An improved fractional‐order state estimation algorithm based on an unscented particle filter for state of charge estimation of lithium‐ion batteries with adaptive estimations of unknown parameters

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