Parameter Identification and SOC Estimation of a Battery Under the Hysteresis Effect

Kwak, Minkyu; Lkhagvasuren, Bataa; Park, Jinsoo; You, Jin-Ho

doi:10.1109/tie.2019.2956394

Cited by 59 publications

(27 citation statements)

References 19 publications

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“…After determining OCV − f (SOC) for known SOC, the parameters M , M 0 We observe that for the case T = 25 • C the instantaneous hysteresis parameter M 0 is estimated significantly compared with that reported in [19]. This is because the rectangle rule for integral used in (9) captures the instantaneous hysteresis more efficiently, whereas the trapezoidal rule for integral was used instead in [19]. Another effect observed in Table II is hysteresis level for T = 5 • C is higher than for T = 25 • C, which is expected, since the battery dynamics is more severe in cooler temperatures.…”

Section: Test With Lithium-ion "E2" Cellmentioning

confidence: 64%

“…Starting from the end of rest time, the parameter estimation algorithm (10), ( 11), ( 13), ( 14) and ( 15) is executed with 800 as the size of time window and R 0 , R, C, OCV are estimated. After determining OCV − f (SOC) for known SOC, the parameters M , M 0 We observe that for the case T = 25 • C the instantaneous hysteresis parameter M 0 is estimated significantly compared with that reported in [19]. This is because the rectangle rule for integral used in (9) captures the instantaneous hysteresis more efficiently, whereas the trapezoidal rule for integral was used instead in [19].…”

Section: Test With Lithium-ion "E2" Cellmentioning

confidence: 78%

“….., k 0 + n. In the work [19], trapezoidal rule for integral was used for coulomb counting. The reason that rectangle rule for integral was used in this paper is the equation ( 9) is compatible with the state equation in KF-based SOC estimation.…”

Section: Battery Model and Parameter Identificationmentioning

confidence: 99%

“…Consequently, it says instantaneous hysteresis could have different characteristics depending on the current history. Nevertheless, it's effect on overall SOC estimation is very little and some comparisons were made in [19]. In Fig.…”

Section: Test With Nmc532/graphite Pouch Cellmentioning

confidence: 99%

“…By proposing a special discretization for battery's dynamics equations, the authors in [19] extracted the battery's electrical model parameters more efficiently and the initial value of SOC in each time window was found by a nonlinear optimization solver. In this paper, we introduce a refined version of the discretization used in [19] and execute KFs to estimate SOC. This combined estimation scheme, in which the initial value of OCV is computed window-wise and the parameter variability is taken into account, yields more accurate results.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A variable-length window-wise parameter-dependent state of charge estimation by Kalman filters

Lkhagvasuren¹,

Kwak²,

Jin³

et al. 2021

Preprint

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<div>This paper proposes a new window-wise state of charge (SOC) estimation algorithm based on Kalman filters (KF). In the first stage, the equivalent circuit model's parameters are estimated by a least square estimation window-wise, assuming a linear SOC and open-circuit voltage (OCV) relation. The algorithm accurately estimates the parameters and observes the changes that depend on SOC. Moreover, based on the estimated parameters, the OCV values are identified. In the next stage, window-wise linear Kalman filter(ES-LKF) without hysteresis and extended Kalman filter (ES-EKF) and sigma-point Kalman filter (ES-SPKF) algorithm with hysteresis are executed to estimate SOC. Having fewer state equations and hysteresis parameters tuned up in an off-line way, the ES-EKF and ES-SPKF perform better than the algorithms considered in previous works. The algorithms are validated by experiments with real data obtained from lab tests.</div>

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Section: Test With Lithium-ion "E2" Cellmentioning

confidence: 64%

Section: Test With Lithium-ion "E2" Cellmentioning

confidence: 78%

Section: Battery Model and Parameter Identificationmentioning

confidence: 99%

Section: Test With Nmc532/graphite Pouch Cellmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A variable-length window-wise parameter-dependent state of charge estimation by Kalman filters

Lkhagvasuren¹,

Kwak²,

Jin³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Online state‐of‐charge estimation by modified Coulomb counting method based on the estimated parameters of lithium‐ion battery

Das,

Samanta,

Gupta

2023

Circuit Theory & Apps

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SummaryThe state‐of‐charge (SOC) of the battery is the most critical parameter for an electric vehicle (EV) dashboard. Generally, the Coulomb counting (CC) method is used for SOC estimation, but the conventional CC method has some errors due to initial SOC determination. This work uses battery modeling and parameter estimation of a lithium‐iron‐phosphate (LFP) battery to determine the initial SOC. The equivalent circuit models (ECMs) have been used for battery parameters estimation by the least‐squares estimation (LSE) algorithm. This algorithm uses only experimental terminal voltage and current data from Arbin battery testing system. The ECM parameters vary with battery SOC, and the differential capacity plot has explained the variation. SOC can be estimated using estimated parameters and the relationship between SOC and open‐circuit voltage (OCV). Additionally, a modified CC method is proposed to solve the problem of estimating initial SOC by determining an OCV‐SOC relation using experimental data. The modeling of the battery is validated by comparing ECMs with physical battery terminal voltage profiles.

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An improved proportional control forgetting factor recursive least square-Monte Carlo adaptive extended Kalman filtering algorithm for high-precision state-of-charge estimation of lithium-ion batteries

Zhu

Wang

et al. 2023

J Solid State Electrochem

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Parameter Identification and SOC Estimation of a Battery Under the Hysteresis Effect

Cited by 59 publications

References 19 publications

A variable-length window-wise parameter-dependent state of charge estimation by Kalman filters

A variable-length window-wise parameter-dependent state of charge estimation by Kalman filters

Online state‐of‐charge estimation by modified Coulomb counting method based on the estimated parameters of lithium‐ion battery

An improved proportional control forgetting factor recursive least square-Monte Carlo adaptive extended Kalman filtering algorithm for high-precision state-of-charge estimation of lithium-ion batteries

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