On the relative contributions of bias and noise to lithium-ion battery state of charge estimation errors

Mendoza, S.; Liu, Ji; Mishra, Partha; Fathy, Hosam K.

doi:10.1016/j.est.2017.01.006

Cited by 27 publications

(7 citation statements)

References 18 publications

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“…Integ.Error (37) where the integration error is incorporated based on (30). SOC at time instant k = 0, 1, 2 .…”

Section: Effect Of Approximating Current Integrationmentioning

confidence: 99%

“…Generate a perfectly integrable current, where the generated current allows one to perfectly compute k+1 k i(k)dk shown in (30). -First 18 s of the noiseless current profile i(k) is shown in red in Figure 7.…”

Section: Effect Of Current Integration Errormentioning

confidence: 99%

“…In [19], the battery SOC estimation error was derived theoretically as a function of sensor noises; the proposed approach considers measurement noise in both the current and voltage. The effect of different components involved in SOC estimation were demonstrated, using a parameter sensitivity analysis in [29], and the effect of bias and noise were reported in [30] as well.…”

Section: Introductionmentioning

confidence: 99%

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A Critical Look at Coulomb Counting Approach for State of Charge Estimation in Batteries

et al. 2021

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In this paper, we consider the problem of state-of-charge estimation for rechargeable batteries. Coulomb counting is a well-known method for estimating the state of charge, and it is regarded as accurate as long as the battery capacity and the beginning state of charge are known. The Coulomb counting approach, on the other hand, is prone to inaccuracies from a variety of sources, and the magnitude of these errors has not been explored in the literature. We formally construct and quantify the state-of-charge estimate error during Coulomb counting due to four types of error sources: (1) current measurement error; (2) current integration approximation error; (3) battery capacity uncertainty; and (4) timing oscillator error/drift. It is demonstrated that the state-of-charge error produced can be either time-cumulative or state-of-charge-proportional. Time-cumulative errors accumulate over time and have the potential to render the state-of-charge estimation utterly invalid in the long term.The proportional errors of the state of charge rise with the accumulated state of charge and reach their worst value within one charge/discharge cycle. The study presents methods for reducing time-cumulative and state-of-charge-proportional mistakes through simulation analysis.

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“…Integ.Error (37) where the integration error is incorporated based on (30). SOC at time instant k = 0, 1, 2 .…”

Section: Effect Of Approximating Current Integrationmentioning

confidence: 99%

Section: Effect Of Current Integration Errormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Critical Look at Coulomb Counting Approach for State of Charge Estimation in Batteries

et al. 2021

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“…In this way, the computing cost is significantly reduced, but the estimation accuracy of SOC is also decreased when a constant bias exists. Mendoza et al quantified the SOC estimation errors and classified them into an average bias plus estimation noise. The bias affects the SOC estimation accuracy more than the estimation noise.…”

Section: Introductionmentioning

confidence: 99%

Two‐layer online state‐of‐charge estimation of lithium‐ion battery with current sensor bias correction

Feng

et al. 2019

Int J Energy Res

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Summary Because of the harsh working condition in electrified vehicles, the measured current and voltage signals typically contain non‐ignorable noises and bias, which potentially decline the accuracy of state‐of‐charge estimation. In this regard, the noise and bias corruption should be well addressed to maintain sufficient accuracy and robustness. This paper improves the existing methods in the literature from two aspects: (a) A novel offset‐free equivalent circuit model is developed to remove the current bias; and (b) based on the offset‐free equivalent circuit model, a two‐layer estimator is proposed to estimate the state of charge using real‐time identified model parameters. The robustness of the two‐layer estimator against model uncertainties and the aging effect is further evaluated. Simulation and experimental results show that the proposed two‐layer estimator can effectively attenuate the current bias and estimate the state of charge accurately with the error confined to ±4% under different levels of current bias and model uncertainties.

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“…System noise is unpredictable and therefore SOC estimation is highly complex [9][10][11]. Each measured variable associated with SOC estimation has an associated deviation from its true value and the separation of noise or calculation of noise has been the focus of many investigations.…”

Section: Introductionmentioning

confidence: 99%

A State of Charge Estimation Method Based on Adaptive Extended Kalman-Particle Filtering for Lithium-ion Batteries

Xia

Guo

Wang³

et al. 2018

Energies

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A state of charge (SOC) estimation method is proposed. An Adaptive Extended Kalman Particle filter (AEKPF) based on Particle Filter (PF) and Adaptive Kalman Filter (AKF) is used in order to decrease the error and reduce calculations. The second-order resistor-capacitor (RC) Equivalent Circuit Model (ECM) is used to identify dynamic parameters of the battery. After testing (include Dynamic Stress test (DST), New European Driving Cycle (NEDC), Federal Urban Dynamic Schedule (FUDS), Urban Dynamometer Driving Schedules (UDDS), etc.) at different temperatures and times, it was found that the AEKPF exhibits greater tolerance for high system noise (10% or higher) and provides more accurate estimations under common operating conditions.

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On the relative contributions of bias and noise to lithium-ion battery state of charge estimation errors

Cited by 27 publications

References 18 publications

A Critical Look at Coulomb Counting Approach for State of Charge Estimation in Batteries

A Critical Look at Coulomb Counting Approach for State of Charge Estimation in Batteries

Two‐layer online state‐of‐charge estimation of lithium‐ion battery with current sensor bias correction

A State of Charge Estimation Method Based on Adaptive Extended Kalman-Particle Filtering for Lithium-ion Batteries

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