Optimizing Current Profiles for Efficient Online Estimation of Battery Equivalent Circuit Model Parameters Based on Cramer–Rao Lower Bound

Pillai, Prarthana; Sundaresan, Sneha; Pattipati, Krishna R.; Balasingam, Balakumar

doi:10.3390/en15228441

Cited by 5 publications

(3 citation statements)

References 31 publications

(37 reference statements)

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“…Table IX shows the estimated resistance, R0 , of each battery and compares them with those obtained by the Arbin battery testing system which utilizes a high-frequency signal at 1000 Hz to estimate the Ohmic resistance R Ω . It is reported in [15], [17] that resistance estimated using high-frequency signals returns the Ohmic resistance R Ω whereas that estimated using low-frequency signals return total resistance The observation always satisfy the expectation that R0 > RΩ .…”

Section: B Total Resistancesupporting

confidence: 53%

“…Further, the table also contains the values of the standard mean calculated by dividing the mean by the standard deviation. Based on the magnitude of the standard mean values and the inequality for significance in (15), it can be confirmed with 95% that the OCV uncertainty due to the C-Rate is also zero-mean Gaussian.…”

Section: Cycle-rate Errormentioning

confidence: 82%

“…Figure 13 shows the voltage and current across a battery that would be used in resistance estimation using the least squares approach. More details on the resistance estimation pulses are presented in [2] and more details on the estimation approach and the performance bound can be found in [15]. The time domain resistance estimation approach employed in this paper was evaluated based on a frequency domain approach in [16].…”

Section: B Total Resistancementioning

confidence: 99%

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Performance Comparison of Open-Circuit Voltage Modelling of Li-ion Batteries at Different C-Rates

Pillai

Nguyen

Balachandran

2023

2023 IEEE Transportation Electrification Conference &Amp; Expo (ITEC)

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This paper is the third part of a series of papers about empirical approaches to open circuit voltage (OCV) modeling of lithium-ion batteries. The first part of the series [1] proposed models to quantify various sources of uncertainties in the OCV models; and, the second part of the series [2] presented systematic data collection approaches to compute the uncertainties in the OCV-SOC models. This paper uses data collected from 28 OCV characterization experiments, performed according to the data collection plan presented in [2], to compute and analyze the following three different OCV uncertainty metrics: cell-to-cell variations, cycle-rate error, and curve fitting error. From the computed metrics, it was observed that a lower C-Rate showed smaller errors in the OCV-SOC model and vice versa. The results reported in this paper establish a relationship between the C-Rate and the uncertainty of the OCV-SOC model. This research can be thus useful to battery researchers for quantifying the tradeoff between the time taken to complete the OCV characterization test and the corresponding uncertainty in the OCV-SOC modeling. Further, quantified uncertainty model parameters can be used to accurately characterize the uncertainty in various battery management functionalities, such as state of charge and state of health estimation.

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Section: B Total Resistancesupporting

confidence: 53%

Section: Cycle-rate Errormentioning

confidence: 82%