Novel Polarization Voltage Model: Accurate Voltage and State of Power Prediction

Peng, Lin; Wang, Zhenpo; Jin, Peng; Hong, Jichao

doi:10.1109/access.2020.2993930

Cited by 6 publications

(4 citation statements)

References 53 publications

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“…Battery parameters vary greatly between charging and discharging and are reflected in the battery model parameters. Especially for OCV, the OCV in charge state and discharge state is inconsistent, which is called hysteresis characteristic 75,76 . The OCV identified by the MRAS also reflects the hysteresis characteristics of the batteries, as illustrated in Figure 11.…”

Section: Resultsmentioning

confidence: 99%

“…Especially for OCV, the OCV in charge state and discharge state is inconsistent, which is called hysteresis characteristic. 75,76 The OCV identified by the MRAS also reflects the hysteresis characteristics of the batteries, as illustrated in Figure 11. The MRAS can reasonably identify the PNGV model parameters in terms of identification accuracy and hysteresis characteristics of the OCV.…”

Section: Real-time Soc Estimationmentioning

confidence: 97%

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Real‐time identification of partnership for a new generation of vehicles battery model parameters based on the model reference adaptive system

et al. 2021

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Partnership for a new generation of vehicles (PNGV) model is a conventional battery equivalent circuit model (ECM). However, identifying the best parameters for this model is a challenge. In this study, the PNGV model is transformed into a directly identifiable difference equation to identify its parameters. Subsequently, the model reference adaptive system (MRAS) is used to realize the real-time identification of the model parameters. The identification accuracy of the MRAS is found to be superior to that of the recursive extended least square algorithm. For a single hybrid pulse power characterization (HPPC), the PNGV model identified by the MRAS can achieve a high-precision terminal voltage estimation. For lithium iron phosphate, lithium titanate, and nickel-metal hydride batteries, the root mean square errors are 0.024, 0.048, and 0.020 V, respectively. Besides, the real-time state of charge (SOC) estimation can be realized by the identified open-circuit voltage (OCV). The average errors of the three batteries are only −0.02, −0.01, and −0.01, respectively. Since the PNGV model has a capacity of describing the change of OCV with the current accumulation effect, the model is only suitable for simulating single HPPC or positive-negative pulses with equal amplitude and not for other current pulses. This is a major drawback of the PNGV model. The real-time PNGV model parameters identification method proposed in this study can provide a solid foundation for various state estimation of a battery. Novelty Statement• Transformation of the PNGV model into a difference equation that can be directly identified.• Real-time identification of the PNGV model parameters via the MRAS.• The identified OCV realized the real-time SOC estimation and discussed the deficiencies of the PNGV model.

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Section: Resultsmentioning

confidence: 99%

Section: Real-time Soc Estimationmentioning

confidence: 97%

Real‐time identification of partnership for a new generation of vehicles battery model parameters based on the model reference adaptive system

et al. 2021

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“…For this purpose, various studies have proposed online estimation algorithms for maximum peak current estimation [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. In particular, Z. Wei et al [15] compensated and attenuated the inevitable noise effect on the maxim peak current estimation.…”

Section: Introductionmentioning

confidence: 99%

Online Cell Screening Algorithm for Maximum Peak Current Estimation of a Lithium-Ion Battery Pack for Electric Vehicles

2022

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In this study, an online cell screening algorithm is proposed to estimate the maximum peak current considering the cell inconsistencies in battery packs for electric vehicles. Based on the equivalent circuit model, the maximum peak current is mathematically defined, and the inconsistency parameters affecting the maximum peak current are analyzed. The proposed algorithm compares the inconsistency parameters of each cell and subsequently selects a cell or a group of cells whose voltage can exceed the allowable voltage range. The maximum peak current is determined based on the selected cells, while ensuring that all the cells are charged and discharged within the allowable voltage range. The feasibility and superiority of the proposed algorithm are verified through an experiment conducted on a commercially manufactured battery pack for electric vehicles.

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“…In [12,13], the first-order equivalent circuit model is used by the authors to estimate the SOP. In [14][15][16][17][18][19], the second-order Thevenin equivalent model is used by the authors to estimate SOP, and an accuracy of 7.2% is obtained.…”

Section: Introductionmentioning

confidence: 99%

State of Power Estimation of Echelon-Use Battery Based on Adaptive Dual Extended Kalman Filter

Hou

Qiao

et al. 2021

Energies

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Owing to the degradation of the performance of a retired battery and the unclear initial value of the state of charge (SOC), the estimation of the state of power (SOP) of an echelon-use battery is not accurate. An SOP estimation method based on an adaptive dual extended Kalman filter (ADEKF) is proposed. First, the second-order Thevenin equivalent model of the echelon-use battery is established. Second, the battery parameters are estimated by the ADEKF: (a) the SOC is estimated based on an adaptive extended Kalman filtering algorithm, that uses the process noise covariance and observes the noise covariance , and (b) the ohmic internal resistance and actual capacity are estimated based on the aforementioned algorithm, that uses the process noise covariance and observes the noise covariance . Third, the working voltage and internal resistance are predicted using optimal estimation, and the SOP of the echelon-use battery is estimated. MATLAB simulation results show that, regardless of whether or not the initial value of the SOC is clear, the proposed algorithm can be adjusted to the adaptive algorithm, and if the estimation accuracy error of the echelon-use battery SOP is less than 4.8%, it has high accuracy. This paper provides a valuable reference for the prediction of the SOP of an echelon-use battery, and will be helpful for understanding the behavior of retired batteries for further discharge and use.

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Novel Polarization Voltage Model: Accurate Voltage and State of Power Prediction

Cited by 6 publications

References 53 publications

Real‐time identification of partnership for a new generation of vehicles battery model parameters based on the model reference adaptive system

Real‐time identification of partnership for a new generation of vehicles battery model parameters based on the model reference adaptive system

Online Cell Screening Algorithm for Maximum Peak Current Estimation of a Lithium-Ion Battery Pack for Electric Vehicles

State of Power Estimation of Echelon-Use Battery Based on Adaptive Dual Extended Kalman Filter

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