A graphical model for evaluating the status of series-connected lithium-ion battery pack

Feng, Xuning; Xu, Chang; He, Xiangming; Wang, Li; Gao, Shang; Ouyang, Minggao

doi:10.1002/er.4305

Cited by 24 publications

(7 citation statements)

References 45 publications

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“…However, the diffusion coefficient of active substances inside the current collector was inversely proportional to the DOD. An increase in the DOD reduced the amount of electrolyte, lithium ions, and other active substances, which resulted in an increased battery IR 26‐30 . The aforementioned condition may have increased the battery's IR and degraded its performance.…”

Section: Resultsmentioning

confidence: 99%

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State of health prediction model based on internal resistance

Zhang

Pan

et al. 2020

Int J Energy Res

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Summary The state of health (SOH) is a crucial indicator of lithium‐ion batteries. A battery cycle and calendar life are critical for electric vehicle batteries. Complex interactions occur between the SOH and internal resistance of a battery. In this study, several ternary lithium‐ion battery charge discharge experiments were performed to investigate the effects of the ambient temperature, discharge rate, and depth of discharge on a battery's internal resistance. An SOH prediction model was then constructed and used to evaluate the remaining capacity of the electric vehicle battery. The model was verified through various experiments, and a comparison of experimental and model‐derived data revealed a favorable agreement. Thus, the model accurately predicted the SOH of a ternary lithium‐ion battery.

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Section: Resultsmentioning

confidence: 99%

“…An increase in the DOD reduced the amount of electrolyte, lithium ions, and other active substances, which resulted in an increased battery IR. [26][27][28][29][30] The aforementioned condition may have increased the battery's IR and degraded its performance.…”

Section: Methodsmentioning

confidence: 99%

State of health prediction model based on internal resistance

Zhang

Pan

et al. 2020

Int J Energy Res

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“…Firstly, an accurate battery model is established to describe the battery dynamic characteristics. Two representative types of battery models are electrochemical models [8], [9] and equivalent circuit models (ECMs) [10]- [12]. The electrochemical models are highly accurate but with high complexity and heavy computation load in implementation.…”

Section: Introductionmentioning

confidence: 99%

A Variational Bayes Based State-of-Charge Estimation for Lithium-Ion Batteries Without Sensing Current

2021

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State-of-charge (SOC) estimation of lithium-ion batteries in portable devices without sensing the current is considered in this study. Unlike the traditional approach of separate estimation of the SOC and current, we firstly reformulate the problem as state estimation for the nonlinear system with an unknown input which refers to the current in this study, then a novel variational Bayes-based unscented Kalman filter (VB-UKF) is proposed to simultaneously estimate the SOC and the current input for the nonlinear lithiumion battery system. Verifications of the SOC estimation performance are made by the experiments under the pulsed-discharge profile and urban dynamometer driving schedule profile. Experimental results show that the proposed VB-UKF algorithm is superior to the unscented recursive three-step filter (URTSF) in terms of convergence rate and estimation accuracy of the SOC and current. And the SOC root mean square errors of VB-UKF are bounded within ±3% after convergence which indicates the feasibility and effectiveness of the proposed method.INDEX TERMS Battery, state-of-charge, unknown current, unscented Kalman filter, variational Bayes.

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“…Moreover, the relationship between the inconsistent performance parameters of cells is the key to predicting inconsistent characteristics of unpacked cells [9] or battery packs [10], [11]. Because of the enormous amount of inconsistency information and the limited computing capacity, the weakest cells in packs are regarded as the determining factors for battery pack performance [12]- [14], which barely ensures battery reliability control and prediction. Besides, the inconsistent cells need larger space for data storage during operation in EVs, bringing challenges to the data transmitting and storage [15]- [17].…”

Section: Introductionmentioning

confidence: 99%

Understanding Discharge Voltage Inconsistency in Lithium-Ion Cells via Statistical Characteristics and Numerical Analysis

et al. 2020

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In electric vehicles (EVs), owing to the necessity of large amounts of energy and power, lithium-ion batteries need to be used in series and parallel configurations. However, the performance of the battery pack is lower than that of any single cell within the pack because of the inconsistency among the packed cells. Herein, the inconsistent voltages of unpacked cells due to varying capacities during discharge are analyzed to provide mechanical reason for inconsistency of battery pack. In terms of dispersion and symmetry, the statistical characteristics of voltage distribution are described using Weibull parameters and is investigated using a numerical analysis of the characteristic voltage curve. The numerical analysis results agree well with the experimental and statistical ones, which confirms that voltage inconsistency originating from manufacturing processes is primarily related with capacity inconsistency and the features of the voltage curves. Furthermore, this numerical approach can provide not only significant theoretical insight into the formation and evaluation of voltage inconsistency; but also practical guidance for controlling the quality of cell production and state estimation for the battery pack due to its low computational cost. INDEX TERMSVoltage inconsistency, inconsistency model, weibull probability model, 4-d probability nephogram, dispersion and symmetry of voltage distribution.

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A graphical model for evaluating the status of series-connected lithium-ion battery pack

Cited by 24 publications

References 45 publications

State of health prediction model based on internal resistance

State of health prediction model based on internal resistance

A Variational Bayes Based State-of-Charge Estimation for Lithium-Ion Batteries Without Sensing Current

Understanding Discharge Voltage Inconsistency in Lithium-Ion Cells via Statistical Characteristics and Numerical Analysis

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