An electrochemical impedance model of lithium-ion battery for electric vehicle application

Zhang, Qi; Wu, Dafang; Yang, Bowen; Dong, Haosong; Zhu, Cheng; Hao, Ziwei

doi:10.1016/j.est.2022.104182

Cited by 31 publications

(15 citation statements)

References 40 publications

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“…). 39 The lower slope indicates that the ion diffusion coefficient of the BO-CNS is higher than that of the O-CNS, guaranteeing rapid ion migration (Fig. 5c and f).…”

Section: Resultsmentioning

confidence: 96%

An ecofriendly and universal strategy to balance the active sites and electrical conductivity of biomass-derived carbon for superior lithium storage

Chen¹,

Zhang²,

Zhang³

et al. 2023

New J. Chem.

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“…). 39 The lower slope indicates that the ion diffusion coefficient of the BO-CNS is higher than that of the O-CNS, guaranteeing rapid ion migration (Fig. 5c and f).…”

Section: Resultsmentioning

confidence: 96%

An ecofriendly and universal strategy to balance the active sites and electrical conductivity of biomass-derived carbon for superior lithium storage

Chen¹,

Zhang²,

Zhang³

et al. 2023

New J. Chem.

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show abstract

Section: Introductionmentioning

confidence: 99%

“…The electrochemical model describes the diffusion process and charge transfer process of lithium ions in the cell based on porous electrodes and concentrated solution theory and uses a set of coupled partial differential equations to achieve real-time estimation of SOC. [14][15][16] Ref.[17] designed a nonlinear observer with terminal-voltage feedback injection based on the electrochemical single-particle model, which improved the accuracy of SOC estimation. Although the electrochemical model can reflect the actual electrochemical reaction process inside the battery more accurately, the structure is complicated and not conducive to practical simulation and calculation.…”

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confidence: 99%

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Battery Modeling Considering Hysteresis Effect and Temperature Adaptability

et al. 2023

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With the increasing severity of energy crisis and the gradual increase of environmental awareness, countries around the world have devoted themselves to the new energy vehicle industry. [1] As the foundation and core component of electric vehicles, the power battery pack and its management system play a decisive role in the performance and development of electric vehicles. The power battery pack often uses lithium-ion batteries with higher energy density, lower self-discharge rate, and long cycle life. [2][3][4][5][6] Accurate stateof-charge (SOC) estimation is an important factor to control the stable operation of batteries, which directly affects the life and safety of the battery. [7][8][9] At present, SOC estimation can be roughly divided into two categories: principle-based and model-based methods. [10,11] The modelbased SOC estimation method is widely used because of its high robustness and accuracy under variable battery operating conditions. [12,13] Battery models, as an important part of model-based SOC estimation methods, commonly include electrochemical models, blackbox models and equivalent circuit models (ECMs). The electrochemical model describes the diffusion process and charge transfer process of lithium ions in the cell based on porous electrodes and concentrated solution theory and uses a set of coupled partial differential equations to achieve real-time estimation of SOC. [14][15][16] Ref.[17] designed a nonlinear observer with terminal-voltage feedback injection based on the electrochemical single-particle model, which improved the accuracy of SOC estimation. Although the electrochemical model can reflect the actual electrochemical reaction process inside the battery more accurately, the structure is complicated and not conducive to practical simulation and calculation. The blackbox model establishes an abstract mapping relationship between inputs and outputs, reflecting a generalized direct causal relationship between the factors involved. Real-time estimation of SOC can be achieved by taking real-time state quantities (e.g., voltage, current, etc.) during battery operation as inputs. [18][19][20][21][22] Ref. [23] proposed an improved feedforward-long short-term memory (FF-LSTM) modeling method to realize an accurate whole-life-cycle SOC prediction by effectively considering the current, voltage, and temperature variations. Although the calculation of the blackbox model is simple, the accuracy of the estimation depends to a large extent on the quality and quantity of the training data, and the training process may take a long time. From the perspective of external electrical characteristics, ECM uses circuit elements such as resistance, capacitance, and voltage source to form a circuit network to express the relationship between voltage and current in the cell. [24] It cleverly avoids the real structure and complex electrochemical reactions inside the battery and can estimate SOC in real-time by combining it with the filtering algorithm. [25][26][27][28][29] Ref. [30] proposed a novel feedback

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“…[ 1 ] They are widely used in consumer electronics and electric vehicles. [ 2 ] The efficiency and safety of lithium‐ion batteries before delivery are particularly important. When a battery is stored for a long time, its power reduces.…”

Section: Introductionmentioning

confidence: 99%

Self‐Discharge Voltage Drop Estimation Method Based on Improved Gaussian Process Regression

et al. 2022

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The self-discharge rate is an important index for determining the quality of a lithium-ion battery. Currently, the self-discharge of a battery is mainly determined through experimental tests, which are time-consuming and laborious. To provide a fast and reliable self-discharge rate estimation, an improved Gaussian process regression (GPR) model based on the charge-discharge curve is proposed to estimate the self-discharge voltage drop. In this method, the voltage features extracted from the charge-discharge curve are used as the input of the GPR model. These features reflect the self-discharge performance of batteries from different angles. The gray correlation analysis method is used to analyze the degree of correlation between the selected features and self-discharge voltage drop. Furthermore, a kernel function suitable for voltage drop estimation is selected. Simultaneously, the GPR model is adjusted, and the particle swarm optimization algorithm is used to optimize the superparameters of the GPR model to improve the estimation accuracy of the self-discharge voltage drop of the model. The selfdischarge voltage drop data from the experiment are used to demonstrate the estimation effect of the proposed method. The results reveal that this method is reliable and has a high estimation accuracy for the self-discharge voltage drop.

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An electrochemical impedance model of lithium-ion battery for electric vehicle application

Cited by 31 publications

References 40 publications

An ecofriendly and universal strategy to balance the active sites and electrical conductivity of biomass-derived carbon for superior lithium storage

An ecofriendly and universal strategy to balance the active sites and electrical conductivity of biomass-derived carbon for superior lithium storage

Battery Modeling Considering Hysteresis Effect and Temperature Adaptability

Self‐Discharge Voltage Drop Estimation Method Based on Improved Gaussian Process Regression

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