State-of-charge estimation of lithium-ion batteries from a gas-liquid dynamics model including the direct temperature input

Jiang, Haobin; Chen, Biao; Li, Huanhuan; Hua, Lei; Chen, Xijia; Wang, Yaping; Zhao, Qian

doi:10.1016/j.est.2021.102622

Cited by 9 publications

(6 citation statements)

References 44 publications

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“…Besides the above two models, our group proposed a general and feasible gas-liquid dynamics (GLD) battery model from a new perspective. [6][7][8][9] The GLD model is based on the gas equation and Bernoulli equation for gas-liquid systems, reflecting electron transfer, end-voltage hysteresis, Li + diffusion and equilibrium, and ohmic effects in LIBs. 7,8 The advantage of this model is that it can balance the accuracy and complexity of the model to a certain extent, and achieves better estimation accuracy without combining any intelligent algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…7,8 The advantage of this model is that it can balance the accuracy and complexity of the model to a certain extent, and achieves better estimation accuracy without combining any intelligent algorithm. 8,9 The GLD model is mainly used in the field of SOC estimation. However, the simple GLD model has the problem of chattering in the SOC estimation of complex operating conditions and will be affected by the drift voltage noise, which needs to be optimized and improved by relevant algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, each additional RC circuit adds one additional state equation and two uncertain parameters, and the difficulty and uncertainty of model parameter identification increase significantly. Besides the above two models, our group proposed a general and feasible gas‐liquid dynamics (GLD) battery model from a new perspective 6‐9 . The GLD model is based on the gas equation and Bernoulli equation for gas‐liquid systems, reflecting electron transfer, end‐voltage hysteresis, Li + diffusion and equilibrium, and ohmic effects in LIBs 7,8 .…”

Section: Introductionmentioning

confidence: 99%

“…The GLD model is based on the gas equation and Bernoulli equation for gas‐liquid systems, reflecting electron transfer, end‐voltage hysteresis, Li + diffusion and equilibrium, and ohmic effects in LIBs 7,8 . The advantage of this model is that it can balance the accuracy and complexity of the model to a certain extent, and achieves better estimation accuracy without combining any intelligent algorithm 8,9 . The GLD model is mainly used in the field of SOC estimation.…”

Section: Introductionmentioning

confidence: 99%

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SOC estimation based on the gas‐liquid dynamics model using particle filter algorithm

et al. 2022

Intl J of Energy Research

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Lithium-ion battery state of charge (SOC) estimation is the basis for other state estimations in electric vehicle (EV) battery management systems. Accurate SOC estimation can prevent overcharge and discharge of the battery, improve discharge efficiency and prolong cycle life. A high-precision SOC estimation method based on the gas-liquid dynamics (GLD) model is presented. The standard particle filter (PF) and the unscented particle filter (UPF) algorithm are successively used for optimizing and improving the chattering problem of the original GLD model. The results show that the two kinds of PF algorithms can effectively solve the chattering problem of the GLD model and improve the estimation accuracy. Among them, UPF has higher accuracy and the maximum estimation error is less than 2%. The PF method for estimating SOC introduced in this paper has the characteristics of high precision and large amount of calculation. Due to the above characteristics, this method can be used as a cloud SOC calculation method to periodically correct the local SOC of the vehicle.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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SOC estimation based on the gas‐liquid dynamics model using particle filter algorithm

et al. 2022

Intl J of Energy Research

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“…However, due to the inconsistencies of internal parameters, internal states and operating conditions of each battery in the series battery module, there is a great difficulty to predict the series battery module life accurately. 7,8 At present, there are three main research methods regarding the state of health (SOH) of series battery modules. 9 The first method is to introduce the analysis on the inconsistency of every single battery according to the SOH estimation model of the single battery.…”

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confidence: 99%

Research on State of Health for the Series Battery Module Based on the Weibull Distribution

Zhao¹,

Jiang²,

Chen³

et al. 2022

J. Electrochem. Soc.

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The state of health (SOH) of a battery module is an important parameter in the battery management system. Accurately grasping the SOH of the battery module can provide the basis for its detection and diagnosis. Here, the series battery module SOH is taken as the research object. Based on the characteristics of the series battery module, a two-parameter Weibull distribution is selected as the module failure data distribution form and then the reliability function of the Weibull distribution is linearized and the module failure data is preprocessed. The least square method is used to identify the unknown parameters of the linear equation and to carry out correlation analysis and model verification. The result shows the correlation coefficient ρ_(X,Y)=0.9725, indicating that the variables X and Y are significantly correlated. The selected model is tested by the Kolmogorov-Smirnov (K-S) method, and the K-S test statistic D achieves the maximum value D_max=0.0371, which is much smaller than the Dc=0.301 obtained by checking the K-S D critical value table. In the reliability analysis, the failure data are evenly distributed on both sides of the reliability function, indicating that the selected model can well reflect the SOH transformation trend of the series battery module.

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Research on nondestructive measurement of the cross-plane thermal conductivity of a prismatic lithium-ion cell

Chen

Jiang²,

Ding³

et al. 2023

Ionics

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State-of-charge estimation of lithium-ion batteries from a gas-liquid dynamics model including the direct temperature input

Cited by 9 publications

References 44 publications

SOC estimation based on the gas‐liquid dynamics model using particle filter algorithm

SOC estimation based on the gas‐liquid dynamics model using particle filter algorithm

Research on State of Health for the Series Battery Module Based on the Weibull Distribution

Research on nondestructive measurement of the cross-plane thermal conductivity of a prismatic lithium-ion cell

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