A novel hybrid equivalent circuit model for lithium-ion battery considering nonlinear capacity effects

Chen, Dongdong; Xiao, Long; Yan, Wenduan; Guo, Yinbiao

doi:10.1016/j.egyr.2021.06.051

Cited by 27 publications

(9 citation statements)

References 5 publications

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“…22 The SOC value is a relative quantity expressed as a percentage, and the range of SOC values is 0%-100%. 23 The SOC of a LIB reflects the remaining capacity at the current moment during its operation. The SOC value of batteries cannot be directly measured, and can only be indirectly measured through other external parameters of the battery, 24 which plays an important role in the assessment of battery health status.…”

Section: Research Status Of Soc Estimation Methodsmentioning

confidence: 99%

Review—Optimized Particle Filtering Strategies for High-Accuracy State of Charge Estimation of LIBs

Wang¹,

Jia²,

Takyi‐Aninakwa³

et al. 2023

J. Electrochem. Soc.

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Lithium-ion batteries (LIBs) are used as energy storage systems due to their high efficiency. State of charge (SOC) estimation is one of the key functions of the battery management system (BMS). Accurate SOC estimation helps to determine the driving range and effective energy management of electric vehicles (EVs). However, due to complex electrochemical reactions and nonlinear battery characteristics, accurate SOC estimation is challenging. Therefore, this review examines the existing methods for estimating the SOC of LIBs and analyzes their respective advantages and disadvantages. Subsequently, a systematic and comprehensive analysis of the methods for constructing LIB models is conducted from various aspects such as applicability and accuracy. Finally, the advantages of particle filtering (PF) over the Kalman filter (KF) series algorithm for estimating SOC are summarized, and various improved PF algorithms for estimating the SOC of LIBs are compared and discussed. Additionally, this review provides corresponding suggestions for researchers in the battery field.

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Section: Research Status Of Soc Estimation Methodsmentioning

confidence: 99%

Review—Optimized Particle Filtering Strategies for High-Accuracy State of Charge Estimation of LIBs

Wang¹,

Jia²,

Takyi‐Aninakwa³

et al. 2023

J. Electrochem. Soc.

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“…15 The equivalent circuit model has a simple structure and is easy to realize, but it cannot fully characterize the dynamic process of degradation and overlooks the complex nonlinearity. 16,17 For empirical models, statistical filtering methods are typically applied to simulate the dynamic degradation of batteries, such as Kalman filter, 14,15 particle filter. [18][19][20] These methods establish the state space equation by taking RUL as the internal state of the system, and then realizes estimation and prediction in the form of recursion.…”

Section: Introductionmentioning

confidence: 99%

“…The electrochemical model depends on the electrochemical principle of the battery, but it involves a large number of model parameters and is computationally complex 15 . The equivalent circuit model has a simple structure and is easy to realize, but it cannot fully characterize the dynamic process of degradation and overlooks the complex nonlinearity 16,17 . For empirical models, statistical filtering methods are typically applied to simulate the dynamic degradation of batteries, such as Kalman filter, 14,15 particle filter 18–20 .…”

Section: Introductionmentioning

confidence: 99%

Degradation trajectory prediction of lithium‐ion batteries based on charging‐discharging health features extraction and integrated data‐driven models

Zheng,

Wu,

Tao

2024

Quality & Reliability Eng

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As one of the key technologies in battery management system, accurate remaining useful life (RUL) prediction is critical to guarantee the reliability and safety for electrical equipment. However, the generalization and robustness of a single method are limited. A novel fusion data‐driven RUL prediction method CSSA‐ELM‐LSSVR based on charging‐discharging health features extraction is proposed in this paper, which fusions chaotic sparrow search algorithm (CSSA), extreme learning machine (ELM), and least squares support vector regression (LSSVR). First, four health indicators (HIs) are extracted from the charging‐discharging process, which can reflect the battery degradation phenomenon from multiple perspectives. Then, pearson correlation coefficient is used to numerically analyze the correlation between HIs and battery aging capacities. Second, the extracted HIs are used as the inputs for ELM and LSSVR to predict the degradation trend of battery, where CSSA is used for hyperparameters optimization in ELM. Finally, considering that CSSA‐ELM can capture the general trend of degradation curves, while LSSVR can trace the detail changes, a fusion framework based on CSSA‐ELM and LSSVR is proposed for RUL prediction. Two weighting schemes, namely precision‐based weighting (PW) and random forest regressor‐based weighting (RFRW) are put forward to fix the weights of CSSA‐ELM and LSSVR algorithms. Two publicly available datasets from National Aeronautics and Space Administration (NASA) and MIT are adopted to verify the feasibility and effectiveness of the proposed method. The results indicate that the proposed method with any weighting scheme has an overall superior prediction performance for different kinds of batteries compared with CSSA‐ELM, LSSVR, convolution neural network and long short term memory. Moreover, the RFRW scheme has better overall performance. Specifically, the maximum root mean square error of the predicted method is 2.5126%, the mean absolute percentage error is 12.9167%, the mean absolute error is 1.8376%, the predicted RUL errors are within one cycle, and the determination coefficient R2 is above 0.97.

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“…The battery can be equivalent to circuit configuration, and the variation between voltage and current is reflected by the combination of various electronic components. The dynamic and static characteristics of the battery can be well displayed by appropriate circuit configuration in the charging and discharging processes (Chen et al, 2021). Compared with the electrochemical model and machine learning model or datadriven model, the equivalent circuit model has obvious advantages: 1) It can be used in various types of batteries because of its strong adaptability; 2) it can be described by mathematical expression; 3) identification of this model parameters is much easier than that of other models.…”

Section: Introductionmentioning

confidence: 99%

State of charge estimation of lithium-ion battery based on extended Kalman filter algorithm

Xie

Wei

Bo³

et al. 2023

Front. Energy Res.

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Due to excellent power and energy density, low self-discharge and long life, lithium-ion battery plays an important role in many fields. Directed against the complexity of above noises and the strong sensitivity of the common Kalman filter algorithm to noises, the state of charge estimation of lithium-ion battery based on extended Kalman filter algorithm is investigated in this paper. Based on the second-order resistor-capacitance equivalent circuit model, the battery model parameters are identified using the MATLAB/Simulink software. A battery parameter test platform is built to test the charge-discharge efficiency, open-circuit voltage and state of charge relationship curve, internal resistance and capacitance of the individual battery are tested. The simulation and experimental results of terminal voltage for lithium-ion battery is compared to verify the effectiveness of this method. In addition, the general applicability of state of charge estimation algorithm for the battery pack is explored. The ampere-hour integral method combined with the battery modeling is used to estimate the state of charge of lithium-ion battery. The comparison of extended Kalman filter algorithm between experimental results and simulation estimated results is obtained to verify the accuracy. The extended Kalman filter algorithm proposed in this study not only establishes the theoretical basis for the condition monitoring but also provides the safe guarantee for the engineering application of lithium-ion battery.

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A novel hybrid equivalent circuit model for lithium-ion battery considering nonlinear capacity effects

Cited by 27 publications

References 5 publications

Review—Optimized Particle Filtering Strategies for High-Accuracy State of Charge Estimation of LIBs

Review—Optimized Particle Filtering Strategies for High-Accuracy State of Charge Estimation of LIBs

Degradation trajectory prediction of lithium‐ion batteries based on charging‐discharging health features extraction and integrated data‐driven models

State of charge estimation of lithium-ion battery based on extended Kalman filter algorithm

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