Precise equivalent circuit model for Li-ion battery by experimental improvement and parameter optimization

Wang, Jianfeng; Jia, Yongkai; Yang, Na; Lu, Yanbing; Shi, Mengyu; Ren, Xiaomin; Lu, Dongchen

doi:10.1016/j.est.2022.104980

Cited by 34 publications

(15 citation statements)

References 34 publications

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“…Gaining a comprehensive understanding of the internal performance conditionswhich is typically based on the equivalent circuit model-is the BMS's primary goal. The relationship between parameter identification results and their influencing factors, such as ambient temperature, charge-discharge rate, SOC, and SOH, has been the subject of extensive investigation under the proposed idea [26][27][28][29][30]. The results of the investigation in this work, however, show that the choice of the sample interval also has a significant impact on the results of parameter identification, which has been disregarded in recent research.…”

Section: Discussionmentioning

confidence: 99%

Effect of Sample Interval on the Parameter Identification Results of RC Equivalent Circuit Models of Li-ion Battery: An Investigation Based on HPPC Test Data

Zhang

Deng²,

Zong³

et al. 2022

Batteries

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The validity of the equivalent circuit model (ECM), which is crucial for the development of lithium-ion batteries (LIBs) and state evaluation, is primarily dependent on the precision of the findings of parameter identification. In this study, the commonly used first-order RC (1-RC) circuit and second-order RC (2-RC) circuit models were selected for parameter identification. A time series of voltage with different sample intervals were used for function fitting based on the least square method, which were extracted from the hybrid pulse power characteristic (HPPC) test data of a commercial square punch LIB, and the sample intervals were set to be 0.1 s, 0.2 s, 0.5 s, and 1.0 s to evaluate the effect of sample interval on the parameter identification results. When the sample interval is more than 0.5 s, the results reveal that the 2-RC circuit model’s goodness of fit marginally declines, and for some data scenarios, the bias between the fitted terminal voltage curve and test curve increases obviously. With all of the sample intervals under consideration, the 1-RC circuit model’s imitative effect is satisfactory. This work demonstrates that the sample interval of data samples, in addition to the method itself, affects the accuracy and robustness of parameter identification, with the 1-RC circuit model showing larger advantages under low sample frequency compared to the 2-RC circuit model.

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Section: Discussionmentioning

confidence: 99%

Effect of Sample Interval on the Parameter Identification Results of RC Equivalent Circuit Models of Li-ion Battery: An Investigation Based on HPPC Test Data

Zhang

Deng²,

Zong³

et al. 2022

Batteries

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show abstract

“…[50] Compared with the second-order RC model, the first-order RC model (1-RC-ECM) has a relatively simple structure and low complexity of parameter identification, which has a high practical value while ensuring accuracy. [51,52] To sum up, an enhanced ECM called H_T-ECM is developed by combining the cell internal resistance, first-order RC network, OCV submodel, and hysteresis sub-model, and its topology is shown in Figure 14.…”

Section: Model Constructionmentioning

confidence: 99%

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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“…The equivalent circuit theory-based Li-ion cell models are widely used for low-cost BMS applications [12,13]. The Thevenin-based model with two RC branches is used to develop the model-based SOC estimation.…”

Section: Li-ion Cell Modelmentioning

confidence: 99%

An Extended Kalman Filter Based SOC Estimation for Li-ion Battery Packs Integrated with a Fixed-Point Micro-controller

Khanna,

Singh,

K.V

et al. 2023

Preprint

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A fixed point Extended Kalman filter (EKF) model is designed and implemented for modest Battery Management System (BMS) for light EV applications. The standard EKF state equations are re-scaled to a higher resolution and converted to a fixed-point decimal word-length. The coulomb counting (CC) and open circuit voltage (OCV) based methods are integrated via a fixed point extended Kalman filter technique. The updated approach can overcome the inaccuracy of CC and the insufficiency of OCV-based methods. Initial state estimation, process and measurement noise co-variances are estimated based on experimental cell data. The algorithm is able to achieve < 500μs of computation time on the fixed-point micro-controller BMS unit. The validation results based on the continuous and multiple drive cycle tests indicate an error of ≤ 2% in State of Charge (SOC) estimation.

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Precise equivalent circuit model for Li-ion battery by experimental improvement and parameter optimization

Cited by 34 publications

References 34 publications

Effect of Sample Interval on the Parameter Identification Results of RC Equivalent Circuit Models of Li-ion Battery: An Investigation Based on HPPC Test Data

Effect of Sample Interval on the Parameter Identification Results of RC Equivalent Circuit Models of Li-ion Battery: An Investigation Based on HPPC Test Data

Battery Modeling Considering Hysteresis Effect and Temperature Adaptability

An Extended Kalman Filter Based SOC Estimation for Li-ion Battery Packs Integrated with a Fixed-Point Micro-controller

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