Model Parameters Online Identification and SOC Joint Estimation for Lithium-Ion Battery Based on a Composite Algorithm

Long, Hongyu; Zhu, Cheng-Yong; Huang, Bin; Piao, Changhao; Sun, Yi

doi:10.1007/s42835-019-00179-w

Cited by 18 publications

(12 citation statements)

References 5 publications

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“…Take the 2-RC model as an example, let V(k) = U OC (k) − V L (k), V L (k) and I(k) be the terminal voltage and current value of the kth sampling point, respectively. Then the difference form can be deduced from the transfer function of V and I by Laplace inverse transformation [27]:…”

Section: Soc Estimation Methods Based On Voem-ar-ukf 41 Autoregressive Equationmentioning

confidence: 99%

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Variable-Order Equivalent Circuit Modeling and State of Charge Estimation of Lithium-Ion Battery Based on Electrochemical Impedance Spectroscopy

et al. 2021

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In the battery management system, it is important to accurately and efficiently estimate the state of charge (SOC) of lithium-ion batteries, which generally requires the establishment of a equivalent circuit model of the battery, whose accuracy and rationality play an important role in accurately estimating the state of lithium-ion batteries. The traditional single order equivalent circuit models do not take into account the changes of impedance spectrum under the action of multiple factors, nor do they take into account the balance of practicality and complexity of the model, resulting the low accuracy and poor practicability. In this paper, the theory of electrochemical impedance spectroscopy is used to guide and improve the equivalent circuit model. Based on the analysis of the variation of the high and intermediate frequency range of the impedance spectrum with the state of charge and temperature of the battery, a variable order equivalent model (VOEM) is proposed by Arrhenius equation and Bayesian information criterion (BIC), and the state equation and observation equation of VOEM are improved by autoregressive (AR) equations. Combined with the unscented Kalman filter (UKF), a SOC online estimation method is proposed, named VOEM-AR-UKF. The experimental results show that the proposed method has high accuracy and good adaptability.

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Section: Soc Estimation Methods Based On Voem-ar-ukf 41 Autoregressive Equationmentioning

confidence: 99%

“…In ( 10), a 1 , a 2 , a 3 , b 1 and b 2 can be identified by forgetting factor recursive least squares (FFRLS) [27] online, and then, the state equation and observation equation based on AR equation are written in the form of (11):…”

Section: Soc Estimation Methods Based On Voem-ar-ukf 41 Autoregressive Equationmentioning

confidence: 99%

Variable-Order Equivalent Circuit Modeling and State of Charge Estimation of Lithium-Ion Battery Based on Electrochemical Impedance Spectroscopy

et al. 2021

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“…Through the thermal analysis of the lithium-ion battery, a method for estimating the state of charge of the electrode under different states of charge and aging is proposed [31]. In addition, the online parameter identification algorithm is used to perform high-precision identification of battery model parameters, and on this basis, a new SOC joint estimation algorithm is proposed to improve the accuracy of SOC estimation [32]. The above-mentioned studies have effectively promoted the development of the global energy-saving and new-energy vehicle technology and industry.…”

Section: Literature Reviewmentioning

confidence: 99%

“…FFRLS adds an exponential forgetting factor to the RLS algorithm. By weighting the estimation error to optimize the objective function J(k), the problem of "data saturation" in the time-varying system of the RLS algorithm is solved [32]. The optimized objective function is Equation ( 6):…”

Section: Online Identification Based On Ffrls Algorithmmentioning

confidence: 99%

A Novel Adaptive Function—Dual Kalman Filtering Strategy for Online Battery Model Parameters and State of Charge Co-Estimation

et al. 2021

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This paper aims to improve the stability and robustness of the state-of-charge estimation algorithm for lithium-ion batteries. A new internal resistance-polarization circuit model is constructed on the basis of the Thevenin equivalent circuit to characterize the difference in internal resistance between charge and discharge. The extended Kalman filter is improved through adding an adaptive noise tracking algorithm and the Kalman gain in the unscented Kalman filter algorithm is improved by introducing a dynamic equation. In addition, for benignization of outliers of the two above-mentioned algorithms, a new dual Kalman algorithm is proposed in this paper by adding a transfer function and through weighted mutation. The model and algorithm accuracy is verified through working condition experiments. The result shows that: the errors of the three algorithms are all maintained within 0.8% during the initial period and middle stages of the discharge; the maximum error of the improved extension of Kalman algorithm is over 1.5%, that of improved unscented Kalman increases to 5%, and the error of the new dual Kalman algorithm is still within 0.4% during the latter period of the discharge. This indicates that the accuracy and robustness of the new dual Kalman algorithm is better than those of traditional algorithm.

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“…The ECMs describe the battery's behavior by basic electric components such as resistors and capacitors. Some well-known ECMs are the Thevenin model [24,25], DP model [26], and PNGV (Partnership for a New Generation of Vehicle) model. Hu et al [27] analyzed 12 lithium-ion battery equivalent circuit models and proposed that the selection of the model should take into consideration accuracy and simplicity, and models of higher than the second order only had high accuracy in theory.…”

Section: Introductionmentioning

confidence: 99%

Online Parameters Identification and State of Charge Estimation for Lithium-Ion Battery Using Adaptive Cubature Kalman Filter

Luo

Tan

et al. 2021

WEVJ

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The state of charge (SOC) of a lithium-ion battery plays a key role in ensuring the charge and discharge energy control strategy, and SOC estimation is the core part of the battery management system for safe and efficient driving of electric vehicles. In this paper, a model-based SOC estimation strategy based on the Adaptive Cubature Kalman filter (ACKF) is studied for lithium-ion batteries. In the present study, the dual polarization (DP) model is employed for SOC estimation and the vector forgetting factor recursive least squares (VRLS) method is utilized for model parameter online identification. The ACKF is then designed to estimate the battery’s SOC. Finally, the Urban Dynamometer Driving Schedule and Dynamic Stress Test are utilized to evaluate the performance of the proposed method by comparing with results obtained using the extended Kalman filter (EKF) and the cubature Kalman filter (CKF) algorithms. The simulation and experimental results show that the proposed ACKF algorithm combined with VRLS-based model identification is a promising SOC estimation approach. The proposed algorithm is found to provide more accurate SOC estimation with satisfying stability than the extended EKF and CKF algorithms.

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Model Parameters Online Identification and SOC Joint Estimation for Lithium-Ion Battery Based on a Composite Algorithm

Cited by 18 publications

References 5 publications

Variable-Order Equivalent Circuit Modeling and State of Charge Estimation of Lithium-Ion Battery Based on Electrochemical Impedance Spectroscopy

Variable-Order Equivalent Circuit Modeling and State of Charge Estimation of Lithium-Ion Battery Based on Electrochemical Impedance Spectroscopy

A Novel Adaptive Function—Dual Kalman Filtering Strategy for Online Battery Model Parameters and State of Charge Co-Estimation

Online Parameters Identification and State of Charge Estimation for Lithium-Ion Battery Using Adaptive Cubature Kalman Filter

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