On full-life-cycle SOC estimation for lithium batteries by a variable structure based fractional-order extended state observer

Zhao, Xu; Chen, Yongan; Chen, Luowen; Chen, Ning; Wang, Hao; Huang, Wei; Chen, Jiayao

doi:10.1016/j.apenergy.2023.121828

Cited by 19 publications

(2 citation statements)

References 31 publications

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“…Nevertheless, the algorithm is overly reliant on the training set, the reliability of the imported data dramatically impacts the reliability of the final trained model, and the training data cannot contain all the situations of the car in the actual work, which will also lead to a substantial decrease in the reliability of the SOC estimated by the model and ultimately lead to the failure of the identification. Fourth, the power cell SOC estimation is implemented using the circuit equivalent model [10][11][12] approach, which is based on the principle of constructing equivalent circuits to replace the actual operating conditions of the cell, using the relevant algorithms to solve the model, and then using the filtering algorithm to obtain an exact prediction of the SOC. The modelling approach is not only highly accurate and robust in estimation, but the parameters in the model have a clear physical meaning and a more solid theoretical basis.…”

Section: Introductionmentioning

confidence: 99%

Online estimation of lithium battery SOC based on fractional order FOUKF‐FOMIUKF algorithm with multiple time scales

Xing,

Ren,

Luo

et al. 2024

Energy Science & Engineering

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Aiming at the matter of poor precision in predicting the charge of lithium battery by applying conventional integer‐order models and offline parameter identification, this paper proposes a joint fractional‐order multi‐innovations unscented Kalman filter (FOUKF‐FOMIUKF) algorithm for predicting the cells' state of charge (SOC) online and uses the theory of singular‐value decomposition to tackle the issue of failure of the traceless transformation. Initially, the circuitry model of fractional order is built. The parameters of the model are recognized online by fractional‐order unscented Kalman filtering (FOUKF), and the obtained parameters are then transmitted to the method known as the fractional order multi‐innovations unscented Kalman filter (FOMIUKF) to calculate the SOC of the cell. The algorithm was validated under four working conditions such as FUDS (US Federal Urban Driving Distance), BJDST (Beijing Dynamic Stress Test), DST (Dynamic Stress Test), and US06 (Highway Driving Distance Test), respectively, and compared with the FOMIUKF, MIUKF, and FOUKF algorithms for offline identification. The conclusions demonstrate that the SOC estimated by the FOUKF‐FOMIUKF method is controlled within 0.5% of the mean absolute error under the four conditions and the root‐mean‐square error is controlled within 0.8%. It is not difficult to find that the FOUKF‐FOMIUKF algorithm estimates SOC with higher accuracy and robustness.

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Section: Introductionmentioning

confidence: 99%

Online estimation of lithium battery SOC based on fractional order FOUKF‐FOMIUKF algorithm with multiple time scales

Xing,

Ren,

Luo

et al. 2024

Energy Science & Engineering

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show abstract

“…In recent decades, exploring the characteristics of electronic components arouses ever-increasing attention in circuit theory and application fields [20], and a rich source of evidence suggests that the characteristics of electronic components can be more effectively captured by using the concepts of fractional calculus compared to classical calculus-based models, and factional-order impedance (or constant phase elements, CPEs) based models have been widely adopted to describe the characteristics of electronic components, such as inductors, ultracapacitors (UCs), lithium batteries, power MOSFETs, and non-solid electrolytic capacitors [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Application Study of Fractional Modelling and Analysis Methods for Piecewise-Smooth Fractional-Order Circuit Systems: An Efficiency Analysis Oriented Case for Dickson Type Switched-Capacitor Converters

Chen,

Fu,

Zhang

et al. 2024

Preprint

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This paper focus on the modeling and time-domain analyzing method of piecewise-smooth fractional-order circuit systems. Firstly, an analytical framework is constructed based on a generalized piecewise-smooth two-port impedance network model, which takes the topological characteristics of Dickson type switched-capacitor converters (SCCs) as examples, and considers the fractional-order characteristics of class-2 X7R ceramic capacitors, which are widely used in SCCs. Secondly, the time-domain analyzing method for such a piecewise-smooth fractional-order circuit system is developed, numerical solutions are obtained, and the influence of the fractional-order capacitor on the power conversion efficiency of the system are revealed. Finally, in order to validate the analysis, both numerical simulation and experiments are provided in the work, the results of which confirm that, the proposed fractional-order analytical framework can capture the power loss mechanism of piecewise-smooth fractional-order circuit systems more effectively than the traditional integer-order based approach, thus benefiting the design of circuit systems.

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Fast and high-precision online SOC estimation for improved model of lithium-ion battery based on temperature correlation coefficient

Yi,

Chen,

Wang

et al. 2024

Ionics

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On full-life-cycle SOC estimation for lithium batteries by a variable structure based fractional-order extended state observer

Cited by 19 publications

References 31 publications

Online estimation of lithium battery SOC based on fractional order FOUKF‐FOMIUKF algorithm with multiple time scales

Online estimation of lithium battery SOC based on fractional order FOUKF‐FOMIUKF algorithm with multiple time scales

Application Study of Fractional Modelling and Analysis Methods for Piecewise-Smooth Fractional-Order Circuit Systems: An Efficiency Analysis Oriented Case for Dickson Type Switched-Capacitor Converters

Fast and high-precision online SOC estimation for improved model of lithium-ion battery based on temperature correlation coefficient

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