Comparisons of Modeling and State of Charge Estimation for Lithium-Ion Battery Based on Fractional Order and Integral Order Methods

Xiao, Renxin; Shen, Jiangwei; Li, Xiaoyu; Yan, Wensheng; Pan, Erdong; Chen, Zheng

doi:10.3390/en9030184

Cited by 66 publications

(26 citation statements)

References 38 publications

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“…The Grünald-Letnikov fractional-order derivative is chosen in this work to obtain the numerical solution of the voltage differential equation. The α-order fractional order calculus for state x at time step k can be defined as [23][24][25][26]:…”

Section: Battery Modelingmentioning

confidence: 99%

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Rapid Estimation Method for State of Charge of Lithium-Ion Battery Based on Fractional Continual Variable Order Model

Li²,

et al. 2018

Energies

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In recent years, the fractional order model has been employed to state of charge (SOC) estimation. The non integer differentiation order being expressed as a function of recursive factors defining the fractality of charge distribution on porous electrodes. The battery SOC affects the fractal dimension of charge distribution, therefore the order of the fractional order model varies with the SOC at the same condition. This paper proposes a new method to estimate the SOC. A fractional continuous variable order model is used to characterize the fractal morphology of charge distribution. The order identification results showed that there is a stable monotonic relationship between the fractional order and the SOC after the battery inner electrochemical reaction reaches balanced. This feature makes the proposed model particularly suitable for SOC estimation when the battery is in the resting state. Moreover, a fast iterative method based on the proposed model is introduced for SOC estimation. The experimental results showed that the proposed iterative method can quickly estimate the SOC by several iterations while maintaining high estimation accuracy.

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Section: Battery Modelingmentioning

confidence: 99%

“…where T s is the sample interval, k is the number of samples for which the derivative is calculated, j is the distance. According to Equation 5, Equation (4) can be written as [15,24]:…”

Section: Battery Modelingmentioning

confidence: 99%

Rapid Estimation Method for State of Charge of Lithium-Ion Battery Based on Fractional Continual Variable Order Model

Li²,

et al. 2018

Energies

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“…Using noninteger order differential equations, fractional models describe reality better than conventional models of the same order as they model accurately the internal impedance and the electrochemical dynamics of a battery cell. Hence, they are used more and more frequently in recent time [6][7][8][9][10][11][12]. As this model is easy to implement, the additional effort compared to integer order models is negligible and the results are convincing, the application for battery modeling is suitable.…”

Section: Introductionmentioning

confidence: 99%

Cascaded Fractional Kalman Filtering for State and Current Estimation of Large-Scale Lithium-Ion Battery Packs

Kupper

Brenneisen

Stark

et al. 2018

2018 Chinese Control and Decision Conference (CCDC)

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In this paper, a cascaded fractional Kalman filter for state of charge and branch current estimation of largescale battery systems is proposed. As a centralized approach for the estimation of a large-scale system is costly in terms of effort and time, a partition into smaller and, therefore, simpler subsystems is applied. Since the overall system is divided into smaller units, a local computation is allowed and complexity reduced. In these distributed systems, usually, the subsystems communicate with each other to exchange relevant data. Using a model based on mesh currents, we receive a cascaded system structure which results in a hierarchical arrangement of all subsystems. This concludes in a one-directional information flow and, therefore, reduces the overall communication effort. Using this proposed approach, it is not only possible to estimate the states of each branch locally but also to calculate the branch currents when the total current is known. Finally, a practical test with real measurement data is presented.

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“…As safety and reliability critical components, lithium-ion batteries and their management, diagnosis and prognosis of state-of-charge (SOC), and state-of-health (SOH), have attracted more and more research efforts in the past decades [3][4][5]. Particularly, the research on battery capacity degradation and remaining useful life (RUL) estimation are of great interest to battery management system (BMS), prognostics and health management (PHM), reliability engineering, and system design, among many other related areas [3]. From a system design point of view, the estimation of the remaining cycle life and assessment of the health state can be used for control reconfiguration and mission replanning to minimize mission failure risk, improve the system availability, and reduce life-cycle cost [6].…”

Section: Introductionmentioning

confidence: 99%

A Run-Time Dynamic Reconfigurable Computing System for Lithium-Ion Battery Prognosis

et al. 2016

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Abstract:As safety and reliability critical components, lithium-ion batteries always require real-time diagnosis and prognosis. This often involves a large amount of computation, which makes diagnosis and prognosis difficult to implement, especially in embedded or mobile applications. To address this issue, this paper proposes a run-time Reconfigurable Computing (RC) system on Field Programmable Gate Array (FPGA) for Relevance Vector Machine (RVM) to realize real-time Remaining Useful Life (RUL) estimation. The system leverages state-of-the-art run-time dynamic partial reconfiguration technology and customized computing circuits to balance the hardware occupation and computing efficiency. Optimal hardware resource consumption is achieved by partitioning the RVM algorithm according to a multi-objective optimization. Moreover, pipelined and parallel computation circuits for kernel function and matrix inverse are proposed on FPGA to further accelerate the computation. Experimental results with two different battery data sets show that, without sacrificing the RUL prediction performance, the embedded RC platform significantly reduces the computation time and the requirement of hardware resources. This demonstrates that complex prognostic tasks can be implemented and deployed on the proposed system, and it can be extended to the embedded computation of other machine learning algorithms.

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Comparisons of Modeling and State of Charge Estimation for Lithium-Ion Battery Based on Fractional Order and Integral Order Methods

Cited by 66 publications

References 38 publications

Rapid Estimation Method for State of Charge of Lithium-Ion Battery Based on Fractional Continual Variable Order Model

Rapid Estimation Method for State of Charge of Lithium-Ion Battery Based on Fractional Continual Variable Order Model

Cascaded Fractional Kalman Filtering for State and Current Estimation of Large-Scale Lithium-Ion Battery Packs

A Run-Time Dynamic Reconfigurable Computing System for Lithium-Ion Battery Prognosis

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