Recursive modeling and online identification of lithium-ion batteries for electric vehicle applications

Li, Yong; Wang, Lifang; Liao, Chenglin; Wang, Liye; DongPin, XU

doi:10.1007/s11431-013-5431-y

Cited by 23 publications

(8 citation statements)

References 25 publications

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“…In [39], resistance R cell was modeled as a function of the ionic conductivity of the electrolyte and the thicknesses of the electrodes. At high charge or discharge rates, the potential distribution inside the electrolyte cannot be neglected [40].…”

Section: Subdiffusion Model Of the Lithium-ion Cellmentioning

confidence: 99%

Fractional Differential Generalization of the Single Particle Model of a Lithium-Ion Cell

et al. 2019

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The effect of anomalous diffusion of lithium on the discharge curves and impedance spectra of lithium-ion batteries (LIB) is studied within the fractional differential generalization of the single-particle model. The distribution of lithium ions in electrolyte and electrode particles is expressed through the Mittag–Leffler function and the Lévy stable density. Using the new model, we generalize the equivalent circuit of LIB. The slope of the low-frequency rectilinear part of the Nyquist diagram does not always unambiguously determine the subdiffusion index and can be either larger or smaller than the slope corresponding to normal diffusion. The new aspect of capacity degradation related to a change in the type of ion diffusion in LIB components is discussed.

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Section: Subdiffusion Model Of the Lithium-ion Cellmentioning

confidence: 99%

Fractional Differential Generalization of the Single Particle Model of a Lithium-Ion Cell

et al. 2019

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“…The one-order ECM cannot completely describe battery dynamic nature, since the model random noise characteristic is not included in (1). As illustrated in Figure 2, a colored noise has been added as a compensation module in oneorder ECM, and is to cover the random error which is not considered in the one-order ECM [21,22].…”

Section: Battery Model and Parameters Identificationmentioning

confidence: 99%

“…In (8) time sequence of Δ ( ) is immeasurable, ( ) cannot be got directly; therefore some traditional least squares (LS) series online identification algorithms are unable to identify ( ). Accordingly, the RELS algorithm [21,22] is introduced to identify the immeasurable noise terms first. Based on (5) and (8), ( ) can be approximated to the difference between measured terminal voltage and identified terminal voltage; meanwhile, Δ ( ) can be approximated to the difference between measured differential terminal voltage and identified differential terminal voltage, as follows.…”

Section: Parameters Identificationmentioning

confidence: 99%

“…Although multiorder ECM can lead to better performance in representing battery dynamic behavior than the one-order ECM, the more ECM parameters need to be identified with deepening of algorithm complexity. In [21,22], an additional noise is added to one-order ECM, but the RLS with constant forgetting factor may encounter the difficulties of compromise between stability and convergence. Therefore, the effective ECM is largely dependent on the balance between complexity and precision of corresponding algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Online State of Charge Estimation for Lithium-Ion Battery by Combining Incremental Autoregressive and Moving Average Modeling with Adaptive H-Infinity Filter

Liu

Dang

2018

Mathematical Problems in Engineering

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The state of charge (SOC) estimation is one of the most important features in battery management system (BMS) for electric vehicles (EVs). In this article, a novel equivalent-circuit model (ECM) with an extra noise sequence is proposed to reduce the adverse effect of model error. Model parameters identification method with variable forgetting factor recursive extended least squares (VFFRELS), which combines a constructed incremental autoregressive and moving average (IARMA) model with differential measurement variables, is presented to obtain the ECM parameters. The independent open circuit voltage (OCV) estimator with error compensation factors is designed to reduce the OCV error of OCV fitting model. Based on the IARMA battery model analysis and the parameters identification, an SOC estimator by adaptive H-infinity filter (AHIF) is formulated. The adaptive strategy of the AHIF improves the numerical stability and robust performance by synchronous adjusting noise covariance and restricted factor. The results of experiment and simulation have verified that the proposed approach has superior advantage of parameters identification and SOC estimation to other estimation methods.

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“…As one reduced form of complex electrochemical model, the ECM has been extensively researched to describe dynamic hysteresis characteristics of lithiumion battery through composition of basic electrical circuit elements. In [16,17], the dynamic response of lithium-ion battery can be simulated by one-order RC ECM with a simple topology structure, and a recursive least squares (RLS) method is presented for parameters identification. However, the RLS with constant forgetting factor mismatches the ECM parameters with different changing rates.…”

Section: Introductionmentioning

confidence: 99%

A New Method for State of Charge and Capacity Estimation of Lithium-Ion Battery Based on Dual Strong Tracking Adaptive H Infinity Filter

Liu

Dang

2018

Mathematical Problems in Engineering

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As one of the most important features representing the operating state of power battery in electric vehicles (EVs), state of charge (SOC) and capacity estimation is a crucial assessment index in battery management system (BMS). This paper presents a fusion method of SOC and capacity estimation with identified model parameters. The equivalent circuit model (ECM) parameters are obtained online by variable forgetting factor recursive least squares (VFFRLS), which is based on incremental ECM analysis to respond to the inconsistent rates of parameters variation. The independent open-circuit voltage (OCV) estimation way is designed to reduce the effect of mutual coupling between OCV and ECM parameters. Based on the identified ECM parameters and OCV, a dual adaptive H infinity filter (AHIF) combined with strong tracking filter (STF) is proposed to estimate battery SOC and capacity. A new quadratic function as capacity error compensation is introduced to represent the relationship between capacity and OCV. The adaptive strategy of the AHIF can adjust noise covariance and restricted factor, while the STF can regulate prior state covariance by adding suboptimum fading factor. The results of experiment and simulation show the merits of proposed approach in SOC and capacity estimation.

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Recursive modeling and online identification of lithium-ion batteries for electric vehicle applications

Cited by 23 publications

References 25 publications

Fractional Differential Generalization of the Single Particle Model of a Lithium-Ion Cell

Fractional Differential Generalization of the Single Particle Model of a Lithium-Ion Cell

Online State of Charge Estimation for Lithium-Ion Battery by Combining Incremental Autoregressive and Moving Average Modeling with Adaptive H-Infinity Filter

A New Method for State of Charge and Capacity Estimation of Lithium-Ion Battery Based on Dual Strong Tracking Adaptive H Infinity Filter

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