Aging modes analysis and physical parameter identification based on a simplified electrochemical model for lithium-ion batteries

Li, Junfu; Wu, Dafang; Deng, Lei; Cui, Zhiquan; Lyu, Chao; Wang, Lixin; Pecht, Michael

doi:10.1016/j.est.2020.101538

Cited by 57 publications

(25 citation statements)

References 17 publications

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“…Ref. [12] introduced a group of lumped parameters via their functional relationship with the true electrochemical parameters and then identified lumped parameters as an alternative. The similar idea was taken in ref.…”

Section: ± Ocpmentioning

confidence: 99%

Electrochemical Parameter Identification for Lithium-ion Battery Sources in Self-Sustained Transportation Energy Systems

Gu¹,

Wang²,

Chen³

et al. 2022

Preprint

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Lithium-ion batteries (LIBs) play an essential role in the energy sector and have been widely deployed in recent years. Generally, LIBs are managed in model-driven manners, leading to the need for parameter identification. However, as an electrochemical system, the battery contains various parameters while the measurements are mainly the current and voltage, inducing an inherent ill-conditioned identification problem. A flexible and lightweight parameter identification framework, including the test, model, and algorithm, is proposed in this work. Electrochemical parameters are grouped by time-variant features and identified on separated time-scales. Parameters with slow dynamics are identified in a hybrid data-driven and model-driven approach based on the data of a quasi-static test covering the partial SOC range. Parameters with fast dynamics are identified using a specifically designed sensitivity-oriented stepwise optimization algorithm based on the data of a dynamic test consisting of a series of constant current (CC) pulses. The mixed impacts of different parameters can be decoupled and the time costs of the test and computation can be reduced. Specifically, it takes a few hours to identify slow dynamic parameters and a few minutes to identify fast dynamic parameters. Numerical experiments on a typical LiNCM battery at different life stages are conducted. The results show that the identification accuracy of crucial parameters can reach over 95%, and the battery model error is reduced below 2×10 −3 V.

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Section: ± Ocpmentioning

confidence: 99%

Electrochemical Parameter Identification for Lithium-ion Battery Sources in Self-Sustained Transportation Energy Systems

Gu¹,

Wang²,

Chen³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In that sense, particular advancements can be found in terms of BESS-demand coordination [10], BESS-Supercapacitor coordination [11], and BESS-co-generation at the residential level [12]. In terms of aging estimation techniques, there are numerous works on improved aging models [13], datadriven methods [14], use of transfer component analysis to improve data-driven aging models [15], adaptive Kalman filter estimation [16], continuous time aging estimation [17], and physical parameter identification [18]. The progress on estimating BESS aging is significant, but its relation to power system frequency control operation is visited in relatively few specialized works.…”

Section: State Of the Artmentioning

confidence: 99%

The Impact of Aging-Preventive Algorithms on BESS Sizing under AGC Performance Standards

et al. 2021

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It is normally accepted that Battery Energy Storage Systems improve frequency regulation by providing fast response to the Automatic Generation Control. However, currently available control strategies may lead to early Energy Storage Systems aging given that Automatic Generation Control requirements are increasing due to zero carbon power generation integration. In this sense, it is important to analyze the aging phenomena in order to assess the technical–economical usefulness of Battery Energy Storage Systems towards zero carbon power systems. In order to avoid early aging, various proposals on aging-reducing algorithms can be found; however, it is unclear if those aging-reducing algorithms affect the performance of Battery Energy Storage Systems. It is also unclear whether those effects must be internalized to properly dimension the capacity of Battery Energy Storage Systems to both comply with performance standards and to prevent early aging. Thus, this paper estimates the storage capacity of a Battery Energy Storage Systems to comply with Automatic Generation Control performance standard under aging-reducing operating algorithms by dynamics simulations of a reduced-order, empirically-validated model of the Electric Reliability Council of Texas. The results show the relationship between the required performance of Automatic Generation Control and Battery Energy Storage System capacity, considering a 1-year simulation of Automatic Generation Control dynamics. It can be concluded that the compliance with performance standards is strongly related to the storage capacity, regardless of how fast the device can inject or withdraw power from the grid. Previous results in the state-of-the-art overlook the quantification of this relationship between compliance with performance standards and storage capacity.

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“…Jun et al 12 constructed the impedance model of the porous electrode of the lithium‐ion battery and carried out a mathematical representation of the model's order reduction space. Li et al 13 realized the dynamic modal analysis and physical parameter identification of lithium‐ion batteries based on a simplified electrochemical model, but the external factors considered were relatively single and failed to fully consider the factors affecting aging.…”

Section: Introductionmentioning

confidence: 99%

A novel equivalent modeling method combined with the splice‐electrochemical polarization model and prior generalized inverse least‐square parameter identification for UAV lithium‐ion batteries

Peng

Shi

Wang

et al. 2022

Energy Science & Engineering

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The accuracy of lithium-ion battery state estimation is critical to the safety of unmanned aerial vehicles (UAVs). In this paper, aiming at the high-fidelity modeling of the UAV lithium-ion battery, a splice-electrochemical polarization model (S-EPM) for UAV lithium-ion battery is constructed by combining the traditional electrochemical model with the equivalent circuit model, which greatly improved the accuracy of the battery modeling. In addition, a novel prior generalized inverse least-squares algorithm is proposed. Also, based on this algorithm, the full-parameter identification and multicondition error analysis of the S-EPM are realized based on this algorithm. Finally, a targeted complex discharge rate test and a full-function charge-discharge test were designed to further verify the applicability of the S-EPM to complex conditions. The experimental results show that the voltage error of the model under each working condition is 5.50 and 3.0 mV, and the maximum percentage error ratio is 0.20% and 0.07%. This experiment can provide a theoretical basis for the combination of the electrochemical model and equivalent circuit model and the accurate estimation of internal state variables of lithium-ion batteries.improve the Nernst model, lithium-ion batteries, parameter identification, prior generalized inverse least square, splice-electrochemical polarization model | INTRODUCTIONThe power battery system is one of the most important components of an unmanned aerial vehicle (UAV), which has a great impact on the performance and safety of the whole aircraft. 1 The lithium-ion battery has become the most widely used power battery for aircraft due to its excellent performance. For UAVs, the highperformance battery management system can not only ensure the timeliness of the drone flight but also extend Energy Sci. Eng.

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Aging modes analysis and physical parameter identification based on a simplified electrochemical model for lithium-ion batteries

Cited by 57 publications

References 17 publications

Electrochemical Parameter Identification for Lithium-ion Battery Sources in Self-Sustained Transportation Energy Systems

Electrochemical Parameter Identification for Lithium-ion Battery Sources in Self-Sustained Transportation Energy Systems

The Impact of Aging-Preventive Algorithms on BESS Sizing under AGC Performance Standards

A novel equivalent modeling method combined with the splice‐electrochemical polarization model and prior generalized inverse least‐square parameter identification for UAV lithium‐ion batteries

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