A physics-based fractional order model and state of energy estimation for lithium ion batteries. Part II: Parameter identification and state of energy estimation for LiFePO 4  battery

Li, Xiaoyu; Pan, Ke; Fan, Guodong; Lu, Rengui; Zhu, Chunbo; Rizzoni, Giorgio; Canova, Marcello

doi:10.1016/j.jpowsour.2017.09.048

Cited by 53 publications

(19 citation statements)

References 35 publications

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“…For instance, see Ref. [23] for sequential parameter estimation procedures. The experiments are an OCP measurement for PE step 1, C-rate tests for kinetic parameters in PE step 2, and EIS data for improved identification of kinetic parameters in PE step 3.…”

Section: Parameterization Proceduresmentioning

confidence: 99%

“…Different objective functions were applied to different parts of the experimental data. For instance, Li et al applied three steps: high SOC and low current, low SOC and low current, and eventually considered a dynamic driving cycle [23]. Jin et al considered thermodynamic parameters and kinetic parameters independently for a P2D model as well as linear or logarithmic scaling for different parameters [17].…”

Section: Introductionmentioning

confidence: 99%

“…Pozna et al showed that even a first-order RC equivalent circuit battery model is not identifiable [30]. For fractional-order models (FOM) of lithium-ion batteries, parameter identifiability was assessed by Guo et al and Li et al [13,23]. FOM is based on Laplace transformation and provides lumped parameters which have to be estimated.…”

Section: Introductionmentioning

confidence: 99%

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Practical identifiability of electrochemical P2D models for lithium-ion batteries

2021

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Electrochemical models play a significant role in today’s rapid development and enhancement of lithium-ion batteries. For instance, they are applied for design and process optimization. More recently, model and parameter identifiability are gaining interest as thorough model parameterization is key to reliable simulation results. Especially electrochemical models are often prone to unidentifiability and overfitting due to their high number of adjustable parameters. In this article, the most common electrochemical peudo-2D model of a lithium-ion battery is parameterized. A three-step procedure is applied which considers quasi-static 3-electrode measurements of the open-circuit potential, C-rate tests, and electrochemical impedance spectra. Identifiability of each step is discussed in-depth and a general guidance for future parameterizations is derived. The conducted study reveals the insufficiency of open-circuit potential and C-rate tests to fully parameterize the electrochemical model. Highly dynamic tests, e.g., impedance spectroscopy, are required to resolve the ambiguity of diffusive and electric processes under quasi-static conditions. Any parameterization of electrochemical models requires experimental data of electrode-resolved tests, as well as a combination of quasi-static and highly dynamic tests. The results of this study provide guidance for the use of electrochemical models in applied sciences and industry. Graphic abstract

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Section: Parameterization Proceduresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Practical identifiability of electrochemical P2D models for lithium-ion batteries

2021

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“…By extending these estimation approaches to FOMs, some initial work has recently been attempted to estimate the SoC of Li-ion cells [65,114] and of supercapacitors [30]. In 2017, Li et al [115] applied an adaptive fractionalorder extended Kalman filter to the SoE estimation for Li-ion batteries in EVs. In the context of lead-acid batteries, Cugnet et al [82] pioneered a fractional resistance-estimator to indicate its crankability in starting a vehicle.…”

Section: Challenges and Future Prospectsmentioning

confidence: 99%

A review of fractional-order techniques applied to lithium-ion batteries, lead-acid batteries, and supercapacitors

Zou

Zhang

et al. 2018

Journal of Power Sources

383

149

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Electrochemical energy storage systems play an important role in diverse applications, such as electrified transportation and integration of renewable energy with the electrical grid. To facilitate model-based management for extracting full system potentials, proper mathematical models are imperative. Due to extra degrees of freedom brought by differentiation derivatives, fractional-order models may be able to better describe the dynamic behaviors of electrochemical systems. This paper provides a critical overview of fractional-order techniques for managing lithium-ion batteries, lead-acid batteries, and supercapacitors. Starting with the basic concepts and technical tools from fractional-order calculus, the modeling principles for these energy systems are presented by identifying disperse dynamic processes and using electrochemical impedance spectroscopy. Available battery/supercapacitor models are comprehensively reviewed, and the advantages of fractional types are discussed. Two case studies demonstrate the accuracy and computational efficiency of fractional-order models. These models offer 15-30% higher accuracy than their integer-order analogues, but have reasonable complexity. Consequently, fractional-order models can be good candidates for the development of advanced battery/supercapacitor management systems. Finally, the main technical challenges facing electrochemical energy storage system modeling, state estimation, and control in the fractional-order domain, as well as future research directions, are highlighted.

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“…Within the last two decades, rechargeable cells especially Li-ion cells have received a relatively wide application for large-scale electric storage, mostly in EVs (electric vehicles) and digital products such as mobile phones 1 for its terrific superiority of high energy density, long lifetime, high voltage, and low self-discharge ratio. [2][3][4] For instance, during the year 2000, the worldwide production of lithium-ion batteries reached about 500 million cells. Moreover, from 2000 to 2010, it increased with an annual growth rate of 800%.…”

Section: Introductionmentioning

confidence: 99%

A review of the estimation and heating methods for lithium‐ion batteries pack at the cold environment

Peng

Chen

Garg

et al. 2019

Energy Science & Engineering

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The application of lithium‐ion batteries especially for electric vehicles has been limited by the factors of safety, lifetime, charging time, and cost. One of the principal limitations is that the performance of Li‐ion batteries drops intensely in a cold environment. Cold environment dramatically reduces the available capacity of the batteries and increases its internal impedance at the same time. Therefore, the estimation of state‐of‐health is of great importance in battery performance evaluation and lifetime prediction. Furthermore, the heating methods need to be developed to ensure that batteries work in abnormal temperature conditions. This paper conducts a comprehensive review specifically on the poor performance of lithium‐ion cells under severe conditions. The content contains three sections. First, a comprehensive study on the aging mechanisms of lithium‐ion batteries at cold temperatures is undertaken. Second, the estimation methods of the health state of the batteries are conducted, which is vital to understand the fundamentals and quantify the performance and aging effects for lithium‐ion batteries. Third, the heating methods are classified and studied in detail to reduce the degradation mechanism and promote the performance of lithium‐ion batteries under sub‐zero conditions.

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A physics-based fractional order model and state of energy estimation for lithium ion batteries. Part II: Parameter identification and state of energy estimation for LiFePO 4 battery

Cited by 53 publications

References 35 publications

Practical identifiability of electrochemical P2D models for lithium-ion batteries

Practical identifiability of electrochemical P2D models for lithium-ion batteries

A review of fractional-order techniques applied to lithium-ion batteries, lead-acid batteries, and supercapacitors

A review of the estimation and heating methods for lithium‐ion batteries pack at the cold environment

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