Semi-Empirical Capacity Fading Model for SoH Estimation of Li-Ion Batteries

Singh, Preetpal; Chen, Che; Tan, Cher Ming; Huang, Shyh‐Chin

doi:10.3390/app9153012

Cited by 55 publications

(19 citation statements)

References 29 publications

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“…Only one battery is tested under each condition due to time and resources limitations. However, as the model employed here has been verified in the work by Preetpal et al [17] on different charge-discharge cycles for LiB, the results obtained in this work are presented with confidence despite the small sample size. Verification of the model using another 3 cells from the same batch will be performed later.…”

Section: Methodssupporting

confidence: 76%

“…Both errors are within the acceptable limit of 10%. From Figure 5, we can see that the estimation error of using the SECF model is sufficiently accurate for predicting the SoH of its own cell at larger cycle number, and this has been elaborated in the work by Preetpal et al [17]. We can also see that it is fairly accurate when it is applied to the cells at different C rates.…”

Section: Resultssupporting

confidence: 53%

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Accurate Real Time On-Line Estimation of State-of-Health and Remaining Useful Life of Li ion Batteries

2020

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Inaccurate state-of-health (SoH) estimation of battery can lead to over-discharge as the actual depth of discharge will be deeper, or a more-than-necessary number of charges as the calculated SoC will be underestimated, depending on whether the inaccuracy in the maximum stored charge is over or under estimated. Both can lead to increased degradation of a battery. Inaccurate SoH can also lead to the continuous use of battery below 80% actual SoH that could lead to catastrophic failures. Therefore, an accurate and rapid on-line SoH estimation method for lithium ion batteries, under different operating conditions such as varying ambient temperatures and discharge rates, is important. This work develops a method for this purpose, and the method combines the electrochemistry-based electrical model and semi-empirical capacity fading model on a discharge curve of a lithium-ion battery for the estimation of its maximum stored charge capacity, and thus its state of health. The method developed produces a close form that relates SoH with the number of charge-discharge cycles as well as operating temperatures and currents, and its inverse application allows us to estimate the remaining useful life of lithium ion batteries (LiB) for a given SoH threshold level. The estimation time is less than 5 s as the combined model is a closed-form model, and hence it is suitable for real time and on-line applications.

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Section: Methodssupporting

confidence: 76%

Section: Resultssupporting

confidence: 53%

Accurate Real Time On-Line Estimation of State-of-Health and Remaining Useful Life of Li ion Batteries

2020

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“…Due to new applications for models and a significant increase of computational power, a wide variety of models has been developed and different applications have been addressed, e.g., models have been used to quantify solid-electrolyte interface (SEI) formation and aging [14,34,39]. The variety of models is summarized in reviews about modeling of LIBs with focus on systems engineering, multi-scale modeling, and state estimation in electric cars, respectively [12,19,28,33].…”

Section: Introductionmentioning

confidence: 99%

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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“…Zhang, Miao, & Liu, 2017;Tang et al, 2019;Perez et al, 2018), polynomial (Micea, Ungurean, Cârstoiu, & Groza, 2011), sigmoid (Johnen et al, 2020) or a combination of these (Xing, Ma, Tsui, & Pecht, 2013). More complicated models also account for differences in C-rates and temperatures (Ji et al, 2020;Singh, Chen, Tan, & Huang, 2019). Other analytical model may be based on other relationships, such as a current-time constant (J.…”

Section: Empirical/analytical Modelsmentioning

confidence: 99%

Data-Driven Diagnostics and Prognostics for Modelling the State of Health of Maritime Battery Systems – a Review

Vanem¹,

Alnes

Lam³

2021

PHM_CONF

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Battery systems are becoming an increasingly attractive alternative for powering ocean going ships, and the number of fully electric or hybrid ships relying on battery power for propulsion and maneuvering is growing. In order to ensure the safety of such electric ships, it is of paramount importance to monitor the available energy that can be stored in the batteries, and classification societies typically require that the state of health of the batteries can be verified by independent tests – annual capacity tests. However, this paper discusses data-driven diagnostics for state of health modelling for maritime battery systems based on operational sensor data collected from the batteries as an alternative approach. Thus, this paper presents a comprehensive review of different data-driven approaches to state of health modelling, and aims at giving an overview of current state of the art. Furthermore, the various methods for data-driven diagnostics are categorized in a few overall approaches with quite different properties and requirements with respect to data for training and from the operational phase. More than 300 papers have been reviewed, most of which are referred to in this paper. Moreover, some reflections and discussions on what types of approaches can be suitable for modelling and independent verification of state of health for maritime battery systems are presented.

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Semi-Empirical Capacity Fading Model for SoH Estimation of Li-Ion Batteries

Cited by 55 publications

References 29 publications

Accurate Real Time On-Line Estimation of State-of-Health and Remaining Useful Life of Li ion Batteries

Accurate Real Time On-Line Estimation of State-of-Health and Remaining Useful Life of Li ion Batteries

Practical identifiability of electrochemical P2D models for lithium-ion batteries

Data-Driven Diagnostics and Prognostics for Modelling the State of Health of Maritime Battery Systems – a Review

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