Embedded real-time fractional-order equivalent circuit model for internal resistance estimation of lithium-ion cells

Bensaad, Yassine; Friedrichs, Fabian; Baumhöfer, Thorsten; Eswein, Mathias; Bähr, Judith; Fill, Alexander; Birke, Kai Peter

doi:10.1016/j.est.2023.107516

Cited by 11 publications

(5 citation statements)

References 31 publications

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“…The degradation mechanism of batteries has been well explained in past research. Existing methods for battery aging include electrochemical models [22], equivalent circuit models [23], and empirical models [24], all of which require long-term (days or months) charge/discharge data to quantify battery aging. However, in V2G scheduling, the time frame is often less than a day or even several hours, rendering the above methods unsuitable for V2G scheduling.…”

Section: Magnitude-based Rain-flow Cycle Countingmentioning

confidence: 99%

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Online Optimization of Vehicle-to-Grid Scheduling to Mitigate Battery Aging

Zhang,

Ikram,

2024

Energies

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The penetration of electric vehicles (EVs) in vehicle-to-grid (V2G) interaction can effectively assist the grid in achieving frequency regulation and peak load balancing. However, the customer perceives that participating in V2G services would result in the additional cycling of the battery and the accelerated aging of the EVs’ power battery, which has become a major obstacle to the widespread adoption of V2G services. Most existing methods require long-term cycling data and battery parameters to quantify battery aging, which is not suitable for the V2G scenario with large-scale and short-time intervals. Consequently, the real-time and accurate quantification of battery aging for optimization remains a challenge. This study proposes a charging scheduling method for EVs that can accurately and online quantify battery aging. Firstly, V2G scheduling is formulated as an optimization problem by defining an online sliding window to collect real-time vehicle information on the grid, enabling online optimization. Secondly, battery aging is more accurately quantified by proposing a novel amplitude-based rain-flow cycle counting (MRCC) method, which utilizes the charging information of the battery within a shorter time period. Lastly, an intelligent optimization algorithm is employed to optimize the charging and discharging power of EVs, aiming to minimize grid fluctuations and battery aging. The proposed method is validated using a V2G scenario with 50 EVs with randomly generated behaviors, and the results demonstrate that the proposed online scheduling method not only reduces the EFCC of the battery by 8.4%, but also achieves results close to global optimization.

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Section: Magnitude-based Rain-flow Cycle Countingmentioning

confidence: 99%

“…Existing battery aging models mainly include electrochemical models [22], equivalent circuit models [23], and empirical models [24]. The models mentioned above require either a large number of accurate chemical parameters or extensive data fitting.…”

Section: Introductionmentioning

confidence: 99%

Online Optimization of Vehicle-to-Grid Scheduling to Mitigate Battery Aging

Zhang,

Ikram,

2024

Energies

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“…ZARC elements are commonly employed to represent the SEI, while Warburg impedances are used to characterise diffusion. The use of these elements and their phase shape can are described in [13,62]. The use of these frequency-dependent elements requires precise characterisation.…”

Section: Electrochemical Ecmmentioning

confidence: 99%

Modelling and Estimation in Lithium-Ion Batteries: A Literature Review

Martí-Florences,

Cecilia,

Costa-Castelló

2023

Energies

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Lithium-ion batteries are widely recognised as the leading technology for electrochemical energy storage. Their applications in the automotive industry and integration with renewable energy grids highlight their current significance and anticipate their substantial future impact. However, battery management systems, which are in charge of the monitoring and control of batteries, need to consider several states, like the state of charge and the state of health, which cannot be directly measured. To estimate these indicators, algorithms utilising mathematical models of the battery and basic measurements like voltage, current or temperature are employed. This review focuses on a comprehensive examination of various models, from complex but close to the physicochemical phenomena to computationally simpler but ignorant of the physics; the estimation problem and a formal basis for the development of algorithms; and algorithms used in Li-ion battery monitoring. The objective is to provide a practical guide that elucidates the different models and helps to navigate the different existing estimation techniques, simplifying the process for the development of new Li-ion battery applications.

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“…Two distinct subtypes can be identified. Electrochemical ECMs employ a combination of electrical components along with Constant Phase Elements to replicate the cell's frequency response [40,41]. Phenomenological ECMs represent a purely electric circuit that emulates the dynamic behaviour of the battery.…”

Section: Introductionmentioning

confidence: 99%

SoC Estimation in Lithium-Ion Batteries with Noisy Measurements and Absence of Excitation

Martí-Florences,

Piñol,

Clemente

et al. 2023

Batteries

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Accurate State-of-Charge estimation is crucial for applications that utilise lithium-ion batteries. In real-time scenarios, battery models tend to present significant uncertainty, making it desirable to jointly estimate both the State of Charge and relevant unknown model parameters. However, parameter estimation typically necessitates that the battery input signals induce a persistence of excitation property, a need which is often not met in practical operations. This document introduces a joint state of charge/parameter estimator that relaxes this stringent requirement. This estimator is based on the Generalized Parameter Estimation-Based Observer framework. To the best of the authors’ knowledge, this is the first time it has been applied in the context of lithium-ion batteries. Its advantages are demonstrated through simulations.

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Embedded real-time fractional-order equivalent circuit model for internal resistance estimation of lithium-ion cells

Cited by 11 publications

References 31 publications

Online Optimization of Vehicle-to-Grid Scheduling to Mitigate Battery Aging

Online Optimization of Vehicle-to-Grid Scheduling to Mitigate Battery Aging

Modelling and Estimation in Lithium-Ion Batteries: A Literature Review

SoC Estimation in Lithium-Ion Batteries with Noisy Measurements and Absence of Excitation

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