One-shot parameter identification of the Thevenin’s model for batteries: Methods and validation

Tian, Ning; Wang, Yebin; Chen, Jian; Fang, Huazhen

doi:10.1016/j.est.2020.101282

Cited by 36 publications

(5 citation statements)

References 68 publications

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“…The battery cells are assumed to be Samsung INR18650-25R, and we have identified their parameters (see Table II) and SoC/OCV relationship (see Fig. 3) from experiments using the approach in [29]. We approximate the SoC/OCV curve using a piecewise linear function with three segments that together span from zero to 100% SoC.…”

Section: Simulation Resultsmentioning

confidence: 99%

A Novel Modular, Reconfigurable Battery Energy Storage System: Design, Control, and Experimentation

Farakhor,

Wu,

Wang

et al. 2023

Preprint

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This paper presents a novel modular, reconfigurable battery energy storage system. The proposed design is characterized by a tight integration of reconfigurable power switches and DC/DC converters. This characteristic enables isolation of faulty cells from the system and allows fine power control for individual cells toward optimal system-level performance. An optimal power management approach is developed to extensively exploit the merits of the proposed design. Based on recedinghorizon convex optimization, this approach aims to minimize the total power losses in charging/discharging while allocating the power in line with each cell's condition to achieve state-ofcharge (SoC) and temperature balancing. By appropriate design, the approach manages to regulate the power of a cell across its full SoC range and guarantees the feasibility of the optimization problem. We perform extensive simulations and further develop a lab-scale prototype to validate the proposed system design and power management approach.

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Section: Simulation Resultsmentioning

confidence: 99%

A Novel Modular, Reconfigurable Battery Energy Storage System: Design, Control, and Experimentation

Farakhor,

Wu,

Wang

et al. 2023

Preprint

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“…Algorithms to estimate the state of the battery should be based on accurate battery modeling because the estimation performance of algorithms varies significantly depending on the accuracy of the battery modeling 41 . The battery has the properties of nonlinear systems owing to its chemical properties, and because it is difficult to mathematically define the chemical properties of a battery, an EECM can be constructed using electrical elements such as resistors and capacitors 42 . A model that reflects the characteristics of the battery can be created by using resistance to describe linear properties and capacitors to describe nonlinear properties.…”

Section: Combined Methods For Soc Estimationmentioning

confidence: 99%

“…41 The battery has the properties of nonlinear systems owing to its chemical properties, and because it is difficult to mathematically define the chemical properties of a battery, an EECM can be constructed using electrical elements such as resistors and capacitors. 42 A model that reflects the characteristics of the battery can be created by using resistance to describe linear properties and capacitors to describe nonlinear properties. In general, the first RC-ladder model is most commonly used because of its good accuracy and intuitive features.…”

Section: Battery Model Selection and Parameter Identificationmentioning

confidence: 99%

Elaborated EECM parameter identification reflecting adaptive weight for achieving a high‐accuracy SOC estimator

Kim

Park

Sin

et al. 2022

Energy Science & Engineering

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In this paper, a combined method is proposed to improve the accuracy of an electrical equivalent circuit model (EECM) and state-of-charge (SOC) estimation in situations where a lithium-ion battery is frequently charged and discharged.To calculate the terminal voltage in the dynamic region, where charging and discharging are frequently repeated at 0.1 s intervals, a specific region is determined and historical weight is selected, based on the amount of charge in that region, to reflect the model parameters. The historical weights are applied to the charging and discharging parameters to improve the estimated performance of the EECM in estimating the terminal voltage of the battery. Also, the improved EECM is applied to the extended Kalman filter to improve the performance of SOC estimation. A 21,700 cylindrical lithium nickel manganese cobalt oxide-type battery was used for verification, and the parameters required for modeling were extracted through experiments that were conducted to determine electrical characteristics. This paper compared two models not considering the history of charging or discharging characteristics with a model considering history to verify the accuracy of voltage and SOC estimation results. The result shows that the voltage and SOC estimation errors of the proposed combined method are the lowest in the three cases.

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“…Researchers studied different modeling techniques for LIBs and compared their results 12,27‐29 . One of the best results among these studies is the Thevenin ECM 30 . LTO batteries are one of the types of LIBs.…”

Section: Identification Of Lto Battery Modelmentioning

confidence: 99%

Nonlinear estimator‐based state of charge estimation for lithium titanate oxide battery in energy storage systems

Muratoglu

Alkaya

2023

Energy Storage

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Fuel savings, energy savings, and reduction of CO2 emissions are the key requirements in electric and hybrid electric vehicles (EVs and HEVs). These requirements are achieved through the usage of start‐stop systems, high‐rate energy boost during acceleration, and recovery of the energy during braking. Unfortunately, the commonly used lithium‐ion batteries are not a sufficient solution, although their performance is enhanced by supercapacitors. This hybrid system used is only a temporary solution. Therefore, lithium‐titanate‐oxide batteries (Li4Ti5O12—LTO), show high‐rate discharging and charging performance, high power capability, excellent cycle life, and improved cycle stability at wide‐rate temperatures and current rates are promising candidates for HEV and EV applications. There is a need to monitor the state of charge (SoC) for the reliability, performance, and safety of LTO batteries with the help of a battery management system. However, the conventional SoC estimation methods, such as open circuit voltage, Coulomb counting, artificial neural networks, fuzzy logic, and linear Kalman filter algorithm become insufficient. In this paper, a new nonlinear approach for the SoC estimation of an LTO battery is presented. The approach combines the static battery model and the sigma point Kalman filter (SPKF) algorithm while adapting to both constant load and dynamic load. The results show that the SPKF algorithm successfully estimated the SoC of the LTO battery under various initial SoC initialization values. It is also supported by various metric errors calculated.

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One-shot parameter identification of the Thevenin’s model for batteries: Methods and validation

Cited by 36 publications

References 68 publications

A Novel Modular, Reconfigurable Battery Energy Storage System: Design, Control, and Experimentation

A Novel Modular, Reconfigurable Battery Energy Storage System: Design, Control, and Experimentation

Elaborated EECM parameter identification reflecting adaptive weight for achieving a high‐accuracy SOC estimator

Nonlinear estimator‐based state of charge estimation for lithium titanate oxide battery in energy storage systems

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