Non-Zero Intercept Frequency: An Accurate Method to Determine the Integral Temperature of Li-Ion Batteries

Raijmakers, L.H.J.; Danilov, Dmitri L.; Lammeren, J.P.M. van; Lammers, Thieu; Bergveld, Henk Jan; Notten, Peter H. L.

doi:10.1109/tie.2016.2516961

Cited by 48 publications

(43 citation statements)

References 41 publications

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“…Therefore, if we accept the problem of crosstalk and are able to find a solution for this problem, impedance‐based temperature estimation would be a suitable method for application in battery packs. Both of the aforementioned artefacts are known to influence the EIS measurements, as was shown for (dis)charge currents by Raijmakers et al and for crosstalk interference . However, a solution on how to deal with the combination of these two artefacts and how to conduct impedance‐based temperature estimation for battery packs accurately has not yet been presented in the existing literature.…”

Section: Introductionmentioning

confidence: 86%

“…The measurement frequency can be selected by the user; however, the amplitude and measurement time of the excitation signal are fixed. Moreover, note that the SCS is a passive measurement device, ie, the sinusoidal excitation signal is generated by switching a resistor in parallel with the battery cell using a pulse‐density–modulated switching signal of a sine wave . In order to obtain a sine wave perturbation using the passive circuit, the aforementioned DC component is needed.…”

Section: Impedance‐based Temperature Estimationmentioning

confidence: 99%

“…For battery safety, accurate information of the internal temperature is important for early detection of thermal runaway. Considering the recent trend of battery pack supervision on the cell level, instead of measuring the surface temperature directly with external temperature sensors, the (average) internal temperature can be estimated using online electrochemical impedance spectroscopy (EIS) by inferring a temperature relation between the electrochemical battery impedance and the battery temperature . Using impedance‐based temperature estimation is a promising technique as it may provide faster and more accurate information on the (average) internal temperature of a battery cell.…”

Section: Introductionmentioning

confidence: 99%

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Towards impedance‐based temperature estimation for Li‐ion battery packs

et al. 2020

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Summary In order to meet the required power and energy demand of battery‐powered applications, battery packs are constructed from a multitude of battery cells. For safety and control purposes, an accurate estimate of the temperature of each battery cell is of vital importance. Using electrochemical impedance spectroscopy (EIS), the battery temperature can be inferred from the impedance. However, performing EIS measurements simultaneously at the same frequency on each cell in a battery pack introduces crosstalk interference in surrounding cells, which may cause EIS measurements in battery packs to be inaccurate. Also, currents flowing through the pack interfere with impedance measurements on the cell level. In this paper, we propose, analyse, and validate a method for estimating the battery temperature in a battery pack in the presence of these disturbances. First, we extend an existing and effective estimation framework for impedance‐based temperature estimation towards estimating the temperature of each cell in a pack in the presence of crosstalk and (dis)charge currents. Second, the proposed method is analysed and validated on a two‐cell battery pack, which is the first step towards development of this method for a full‐size battery pack. Monte Carlo simulations are used to find suitable measurement settings that yield small estimation errors and it is demonstrated experimentally that, over a range of temperatures, the method yields an accuracy of ±1°C in terms of bias, in the presence of both disturbances.

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Section: Introductionmentioning

confidence: 86%

Section: Impedance‐based Temperature Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Towards impedance‐based temperature estimation for Li‐ion battery packs

et al. 2020

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“…Three popular approaches to assess the heat generation in the batteries are illustrated in Eq. (1), which have been widely applied in real-time applications [24][25][26][27]. …”

Section: Battery Thermal Modelmentioning

confidence: 99%

“…The effect of battery SOC, SOH and temperature on the EIS measurements is analysed, and the excitation signal frequency is selected so that the measured EIS is dominated by temperature, and independent of the battery SOC and SOH. Raijmakers et al [99] also proposed battery sensorless temperature estimation methods using EIS measurements, and compared the performance of different EIS-based battery temperature estimation methods [100] under both thermal equilibrium states and dynamic load conditions. However, it is worth noting that those battery internal temperature estimation methods using the battery impedance measurement at a single frequency can only give an 'averaged' battery temperature, rather than the temperature distribution field or the peak internal temperature, under inhomogeneous or transient temperature distribution conditions.…”

Section: Internal Temperature Estimationmentioning

confidence: 99%

A brief review on key technologies in the battery management system of electric vehicles

et al. 2018

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Batteries have been widely applied in many high-power applications, such as electric vehicles (EVs) and hybrid electric vehicles, where a suitable battery management system (BMS) is vital in ensuring safe and reliable operation of batteries. This paper aims to give a brief review on several key technologies of BMS, including battery modelling, state estimation and battery charging. First, popular battery types used in EVs are surveyed, followed by the introduction of key technologies used in BMS. Various battery models, including the electric model, thermal model and coupled electro-thermal model are reviewed. Then, battery state estimations for the state of charge, state of health and internal temperature are comprehensively surveyed. Finally, several key and traditional battery charging approaches with associated optimization methods are discussed.

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Temperature prediction of lithium‐ion batteries based on electrochemical impedance spectrum: A review

Wang

Duan

et al. 2022

Intl J of Energy Research

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Summary With the rapid development of global electric vehicles, artificial intelligence, and aerospace, lithium‐ion batteries (LIBs) have become more and more widely used due to their high property. More and more disasters are caused by battery combustion. Among them, the temperature prediction of LIBs is the key to prevent the occurrence of fire. At present, using surface temperature sensor to measure the temperature of LIBs is the main method. High‐capacity LIB packs used in electric vehicles and grid‐tied stationary energy storage system essentially consist of thousands of individual LIB cells. Therefore, installing a physical sensor at each cell, especially at the cell core, is not practically feasible from the solution cost, space, and weight point of view. So developing a new method for battery temperature prediction has become an urgent problem to be solved. Electrochemical impedance spectroscopy (EIS) is a widely applied non‐destructive method of characterization of LIBs. In recent years, methods of predicting LIBs temperature by EIS have been developed. The prediction of LIBs temperature based on EIS has the advantages of high real‐time performance and prediction accuracy, and the device is simple and practical. The proposed method has a good development prospect in electric vehicles and other fields and can effectively solve the current problems of LIBs temperature prediction. Therefore, it is urgent to summarize these works to promote the next development. This review summarizes the main methods of using EIS to predict the temperature of LIBs in recent years, including the methods based on the impedance, phase shift, and intercept frequency. The principle and application of various methods are reviewed. The advantages and disadvantages of different methods and the future development direction are discussed. Highlights Use EIS to quickly and effectively predict the internal temperature changes of LIBs. No hardware temperature sensors and thermal model are required. The methods to predict battery temperature based on impedance, phase shift, and intercept frequency are reviewed.

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Non-Zero Intercept Frequency: An Accurate Method to Determine the Integral Temperature of Li-Ion Batteries

Cited by 48 publications

References 41 publications

Towards impedance‐based temperature estimation for Li‐ion battery packs

Towards impedance‐based temperature estimation for Li‐ion battery packs

A brief review on key technologies in the battery management system of electric vehicles

Temperature prediction of lithium‐ion batteries based on electrochemical impedance spectrum: A review

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