Probing lithium-ion batteries' state-of-charge using ultrasonic transmission – Concept and laboratory testing

Gold, Lukas; Bach, Tobias; Virsik, Wolfgang; Schmitt, Angelika; Müller, Jana; Staab, T.E.M.; Sextl, Gerhard

doi:10.1016/j.jpowsour.2017.01.090

Cited by 165 publications

(106 citation statements)

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“…The advantages of a fixed flow channel and transducer geometry, and isotropic/homogeneous liquid state is that it allows for relatively straight-forward SOC determination. This illustrated the broad applicability and effectiveness of ultrasonic methods for determining battery properties.Finally, Gold et al 7 applied this technique to a single lithium-ion pouch cell and demonstrated a proof of concept for SOC determination over one cycle, but they stopped short of showing how this technique can be incorporated into a practical BMS for SOC determination over many cycles. They applied ultrasonic pulses at a frequency which is an order of magnitude lower than the one used by Hsieh et al (200 kHz vs. 2.25 MHz).…”

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confidence: 99%

“…Recently, ultrasonic techniques have been applied to batteries to understand cell behavior. [5][6][7][8] Hsieh et al 5 first introduced the concept, applying 2.25 MHz ultrasonic pulses in transmission and pulse-echo modes to lithium-ion pouch cells, and both lithium-ion and alkaline cylindrical cells. Ultrasonic waveforms were measured over a time of flight range of ≤ 20 μs, and measurements were taken across all states of charge during cycling.…”

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confidence: 99%

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State of Charge and State of Health Estimation Using Electrochemical Acoustic Time of Flight Analysis

Davies

Knehr

Tassell³

et al. 2017

J. Electrochem. Soc.

156

103

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Ultrasonic analysis was used to predict the state of charge and state of health of lithium-ion pouch cells that have been cycled for several hundred cycles. The repeatable ultrasonic trends are reduced to two key metrics: time of flight shift and total signal amplitude, which are then used with voltage data in a supervised machine learning technique to build a model for state of charge (SOC) prediction. Using this model, cell SOC is predicted to ∼1% accuracy for both lithium cobalt oxide and lithium iron phosphate cells. Elastic wave propagation theory is used to explain that the changes in ultrasonic signal are related to changes in the material properties of the active materials (i.e., elastic modulus and density) during cycling. Finally, we show the machine learning model can accurately predict cell state of health with an error ∼1%. This is accomplished by extending the data inputs into the model to include full ultrasonic waveforms at top of charge. A key component of an electric vehicle is the battery management system (BMS), which is responsible for controlling the operating conditions on a given battery cell (or stack of cells) in order to optimize the performance and lifetime of the full battery system. The most effective battery management systems must be able to track battery state of charge (SOC), state of health (SOH) and cell failure, including early prediction of catastrophic failure. Despite the importance of this task, being able to reliably determine SOC, SOH and failure at low cost still presents a significant challenge. A range of methods exist at present, however the simplest methods can prove inaccurate, and more complex methods are not suitable for low-cost, in-operando SOC determination. 1-3For instance, in its most common implementation, SOC prediction consists of voltage monitoring (direct measurement) combined with coulomb-counting (book-keeping).1 This can present challenges for a variety of reasons. First, for voltage measurements, the flatness of voltage readings over the majority of battery capacity, especially for lithium iron phosphate (LFP) cells, presents difficulties. 4 Furthermore, voltage fade, changing cell impedances, and varying discharge rates impact measured voltage, obscuring true SOC. Second, coulomb counting is also an inexact science, as discharge rate, environmental factors such as temperature, and cell degradation can all impact the actual capacity for any given discharge. This can lead to a cycle of abuse, whereby discharge conditions lead to an incorrect estimate of SOC, and therefore the cell becomes inadvertently over-discharged. This causes damage to the cell, which leads to further inaccuracy in the SOC prediction, resulting in continued over-discharging and cell damage. Effectively, a battery "death-spiral" ensues.One method to further increase the accuracy of battery management systems is to introduce a technique that can directly measure the physical state of the battery to enhance the determination of SOC, SOH, and cell failure, especially when applied i...

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mentioning

confidence: 99%

mentioning

confidence: 99%

State of Charge and State of Health Estimation Using Electrochemical Acoustic Time of Flight Analysis

Davies

Knehr

Tassell³

et al. 2017

J. Electrochem. Soc.

156

103

View full text Add to dashboard Cite

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“…Other measurements, such as the ultrasound velocity [140], magnetic field intensity [141], or mechanical stress [142] can also be used for some special SOC estimation in research. Nevertheless, the feasibility is poor because additional sensors are needed.…”

Section: Estimation Algorithmmentioning

confidence: 99%

State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles

et al. 2018

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Sate of charge (SOC) accurate estimation is one of the most important functions in a battery management system for battery packs used in electrical vehicles. This paper focuses on battery SOC estimation and its issues and challenges by exploring different existing estimation methodologies. The key technologies of lithium-ion battery state estimation methodologies of the electrical vehicles categorized under five groups, such as the conventional method, adaptive filter algorithm, learning algorithm, nonlinear observer, and the hybrid method, are explored in an in-depth analysis. Lithium-ion battery characteristic, battery model, estimation algorithm, and cell unbalancing are the most important factors that affect the accuracy and robustness of SOC estimation. Finally, this paper concludes with the challenges of SOC estimation and suggests other directions for possible research efforts.

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“…Ultrasonic methods have been used for non-destructive testing of materials for decades. Recently, measurements with ultrasonic acoustic waves on battery cells have gained attention as a non-destructive tool to evaluate the mechanical characteristics of lithium-ion batteries [27,28], and to use the acquired parameters for state of health (SOH) and state of charge (SOC) estimation [29,30]. Using piezoelectric transducers, which can either be glued or pressed onto the cell casing, a voltage pulse excites a mechanical compressional wave.…”

Section: Ultrasonic Acoustic Guided Wave Measurementsmentioning

confidence: 99%

In-Operando Impedance Spectroscopy and Ultrasonic Measurements during High-Temperature Abuse Experiments on Lithium-Ion Batteries

et al. 2020

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Lithium-Ion batteries are used in ever more demanding applications regarding operating range and safety requirements. This work presents a series of high-temperature abuse experiments on a nickel-manganese-cobalt oxide (NMC)/graphite lithium-ion battery cell, using advanced in-operando measurement techniques like fast impedance spectroscopy and ultrasonic waves, as well as strain-gauges. the presented results show, that by using these methods degradation effects at elevated temperature can be observed in real-time. These methods have the potential to be integrated into a battery management system in the future. Therefore they make it possible to achieve higher battery safety even under the most demanding operating conditions.

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Probing lithium-ion batteries' state-of-charge using ultrasonic transmission – Concept and laboratory testing

Cited by 165 publications

References 50 publications

State of Charge and State of Health Estimation Using Electrochemical Acoustic Time of Flight Analysis

State of Charge and State of Health Estimation Using Electrochemical Acoustic Time of Flight Analysis

State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles

In-Operando Impedance Spectroscopy and Ultrasonic Measurements during High-Temperature Abuse Experiments on Lithium-Ion Batteries

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