On state-of-charge determination for lithium-ion batteries

Li, Zhe; Huang, Jun; Liaw, Bor Yann; Zhang, Jianbo

doi:10.1016/j.jpowsour.2017.03.001

Cited by 245 publications

(98 citation statements)

References 142 publications

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“…This manifests itself as a change in the properties of the electrode materials as a whole, which leads to shifts in the transmitted ultrasonic signal at MHz frequencies. Despite cell variability 2,3,11 and measurement noise, we demonstrate that practical determination of state of charge is possible, beyond a proof of concept. This technique relies on an ultrasonic library of similar cells being generated, with the data then fed into a machine learning algorithm.…”

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confidence: 96%

“…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][2][3] For 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.…”

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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.

147

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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confidence: 96%

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.

147

103

View full text Add to dashboard Cite

show abstract

“…Many algorithms were developed and are currently under study to provide a value of residual capacity that is as precise as possible. These algorithms often refer to a dimensionless quantity that is the State Of Charge (SOC), defined in Equation (1) [14,39]. To consider the capacity reduction with temperature, current and number of cycles, different strategies can be implemented such as the definition of indices or Look Up Tables (LUTs) after proper tests [40,41].…”

Section: Battery Modellingmentioning

confidence: 99%

Battery Modelling and Simulation Using a Programmable Testing Equipment

2018

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Abstract:In this paper, the study and modelling of a lithium-ion battery cell is presented. To test the considered cell, a battery testing system was built using two programmable power units: an electronic load and a power supply. To communicate with them, a software/hardware interface was implemented within the National Instruments (NI) LabVIEW environment. This dedicated laboratory equipment can be used to apply charging/discharging cycles according to user defined load profiles. The battery modelling and the parameters identification procedure are described. The model was used to estimate the State Of Charge (SOC) under dynamic loading conditions. The most spread techniques used in the field of battery modelling and SOC estimation are implemented and compared.

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“…The open circuit battery voltage which depends on the battery current, state of charge (SOC) and hysteresis phenomenon of the battery with the temperature ignored as follows [14]. The value of battery voltage in the discharge mode (I > 0) and charge mode (I < 0) is given by; …”

Section: E Battery Systemmentioning

confidence: 99%

Clean Power Grid with Tidal Power Generations

2017

Dec. 7-8, 2017 Paris (France) CCCME-17, MAIE-17, CAEBS-17, L2hss-17 &Amp; LEBHM-17

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On state-of-charge determination for lithium-ion batteries

Cited by 245 publications

References 142 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

Battery Modelling and Simulation Using a Programmable Testing Equipment

Clean Power Grid with Tidal Power Generations

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