Accelerated Internal Resistance Measurements of Lithium-Ion Cells to Support Future End-of-Life Strategies for Electric Vehicles

Grandjean, Thomas R. B.; Groenewald, Jakobus; McGordon, Andrew; Widanage, Widanalage Dhammika; Marco, James

doi:10.3390/batteries4040049

Cited by 33 publications

(14 citation statements)

References 47 publications

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“…Thus, it is difficult to obtain parameters for SLBs' RUL because the information of the batteries on their fresh state is normally unknown [43]. However, some authors have used empirical approximations, such as Arrhenius equation (takes temperature as an accelerated aging factor) and power law (takes mechanical/electrical stress as an accelerated aging factor), to model capacity loss on batteries as a function of cycle number [30,44].…”

Section: White-box Methodsmentioning

confidence: 99%

A Circular Economy of Electrochemical Energy Storage Systems: Critical Review of SOH/RUL Estimation Methods for Second-Life Batteries

Montoya-Bedoya¹,

Sabogal-Moncada²,

Garcia-Tamayo³

et al. 2020

Green Energy and Environment

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Humanity is facing a gloomy scenario due to global warming, which is increasing at unprecedented rates. Energy generation with renewable sources and electric mobility (EM) are considered two of the main strategies to cut down emissions of greenhouse gasses. These paradigm shifts will only be possible with efficient energy storage systems such as Li-ion batteries (LIBs). However, among other factors, some raw materials used on LIB production, such as cobalt and lithium, have geopolitical and environmental issues. Thus, in a context of a circular economy, the reuse of LIBs from EM for other applications (i.e., second-life batteries, SLBs) could be a way to overcome this problem, considering that they reach their end of life (EoL) when they get to a state of health (SOH) of 70-80% and still have energy storage capabilities that could last several years. The aim of this chapter is to make a review of the estimation methods employed in the diagnosis of LIB, such as SOH and remaining useful life (RUL). The correct characterization of these variables is crucial for the reassembly of SLBs and to extend the LIBs operational lifetime.

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Section: White-box Methodsmentioning

confidence: 99%

A Circular Economy of Electrochemical Energy Storage Systems: Critical Review of SOH/RUL Estimation Methods for Second-Life Batteries

Montoya-Bedoya¹,

Sabogal-Moncada²,

Garcia-Tamayo³

et al. 2020

Green Energy and Environment

View full text Add to dashboard Cite

show abstract

“…There are also some other cases where the EOL is defined by the relative resistance instead of the relative capacity. In those cases, the EOL is typically set to 200% of the initial resistance of the battery [8,9]. The relative capacity is commonly linked to high-energy EV applications and the relative resistance threshold is normally related to high-power EV applications [9].…”

Section: Introductionmentioning

confidence: 99%

“…Considering these three hypotheses, the required resistance increase (∆R) that leads to overcome the minimum voltage threshold (U EOD ) is calculated at each loop iteration based on the electric equivalent circuit equations, see Equation (8).…”

mentioning

confidence: 99%

“…else if I CHA > 0 7: For the second issue, firstly, it is assumed that the real resistance increase (∆R) is below a certain resistance increase (∆R 0 ). This resistance increase (∆R 0 ) is calculated based on the two stated hypotheses and by neglecting the effect of the resistance increase on the battery response, see Equation (8). Afterwards, a bisection method (a numerical method) is applied with a limit of iterations (N BM = 5), see Algorithm 3.…”

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confidence: 99%

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Application Dependent End-of-Life Threshold Definition Methodology for Batteries in Electric Vehicles

et al. 2021

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The end-of-life event of the battery system of an electric vehicle is defined by a fixed end-of-life threshold value. However, this kind of end-of-life threshold does not capture the application and battery characteristics and, consequently, it has a low accuracy in describing the real end-of-life event. This paper proposes a systematic methodology to determine the end-of-life threshold that describes accurately the end-of-life event. The proposed methodology can be divided into three phases. In the first phase, the health indicators that represent the aging behavior of the battery are defined. In the second phase, the application specifications and battery characteristics are evaluated to generate the end-of-life criteria. Finally, in the third phase, the simulation environment used to calculate the end-of-life threshold is designed. In this third phase, the electric-thermal behavior of the battery at different aging conditions is simulated using an electro-thermal equivalent circuit model. The proposed methodology is applied to a high-energy electric vehicle application and to a high-power electric vehicle application. The stated hypotheses and the calculated end-of-life threshold of the high-energy application are empirically validated. The study shows that commonly assumed 80 or 70% EOL thresholds could lead to mayor under or over lifespan estimations.

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“…But this method can not only take a long test time and have high cost, but also cause irreversible destructive to the battery. Therefore, it is very important to predict the RUL of lithium‐ion battery accurately in a relatively short time …”

Section: Introductionmentioning

confidence: 99%

Remaining useful life prediction of lithium‐ion battery based on extended Kalman particle filter

et al. 2019

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Summary Scientific estimation and prediction of the state of health (SOH) of lithium‐ion battery, especially the remaining useful life (RUL), has important significance to guarantee the battery safety and reliability in the full life cycle to avoid catastrophic accidents as much as possible. In order to accurately predict the RUL of the lithium‐ion battery, this paper firstly analyzes the problems of the standard particle filter (PF). Then, a novel extended Kalman particle filter (EKPF) is proposed, in which the extended Kalman filter (EKF) is used as the sampling density function to optimize PF algorithm. The life cycle tests are designed and carried out to get accurate and reliable data for the RUL prediction. And, the aging properties of lithium‐ion battery are analyzed in detail. The RUL prediction is done based on the established capacity degradation model and the proposed EKPF method. Results show that the RUL prediction error of the proposed method is less than 5%, which has higher precision compared with the standard PF method and can be used both offline and online.

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Accelerated Internal Resistance Measurements of Lithium-Ion Cells to Support Future End-of-Life Strategies for Electric Vehicles

Cited by 33 publications

References 47 publications

A Circular Economy of Electrochemical Energy Storage Systems: Critical Review of SOH/RUL Estimation Methods for Second-Life Batteries

A Circular Economy of Electrochemical Energy Storage Systems: Critical Review of SOH/RUL Estimation Methods for Second-Life Batteries

Application Dependent End-of-Life Threshold Definition Methodology for Batteries in Electric Vehicles

Remaining useful life prediction of lithium‐ion battery based on extended Kalman particle filter

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