Comparison of the capacity fade of Sony US 18650 cells charged with different protocols

Sikha, Godfrey; Ramadass, Premanand; Haran, Bala; White, Ralph E.; Popov, Branko N.

doi:10.1016/s0378-7753(03)00027-2

Cited by 99 publications

(75 citation statements)

References 11 publications

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“…The first step toward developing a physics-based model is to understand how parameters like the weight of the accessible active material and the state of charge within each electrode change as the cell ages. 4 In the present work, a simple mathematical model 25,26 is used to estimate the active material loss in each electrode as well as the state of charge of each electrode at the end of every hundred cycles. Figure 1 presents a schematic illustration of a lithium-ion battery with an individual spherical particle representing each intercalation electrode.…”

Section: Mathematical Modelmentioning

confidence: 99%

Parameter Estimation and Life Modeling of Lithium-Ion Cells

Santhanagopalan¹,

Zhang²,

Kumaresan³

et al. 2008

J. Electrochem. Soc.

133

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Lithium-ion pouch cells were cycled at five different temperatures ͑5, 15, 25, 35, and 45°C͒, and rate capability studies were performed after every hundred cycles. The data were used with a simple physics-based model to estimate parameters that capture the capacity fade in the cell, with cycling. The weight of active material within each electrode was estimated as a function of time, using rate capability data at the C/33 rate. The C-rate for these cells is 1.656 A. The capacity fade due to the loss of active material and that due to the loss of cyclable lithium were quantified. It was found that while the loss of cyclable lithium is the limiting cause for the capacity decay of the cell during the first 200 cycles, the loss of active carbon, which is the anode material, becomes limiting for these cells. The loss of active material leads to a drastic decrease in cell capacity at higher temperatures.

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Section: Mathematical Modelmentioning

confidence: 99%

Parameter Estimation and Life Modeling of Lithium-Ion Cells

Santhanagopalan¹,

Zhang²,

Kumaresan³

et al. 2008

J. Electrochem. Soc.

133

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“…Since these processes are diffusion controlled and slow, it is necessary that a low charging rate is used [3]. The rate capability loss is proportional to the value of the average current used [20]. Ultracapacitors are the other type of energy storage selected in this work.…”

Section: B Batteries and Ultracapacitormentioning

confidence: 99%

Evaluation of a Long Term System coupled with a Short Term System of a hydrogen-based microgrid

Serna

Tadeo

Yahyaoui

et al. 2017

2017 8th International Renewable Energy Congress (IREC)

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“…Each step has an effect on the charging time and battery lifetime. Several techniques have been introduced to improve the cycle life [5] ~ [8]. However, although these works are effective, they are all focused on the single type of EES elements or homogeneous EES systems.…”

Section: Background a Related Workmentioning

confidence: 99%

“…This metric captures the general condition of the EES elements and their ability to store and deliver energy compared to its initial state (i.e., compared to a fresh new EES element). Some researchers have worked on extending the lifetime of EES elements [5]~ [8]. However, they only focus on either a single EES element or a homogeneous EES system, which consists of a single type EES element array.…”

Section: Introductionmentioning

confidence: 99%

State of health aware charge management in hybrid electrical energy storage systems

Xie

Lin

Wang

et al. 2012

2012 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE)

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-This paper is the first to present an efficient charge management algorithm focusing on extending the cycle life of battery elements in hybrid electrical energy storage (HEES) systems while simultaneously improving the overall cycle efficiency. In particular, it proposes to apply a crossover filter to the power source and load profiles. The goal of this filtering technique is to allow the battery banks to stably (i.e., with low variation) receive energy from the power source and/or provide energy to the load device, while leaving the spiky (i.e., with high variation) power supply or demand to be dealt with by the supercapacitor banks. To maximize the HEES system cycle efficiency, a mathematical problem is formulated and solved to determine the optimal charging/discharging current profiles and charge transfer interconnect voltage, taking into account the power loss of the EES elements and power converters. To minimize the state of health (SoH) degradation of the battery array in the HEES system, we make use of two facts: the SoH of battery is better maintained if (i) the SoC swing is smaller, and (ii) the same SoC swing occurs at lower average SoC. Now then using the supercapacitor bank to deal with the high-frequency component of the power supply or demand, we can reduce the SoC swing for the battery array and lower the SoC of the array. A secondary helpful effect is that, for fixed and given amount of energy delivered to the load device, an improvement in the overall charge cycle efficiency of the HEES system translates into a further reduction in both the average SoC and the SoC swing of the battery array. The proposed charge management algorithm for a Li-ion battery -supercapacitor bank HEES system is simulated and compared to a homogeneous EES system comprised of Li-ion batteries only. Experimental results show significant performance enhancements for the HEES system, an increase of up to 21.9% and 4.82x in terms of the cycle efficiency and cycle life, respectively. Keywords: hybrid electrical energy storage system, charge management, state of health.

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Comparison of the capacity fade of Sony US 18650 cells charged with different protocols

Cited by 99 publications

References 11 publications

Parameter Estimation and Life Modeling of Lithium-Ion Cells

Parameter Estimation and Life Modeling of Lithium-Ion Cells

Evaluation of a Long Term System coupled with a Short Term System of a hydrogen-based microgrid

State of health aware charge management in hybrid electrical energy storage systems

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