Influence of memory effect on the state-of-charge estimation of large-format Li-ion batteries based on LiFePO4 cathode

Shi, Wei; Wang, Jiulin; Zheng, Jianming; Jiang, Jiuchun; Viswanathan, Vilayanur V.; Zhang, Ji‐Guang

doi:10.1016/j.jpowsour.2016.02.041

Cited by 19 publications

(23 citation statements)

References 26 publications

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“…30,31 Therefore, it is important to minimize the polarization effect in practical applications. In this section, we investigated the influence of the particle size distribution on the polarization of LiFePO 4 electrode.…”

Section: Resultsmentioning

confidence: 99%

Comprehensive Study of the Polarization Behavior of LiFePO₄Electrodes Based on a Many-Particle Model

Kondo

Sasaki

Barai

et al. 2018

J. Electrochem. Soc.

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The unique polarization behavior of LiFePO 4 electrodes was investigated using mathematical simulations with a many-particle model, which have a non-monotonic potential profile for each LiFePO 4 particle, and analyzed by the active population concept. The present simulations reveal two known polarization behaviors, namely the memory effect and path dependence. Notably, a hitherto unknown polarization behavior, the so-called relaxation-induced polarization (RIP), was also identified. The memory effect requires, at a minimum, a sequential four-step operation. By comparison, RIP is triggered only by a one-step operation, namely a long rest. In effect, the polarization associated with the memory effect and RIP was caused by a reduction of the active particles in the two-phase region via relaxation during a rest. The path-dependence mechanism was attributed to kinetically inhomogeneous reactions for each particle. We further found that narrowing the particle size distribution was an effective means to reduce polarization viz. the memory effect and path dependence of LiFePO 4 electrodes. However, RIP could not be suppressed by narrowing the particle size distribution. A comprehensive understanding of these polarization behaviors with our model provides a more accurate way to estimate the state of charge in Li-ion batteries with LiFePO 4 electrodes. As a promising cathode material, LiFePO 4 has been commercialized in Li-ion batteries for power tools and electric vehicles because of its low cost, high power, and thermal stability. Unlike other current commercial cathode materials, LiFePO 4 undergoes a two-phase reaction between a Li-rich phase and a Li-poor phase with a flat potential profile during charging and discharging.1 It also shows two sloping potential regions at each limit in the Li composition (Li x FePO 4 , 0 ≤ x ≤ 1) corresponding to the solid-solution reaction regions. 2,3LiFePO 4 has been reported to exhibit a number of unique polarization behaviors, including voltage hysteresis, 4 path dependence, 5 and memory effect. 6 Voltage hysteresis refers to the phenomenon wherein the charge and discharge potentials of a LiFePO 4 electrode do not collapse even at an extremely low rate (C/1000).4 Path dependence refers to a change of polarization in the charge/discharge profiles depending on whether the previous process is charging or discharging.5 Finally, memory effect refers to an unusual polarization bump during charge/discharge, at a state of charge (SOC) where a partial charge/discharge had been terminated previously. 6 This effect has been known to be an essential issue for Ni-metal hydride (Ni-MH) batteries 7,8 and believed to be absent in Li-ion batteries.Several mathematical models have been developed to account for these unique phenomena in the electrochemical behavior of LiFePO 4 , including the shrinking-core model, 5 the resistive-reactant model, 9 and the many-particle model. 4,10 Among all these models, the manyparticle model proposed by Dreyer et al. 4 shows behavior that is consistent with all the...

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Section: Resultsmentioning

confidence: 99%

Comprehensive Study of the Polarization Behavior of LiFePO₄Electrodes Based on a Many-Particle Model

Kondo

Sasaki

Barai

et al. 2018

J. Electrochem. Soc.

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“…rough data preprocessing, the level of mining can be greatly improved and the time spent in analysis can be reduced [23][24][25].…”

Section: Data Preprocessingmentioning

confidence: 99%

Influence Model of Analyzing the Effect of Mental Health Level Based on Big Data Mining System

Wang

2022

Scientific Programming

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In order to explore the effect of big data mining system on analyzing mental health level, this paper proposes to influence model of analyzing the effect of mental health level based on big data mining system. Through continuous testing and analysis, the main symptom affecting students’ mental health is obsessive-compulsive disorder. Therefore, taking obsessive-compulsive disorder as the classification target to view the model, in this application, the factor of compulsion in students’ psychology occupies a relatively high proportion. Anxiety, interpersonal relationship, and paranoia have a great impact on goal attribute obsessive-compulsive disorder. The results showed that if the degree of anxiety = medium, there was a tendency of obsessive-compulsive disorder regardless of the degree of interpersonal relationship. If anxiety level = none, when paranoia level = [mild, moderate], obsessive-compulsive symptoms level = mild, and when paranoia level = none, it is related to interpersonal relationship and hostility. If the degree of paranoia = “severe” or “extremely severe”, the degree of obsessive-compulsive symptoms = none. If the degree of anxiety = light, there is a tendency of obsessive-compulsive disorder regardless of the degree of interpersonal relationship. If the degree of anxiety = severe, the degree of obsessive-compulsive symptoms = moderate. If the degree of depression = medium, the degree of anxiety = medium. If the degree of depression = none, when the degree of terror = medium, the degree of anxiety = light, and when the degree of terror = [none, light, heavy], there is almost no anxiety. If the degree of depression = mild and the degree of obsessive-compulsive symptoms = none, there is no anxiety tendency. If the degree of depression = severe, the degree of anxiety = severe. If the degree of depression = medium, the degree of interpersonal relationship = medium. If the degree of depression = none, when the degree of terror = light and there is psychosis, the degree of interpersonal relationship = light. If the degree of depression = mild and there is obsessive-compulsive disorder, there are problems in interpersonal relationship. The data analysis of mental health problems has been greatly improved, verifying the reliability of the application of data mining systems in mental health evaluation systems.

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“…Batteries in parallel to meet the high capacity requirements and provide the required system voltage in the series. The influence of memory effect on the SOC estimation is conducted by Shi et al (2016) for large format LIBs based on LiFePO4 cathode. In a pack, the battery capacity and internal resistance of the battery parameters, from cell to cell due to manufacturing variation, aging, and different operating conditions, such as temperature gradients within the package.…”

Section:  mentioning

confidence: 99%

Online state of charge estimation for the aerial lithium-ion battery packs based on the improved extended Kalman filter method

Wang

Fernandez

Shang

et al. 2017

Journal of Energy Storage

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Influence of memory effect on the state-of-charge estimation of large-format Li-ion batteries based on LiFePO4 cathode

Cited by 19 publications

References 26 publications

Comprehensive Study of the Polarization Behavior of LiFePO₄Electrodes Based on a Many-Particle Model

Comprehensive Study of the Polarization Behavior of LiFePO₄Electrodes Based on a Many-Particle Model

Influence Model of Analyzing the Effect of Mental Health Level Based on Big Data Mining System

Online state of charge estimation for the aerial lithium-ion battery packs based on the improved extended Kalman filter method

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Influence of memory effect on the state-of-charge estimation of large-format Li-ion batteries based on LiFePO4 cathode

Cited by 19 publications

References 26 publications

Comprehensive Study of the Polarization Behavior of LiFePO4Electrodes Based on a Many-Particle Model

Comprehensive Study of the Polarization Behavior of LiFePO4Electrodes Based on a Many-Particle Model

Influence Model of Analyzing the Effect of Mental Health Level Based on Big Data Mining System

Online state of charge estimation for the aerial lithium-ion battery packs based on the improved extended Kalman filter method

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Product

Resources

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Comprehensive Study of the Polarization Behavior of LiFePO₄Electrodes Based on a Many-Particle Model

Comprehensive Study of the Polarization Behavior of LiFePO₄Electrodes Based on a Many-Particle Model