Statistical analysis for understanding and predicting battery degradations in real-life electric vehicle use

Barré, Anthony; Suard, Frédéric; Gérard, Mathias; Montaru, Maxime; Riu, Delphine

doi:10.1016/j.jpowsour.2013.07.052

Cited by 86 publications

(39 citation statements)

References 53 publications

(56 reference statements)

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“…Honbo et al studied Li deposition on graphite via SEM and revealed dendritic and granular morphologies on pristine and grinded carbon, respectively. 123 Zier et al showed that it is possible to enhance the material contrast of Li deposition on graphite electrodes by reaction with OsO 4 . 119 The study of other anode materials such as Li 4 Ti 5 O 12 by SEM has so far not given precise information on the degradation mechanisms.…”

Section: 121mentioning

confidence: 99%

“…[1][2][3][4] Furthermore, the rise of renewable energy sources like wind and solar power, which are only intermittently available, demands reliable and highly flexible stationary energy storage solutions, which provide high capacities and predictable life-times. 2,5 Aging of Li-ion batteries is a general problem for manufacturers as they have to guarantee long-term reliability of their products.…”

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confidence: 99%

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Review—Post-Mortem Analysis of Aged Lithium-Ion Batteries: Disassembly Methodology and Physico-Chemical Analysis Techniques

Waldmann

Iturrondobeitia

Kasper

et al. 2016

J. Electrochem. Soc.

235

149

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Improvement of life-time is an important issue in the development of Li-ion batteries. Aging mechanisms limiting the life-time can efficiently be characterized by physico-chemical analysis of aged cells with a variety of complementary methods. This study reviews the state-of-the-art literature on Post-Mortem analysis of Li-ion cells, including disassembly methodology as well as physicochemical characterization methods for battery materials. A detailed scheme for Post-Mortem analysis is deduced from literature, including pre-inspection, conditions and safe environment for disassembly of cells, as well as separation and post-processing of components. Special attention is paid to the characterization of aged materials including anodes, cathodes, separators, and electrolyte. More specifically, microscopy, chemical methods sensitive to electrode surfaces or to electrode bulk, and electrolyte analysis are reviewed in detail. The techniques are complemented by electrochemical measurements using reconstruction methods for electrodes built into half and full cells with reference electrode. The changes happening to the materials during aging as well as abilities of the reviewed analysis methods to observe them are critically discussed. Li-ion batteries are currently used in everyday objects such as smart-phones, power tools and tablet computers as well as in the growing fields of light electric vehicles (LEVs), unmanned aerial vehicles (UAVs), battery electric vehicles (BEVs), hybrid electric vehicles (HEVs), and plug-in hybrid electric vehicles (PHEVs). [1][2][3][4] Furthermore, the rise of renewable energy sources like wind and solar power, which are only intermittently available, demands reliable and highly flexible stationary energy storage solutions, which provide high capacities and predictable life-times. 2,5Aging of Li-ion batteries is a general problem for manufacturers as they have to guarantee long-term reliability of their products. For state-of-the-art cells, degradation effects on the material level lead to capacity fade and resistance increase on the cell level. The aging state of a battery is often characterized by the state-of-health (SOH) in % according to 3,16,22,[29][30][31] SO H(t) = discharge capacity (t) discharge capacity (t = 0)[1]where t represents the aging time. In general, one has to differentiate between cycling 7,16,18,21,[23][24][25]32 and calendar aging. 7,19,[21][22][23][24]27 Since commercial Li-ion cells can be subject to calendar aging in the time between manufacturing and delivery, it is good practice to measure the discharge capacity at t = 0 for each cell that undergoes an aging test. Since the discharge capacity depends mainly on temperature, depth-of-discharge (DOD), and discharge current, the SOH is usually monitored by regular check-ups with defined parameter sets, 7,16,21,23,24 which can vary depending on the application. Typically, a temperature of 25• C, 16,22,24 DOD of 100%, 16,21 and discharge rates of 1C 7,16,21,22,24 or lower 23 are used in check-ups. The performance dec...

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Section: 121mentioning

confidence: 99%

mentioning

confidence: 99%

Review—Post-Mortem Analysis of Aged Lithium-Ion Batteries: Disassembly Methodology and Physico-Chemical Analysis Techniques

Waldmann

Iturrondobeitia

Kasper

et al. 2016

J. Electrochem. Soc.

235

149

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“…where SOC i,0 is the initial SOC; s i the nominal mileage; d i,h the trip distance at day h; DSOC i,h the SOC increment at [21], it is introduced to consider the capacity and mileage decreases of PrEV i , while it can be determined by…”

Section: Time Indicators Related Parametersmentioning

confidence: 99%

Procedural simulation method for aggregating charging load model of private electric vehicle cluster

Ban

2015

J. Mod. Power Syst. Clean Energy

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The usage of each private electric vehicle (PrEV) is a repeating behavior process composed by driving, parking, discharging and charging, in which PrEV shows obvious procedural characteristics. To analyze the procedural characteristics, this paper proposes a procedural simulation method. The method aggregates the behavior process regularity of the PrEV cluster to model the cluster's charging load. Firstly, the basic behavior process of each PrEV is constructed by referring the statistical datasets of the traditional private non-electric vehicles. Secondly, all the basic processes are set as a simulation starting point, and they are dynamically reconstructed by several constraints. The simulation continues until the steady state of charge (SOC) distribution and behavior regularity of the PrEV cluster are obtained. Lastly, based on the obtained SOC and behavior regularity information, the PrEV cluster's behavior processes are simulated again to make the aggregating charging load model available. Examples for several scenarios show that the proposed method can improve the reliability of modeling by grasping the PrEV cluster's procedural characteristics.

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“…A study by Barre et al [9] investigated battery data, which was collected from a passenger EV following a predetermined driving cycle, to determine the causality between vehicle use and battery ageing. This study identified that capacity fade is primarily related to temperature and time, and impedance rise is primarily related to the current profile and the maximum current delivered by the battery.…”

Section: Introductionmentioning

confidence: 99%

Duty-cycle characterisation of large-format automotive lithium ion pouch cells for high performance vehicle applications

Kellner

Worwood

Barai

et al. 2018

Journal of Energy Storage

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A B S T R A C TThe long-term behaviour of lithium ion batteries in high-performance (HP) electric vehicle (EV) applications is not well understood due to a lack of suitable testing cycles and experimental data. As such a generic HP duty cycle (HP-C), representing driving on a race track is validated, and six NMC graphite cells are characterised with respect to cycle-life. To enable a comparison between the HP-EV environment and conventional road driving, two test groups of cells are subject to an experimental evaluation over 200 duty cycles that includes a representative HP-C and a standard duty cycle from the IEC 62660-1 standard (IECC). After testing, both test groups display increased energy capacity, increased pure Ohmic resistance, lower charge transfer resistance an extended OCV operating window. The changes are more pronounced for cells subject to the HP-C. Based on capacity tests, Electrochemical Impedance Spectroscopy (EIS), pseudo-OCV tests, and Pulse Multisine Characterisation, it is concluded that the changes in cell characteristics are most likely caused by cracking of the electrode material caused by high electrical current pulses. With continued cycling, cells cycled with the HP-C are expected to show degradation at an increased rate due to raised temperatures, and more pronounced electrode cracking.

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Statistical analysis for understanding and predicting battery degradations in real-life electric vehicle use

Cited by 86 publications

References 53 publications

Review—Post-Mortem Analysis of Aged Lithium-Ion Batteries: Disassembly Methodology and Physico-Chemical Analysis Techniques

Review—Post-Mortem Analysis of Aged Lithium-Ion Batteries: Disassembly Methodology and Physico-Chemical Analysis Techniques

Procedural simulation method for aggregating charging load model of private electric vehicle cluster

Duty-cycle characterisation of large-format automotive lithium ion pouch cells for high performance vehicle applications

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