Combined cycling and calendar capacity fade modeling of a Nickel-Manganese-Cobalt Oxide Cell with real-life profile validation

Hoog, Joris De; Timmermans, Jean-Marc; Stroe, Daniel‐Ioan; Świerczyński, Maciej; Jaguemont, Joris; Goutam, Shovon; Omar, Noshin; Bossche, Peter Van den

doi:10.1016/j.apenergy.2017.05.018

Cited by 189 publications

(132 citation statements)

References 81 publications

Supporting

Mentioning

121

Contrasting

Order By: Relevance

“…This downward shift is known [31] to be linked to improved transfer kinetics at the electrode electrolyte interfaces in the porous electrode. As discussed within [42,43,52], one reason for this may be an increase in active surface area for Li-ion intercalation which could have been caused by electrode cracking. This shift appears to be of a larger magnitude for the HP-C test sample.…”

Section: Duty-cycle Studymentioning

confidence: 99%

“…Increases in cell capacity at the early stages of cycling for cells of similar chemistry and format have been reported within [41] and [42]. Jalkanen [41] et al tested 3 Dow Kokam® 1 40 Ah pouch at room temperature, 45°C and 65°C for continuous 1C charge and discharge cycling.…”

Section: Duty-cycle Studymentioning

confidence: 99%

“…De Hoog et al [42] tested EIG® 20 Ah NMC pouch cells to formulate a model which separates calendar and cycle ageing. They observed an initial increase in cell capacity for calendar ageing and combined calendar and cycle ageing tests, an effect which was more pronounced for cells stored at low states of charge, and subject to lower DOD.…”

Section: Duty-cycle Studymentioning

confidence: 99%

See 2 more Smart Citations

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

View full text Add to dashboard Cite

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.

show abstract

Section: Duty-cycle Studymentioning

confidence: 99%

Section: Duty-cycle Studymentioning

confidence: 99%

Section: Duty-cycle Studymentioning

confidence: 99%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

show abstract

“…Therefore, the calendar aging data was used to subtract the calendaring influence from the cycling aging test results before the cycling aging model was developed. This method was also used in our previous work on capacity fade modeling [28], and a similar approach was used in [48].…”

Section: Aging Modelingmentioning

confidence: 99%

“…This presents serious difficulties for operating the battery under high current rates. Indeed, aging of LiBs, defined as the irreversible loss of their energy storage capability and power [28], is one such challenge. It is thus important to be aware of the battery degradation behavior with respect to the functionality of the LiBs.…”

Section: Introductionmentioning

confidence: 99%

Combining an Electrothermal and Impedance Aging Model to Investigate Thermal Degradation Caused by Fast Charging

et al. 2018

Self Cite

View full text Add to dashboard Cite

Fast charging is an exciting topic in the field of electric and hybrid electric vehicles (EVs/HEVs). In order to achieve faster charging times, fast-charging applications involve high-current profiles which can lead to high cell temperature increase, and in some cases thermal runaways. There has been some research on the impact caused by fast-charging profiles. This research is mostly focused on the electrical, thermal and aging aspects of the cell individually, but these factors are never treated together. In this paper, the thermal progression of the lithium-ion battery under specific fast-charging profiles is investigated and modeled. The cell is a Lithium Nickel Manganese Cobalt Oxide/graphite-based cell (NMC) rated at 20 Ah, and thermal images during fast-charging have been taken at four degradation states: 100%, 90%, 85%, and 80% State-of-Health (SoH). A semi-empirical resistance aging model is developed using gathered data from extensive cycling and calendar aging tests, which is coupled to an electrothermal model. This novel combined model achieves good agreement with the measurements, with simulation results always within 2°C of the measured values. This study presents a modeling methodology that is usable to predict the potential temperature distribution for lithium-ion batteries (LiBs) during fast-charging profiles at different aging states, which would be of benefit for Battery Management Systems (BMS) in future thermal strategies.

show abstract

Machine Learning‐Based Lifetime Prediction of Lithium‐Ion Cells

et al. 2022

View full text Add to dashboard Cite

Precise lifetime predictions for lithium‐ion cells are crucial for efficient battery development and thus enable profitable electric vehicles and a sustainable transformation towards zero‐emission mobility. However, limitations remain due to the complex degradation of lithium‐ion cells, strongly influenced by cell design as well as operating and storage conditions. To overcome them, a machine learning framework is developed based on symbolic regression via genetic programming. This evolutionary algorithm is capable of inferring physically interpretable models from cell aging data without requiring domain knowledge. This novel approach is compared against established approaches in case studies, which represent common tasks of lifetime prediction based on cycle and calendar aging data of 104 automotive lithium‐ion pouch‐cells. On average, predictive accuracy for extrapolations over storage time and energy throughput is increased by 38% and 13%, respectively. For predictions over other stress factors, error reductions of up to 77% are achieved. Furthermore, the evolutionary generated aging models meet requirements regarding applicability, generalizability, and interpretability. This highlights the potential of evolutionary algorithms to enhance cell aging predictions as well as insights.

show abstract

Combined cycling and calendar capacity fade modeling of a Nickel-Manganese-Cobalt Oxide Cell with real-life profile validation

Cited by 189 publications

References 81 publications

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

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

Combining an Electrothermal and Impedance Aging Model to Investigate Thermal Degradation Caused by Fast Charging

Machine Learning‐Based Lifetime Prediction of Lithium‐Ion Cells

Contact Info

Product

Resources

About