Investigation of the Effect of Temperature on Lithium‐Sulfur Cell Cycle Life Performance Using System Identification and X‐Ray Tomography

Shateri, Neda; Auger, Daniel J.; Fotouhi, Abbas; Brighton, James; Du, Weidong; Owen, Rhodri E.; Brett, Dan J. L.; Shearing, Paul R.

doi:10.1002/batt.202200035

Cited by 7 publications

(7 citation statements)

References 63 publications

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“…In this study, the Thevenin model is only used for offline battery parameter identification to generate a reference benchmark. The mathematical presentation of this model is as follows [35]: Polarisation is a special effect observed in batteries, especially during discharge, which can affect the accuracy of the battery's electrical parameters [31]. Adding one RC model to the Rint model can improve it by considering the effect of polarisation and this model is called the Thevenin model [29], which is illustrated in Figure 9.…”

Section: Battery Modelling For Temperature Prediction 321 Battery Ele...mentioning

confidence: 99%

Battery Temperature Prediction Using an Adaptive Neuro-Fuzzy Inference System

Zhang,

Fotouhi,

Auger

et al. 2024

Batteries

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Maintaining batteries within a specific temperature range is vital for safety and efficiency, as extreme temperatures can degrade a battery’s performance and lifespan. In addition, battery temperature is the key parameter in battery safety regulations. Battery thermal management systems (BTMSs) are pivotal in regulating battery temperature. While current BTMSs offer real-time temperature monitoring, their lack of predictive capability poses a limitation. This study introduces a novel hybrid system that combines a machine learning-based battery temperature prediction model with an online battery parameter identification unit. The identification unit continuously updates the battery’s electrical parameters in real time, enhancing the prediction model’s accuracy. The prediction model employs an Adaptive Neuro-Fuzzy Inference System (ANFIS) and considers various input parameters, such as ambient temperature, the battery’s current temperature, internal resistance, and open-circuit voltage. The model accurately predicts the battery’s future temperature in a finite time horizon by dynamically adjusting thermal and electrical parameters based on real-time data. Experimental tests are conducted on Li-ion (NCA and LFP) cylindrical cells across a range of ambient temperatures to validate the system’s accuracy under varying conditions, including state of charge and a dynamic load current. The proposed models prioritise simplicity to ensure real-time industrial applicability.

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Section: Battery Modelling For Temperature Prediction 321 Battery Ele...mentioning

confidence: 99%

Battery Temperature Prediction Using an Adaptive Neuro-Fuzzy Inference System

Zhang,

Fotouhi,

Auger

et al. 2024

Batteries

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“…However, SOH cannot be simply determined from a single measurement and complementary understanding of the integrity of the internal architecture of EOL batteries must also be considered. The degradation of EV cells usually involves severe architectural or structural deformation, in particular for pouch cells [66], wherein gas generation may distort the electrode structures and thus lead to internal short-circuits. As the degradation coincides with gas evolution [67] the ageing-induced gas via electrolyte decomposition needs to be considered as a crucial metric of SOH.…”

Section: Current and Future Challengesmentioning

confidence: 99%

Roadmap for a sustainable circular economy in lithium-ion and future battery technologies

et al. 2023

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The market dynamics, and their impact on a future circular economy for lithium-ion batteries (LIB), are presented in this roadmap, with safety as an integral consideration throughout the life cycle. At the point of end-of-life, there is a range of potential options – remanufacturing, reuse and recycling. Diagnostics play a significant role in evaluating the state of health and condition of batteries, and improvements to diagnostic techniques are evaluated. At present, manual disassembly dominates end-of-life disposal, however, given the volumes of future batteries that are to be anticipated, automated approaches to the dismantling of end-of-life battery packs will be key. The first stage in recycling after the removal of the cells is the initial cell-breaking or opening step. Approaches to this are reviewed, contrasting shredding and cell disassembly as two alternative approaches. Design for recycling is one approach that could assist in easier disassembly of cells, and new approaches to cell design that could enable the circular economy of LIBs are reviewed. After disassembly, subsequent separation of the black mass is performed before further concentration of components. There are a plethora of alternative approaches for recovering materials; this roadmap sets out the future directions for a range of approaches including pyrometallurgy, hydrometallurgy, short-loop, direct, and the biological recovery of LIB materials. Furthermore, anode, lithium, electrolyte, binder and plastics recovery are considered in the range of approaches in order to maximise the proportion of materials recovered, minimise waste and point the way towards zero-waste recycling. The life-cycle implications of a circular economy are discussed considering the overall system of LIB recycling, and also directly investigating the different recycling methods. The legal and regulatory perspectives are also considered. Finally, with a view to the future, approaches for next-generation battery chemistries and recycling are evaluated, identifying gaps for research.

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“…[282] Other promising systems such as lithium-sulphur pouch cells have recently been studied by laboratory X-ray tomography. [283] Wang analysed sodium-ion batteries and the influence of tin particles by in situ hard X-ray nanotomography. [284] Vanpeene et al characterized the degradation of silicon-based anodes for lithium-ion batteries.…”

Section: Batteriesmentioning

confidence: 99%

X‐ray Tomography and Tomoscopy on Metals: A Review

et al. 2023

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X‐ray tomography is a versatile tool in materials research and engineering since it allows for a non‐destructive and three‐dimensional mapping of the constituents of a heterogeneous material as long as they differ in their interactions with X‐rays. Recent developments of the technique have brought down the time needed for the acquisition of a single tomogram by many orders of magnitude compared to what was needed 25 years ago. Nowadays, up to 1000 full tomograms can be recorded in a second, which enables real‐time studies of changes in samples caused by reactions or by applied processing operations. The term tomoscopy has been coined for such sequences of 3D images. We review the application of X‐ray tomography and tomoscopy on metals and describe each step required and the associated challenges. A selection of representative investigations is presented with a focus on time‐resolved phenomena in metals and alloys ranging from mechanical deformation, solidification to metals processing processes such as welding and additive manufacturing. Finally likely future developments are discussed.

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Investigation of the Effect of Temperature on Lithium‐Sulfur Cell Cycle Life Performance Using System Identification and X‐Ray Tomography

Cited by 7 publications

References 63 publications

Battery Temperature Prediction Using an Adaptive Neuro-Fuzzy Inference System

Battery Temperature Prediction Using an Adaptive Neuro-Fuzzy Inference System

Roadmap for a sustainable circular economy in lithium-ion and future battery technologies

X‐ray Tomography and Tomoscopy on Metals: A Review

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