- Showing results for:
- Reset all filters
Conference paperMalone L, Cardin M-A, Cilliers JJ, et al., 2023,
Exploring Novel Architectures in Lunar In-Situ Resource Utilisation, Brisbane, Australia, 26th World Mining Congress
Conference paperGe P, Caputo C, Teng F, et al., 2022,
A Wireless-Assisted Hierarchical Framework to Accommodate Mobile Energy Resources, Singapore, IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)
Journal articleAi W, Wu B, Martínez-Pañeda E, 2022,
A coupled phase field formulation for modelling fatigue cracking in lithium-ion battery electrode particles, Journal of Power Sources, Vol: 544, ISSN: 0378-7753
Electrode particle cracking is one of the main phenomena driving battery capacity degradation. Recent phase field fracture studies have investigated particle cracking behaviour. However, only the beginning of life has been considered and effects such as damage accumulation have been neglected. Here, a multi-physics phase field fatigue model has been developed to study crack propagation in battery electrode particles undergoing hundreds of cycles. In addition, we couple our electrochemo-mechanical formulation with X-ray CT imaging to simulate fatigue cracking of realistic particle microstructures. Using this modelling framework, non-linear crack propagation behaviour is predicted, leading to the observation of an exponential increase in cracked area with cycle number. Three stages of crack growth (slow, accelerating and unstable) are observed, with phenomena such as crack initialisation at concave regions and crack coalescence having a significant contribution to the resulting fatigue crack growth rates. The critical values of C-rate, particle size and initial crack length are determined, and found to be lower than those reported in the literature using static fracture models. Therefore, this work demonstrates the importance of considering fatigue damage in battery degradation models and provides insights on the control of fatigue crack propagation to alleviate battery capacity degradation.
Conference paperMalone L, Cardin M-A, Cilliers J, et al., 2022,
Development of a Comprehensive Lunar Mining Simulator to Study Design and Decision-Making under Uncertainty, Paris, France, International Astronautical Congress
BookGeronazzo M, Serafin S, 2022,
Sonic interactions in virtual environments, Publisher: Springer, ISBN: 9783031040207
This book tackles the design of 3D spatial interactions in an audio-centered and audio-first perspective, providing the fundamental notions related to the creation and evaluation of immersive sonic experiences. The key elements that enhance the sensation of place in a virtual environment (VE) are:Immersive audio: the computational aspects of the acoustical-space properties of Virutal Reality (VR) technologiesSonic interaction: the human-computer interplay through auditory feedback in VEVR systems: naturally support multimodal integration, impacting different application domainsSonic Interactions in Virtual Environments will feature state-of-the-art research on real-time auralization, sonic interaction design in VR, quality of the experience in multimodal scenarios, and applications. Contributors and editors include interdisciplinary experts from the fields of computer science, engineering, acoustics, psychology, design, humanities, and beyond.Their mission is to shape an emerging new field of study at the intersection of sonic interaction design and immersive media, embracing an archipelago of existing research spread in different audio communities and to increase among the VR communities, researchers, and practitioners, the awareness of the importance of sonic elements when designing immersive environments.
Conference paperNeo VW, Weiss S, McKnight S, et al., 2022,
Polynomial eigenvalue decomposition-based target speaker voice activity detection in the presence of competing talkers, 17th International Workshop on Acoustic Signal Enhancement
Voice activity detection (VAD) algorithms are essential for many speech processing applications, such as speaker diarization, automatic speech recognition, speech enhancement, and speech coding. With a good VAD algorithm, non-speech segments can be excluded to improve the performance and computation of these applications. In this paper, we propose a polynomial eigenvalue decomposition-based target-speaker VAD algorithm to detect unseen target speakers in the presence of competing talkers. The proposed approach uses frame-based processing to compute the syndrome energy, used for testing the presence or absence of a target speaker. The proposed approach is consistently among the best in F1 and balanced accuracy scores over the investigated range of signal to interference ratio (SIR) from -10 dB to 20 dB.
Journal articleCaunhye AM, Cardin M-A, Rahmat M, 2022,
Flexibility and real options analysis in power system generation expansion planning under uncertainty, IISE Transactions, Vol: 54, Pages: 832-844, ISSN: 2472-5854
Over many years, there has been a drive in the electricity industry towards better integration of environmentally friendly and renewable generation resources for power systems. Such resources show highly variable availability, impacting the design and performance of power systems. In this paper, we propose using a stochastic programming approach to optimize generation expansion planning (GEP), with explicit consideration of generator output capacity uncertainty. Flexibility implementation - via real options exercised in response to uncertainty realizations - is considered as an important design approach to the GEP problem. It more effectively captures upside opportunities, while reducing exposure to downside risks. A decision-rule based approach to real options modeling is used, combining conditional-go and finite adaptability principles. The solutions provide decision makers with easy-to-use guidelines with threshold values from which to exercise the options in operations. To demonstrate application of the proposed methodologies and decision rules, a case study situated in the Midwest United States is used. The case study demonstrates how to quantify the value of flexibility, and showcases the usefulness of the proposed approach.
Journal articleBrooks R, Wang H, Ding Z, et al., 2022,
Continuous fibre-reinforced thermoplastics (FRTPs) are replacing metals in certain applications in the aerospace industry due to their superior properties e.g., high strength-to-weight ratio and good fatigue resistance. Adopting these lightweight materials in vehicles is a solution for improving vehicle efficiency across the transport industry. Among various manufacturing techniques for FRTP parts, stamp forming is one of the most advantageous when small structures and mass production are targeted. However, a significant barrier for this technique is the quality control of manufacturing. The current paper reviews the development of stamp forming technology, benefits of using such technology and the typical quality issues in stamp forming of FRTP parts. First, advantages of stamp forming, compared to other thermoforming techniques, are discussed, followed by a review of the historical development of the process. Second, deformation mechanisms of FRTPs during stamp forming are examined, with particular focuses on the frictional behaviour and testing thereof. Third, the main defects associated with stamp forming are considered, alongside suggestions towards reducing their presence. Finally, an extensive survey of the effect of process parameters on the mechanical properties of formed parts is included, with generally expected trends highlighted and methodologies for finding optimum conditions presented. Based on the thorough review of state-of-the-art stamp forming, future trends and research gaps to be tackled for widening the applicability of FRTP stamp forming are suggested.
Journal articleRuan H, Chen J, Ai W, et al., 2022,
Diagnosing lithium-ion battery degradation is challenging due to the complex, nonlinear, and path-dependent nature of the problem. Here, we develop a generalised and rapid degradation diagnostic method with a deep learning-convolutional neural network that quantifies degradation modes of batteries aged under various conditions in 0.012 s without feature engineering. Rather than performing extensive aging experiments, synthetic aging datasets for network training are generated. This dramatically lowers training cost/time, with these datasets covering almost all the aging paths, enabling a generalised degradation diagnostic framework. We show that the five thermodynamic degradation modes are correlated, and systematically elucidate their correlations. We thus propose a non-invasive comprehensive evaluation method and find the degradation diagnostic errors to be less than 1.22% for three leading commercial battery chemistries. The comparison with the traditional diagnostic methods confirms the high accuracy and fast nature of the proposed approach. Quantification of degradation modes with the partial discharge/charge data using the proposed diagnostic framework validates the real-world feasibility of this approach. This work, therefore, enables the promise of online identification of battery degradation and efficient analysis of large-data sets, unlocking potential for long lifetime energy storage systems.
Journal articleAnderson J, Cardin M-A, Grogan P, 2022,
Design and analysis of flexible multi-layer staged deployment for satellite mega-constellations under demand uncertainty, Acta Astronautica, Vol: 198, ISSN: 0094-5765
Internet satellite constellations are expected to play an important role in accommodating the rising global demand for internet access. Such rise in demand, however, is highly uncertain. Staged deployment is an approach that provides flexibility to tackle demand uncertainty by enabling the real option to reconfigure a constellation if demand changes. Advancements in satellite technology have led to the emergence of multi-layered constellations. This opens the opportunity to enhance staged deployment by enabling an additional real option: adding a new layer to a constellation. This real option has no associated reconfiguration costs, and therefore has the potential to reduce the cost of staged systems deployment. This paper proposes a framework to design multi-layer staged deployment systems and analyse their effectiveness in modern mega-constellations under global demand uncertainty. The framework is applied to four case studies based on market projections. Results show that multi-layer staged deployment decreases the expected life-cycle cost (ELCC) by 42.8% compared to optimal traditional single-layer deployment. Multi-layer staged deployment is more cost effective than single-layer staged deployment in all practical cases, which decreases ELCC by 22.9% compared to traditional deployment. Several cost altering mechanisms in staged deployment are identified. The results and analysis provide improved economic performance and better resource utilization, thus contributing in the long term to improved sustainability and market resilience. An accompanying decision support system provides system engineers with valuable insights on how to reduce deployment costs using the proposed multi-layered staged strategy.
Journal articleMao A, Giraudet CSE, Liu K, et al., 2022,
The annual global production of chickens exceeds 25 billion birds, which are often housed in very large groups, numbering thousands. Distress calling triggered by various sources of stress has been suggested as an 'iceberg indicator' of chicken welfare. However, to date, the identification of distress calls largely relies on manual annotation, which is very labour-intensive and time-consuming. Thus, a novel convolutional neural network-based model, light-VGG11, was developed to automatically identify chicken distress calls using recordings (3363 distress calls and 1973 natural barn sounds) collected on an intensive farm. The light-VGG11 was modified from VGG11 with significantly fewer parameters (9.3 million versus 128 million) and 55.88% faster detection speed while displaying comparable performance, i.e. precision (94.58%), recall (94.89%), F1-score (94.73%) and accuracy (95.07%), therefore more useful for model deployment in practice. To additionally improve light-VGG11's performance, we investigated the impacts of different data augmentation techniques (i.e. time masking, frequency masking, mixed spectrograms of the same class and Gaussian noise) and found that they could improve distress calls detection by up to 1.52%. Our distress call detection demonstration on continuous audio recordings, shows the potential for developing technologies to monitor the output of this call type in large, commercial chicken flocks.
Journal articleDeterding S, Malmdorf Andersen M, Kiverstein J, et al., 2022,
Mastering uncertainty: a predictive processing account of enjoying uncertain success in video game play, Frontiers in Psychology, ISSN: 1664-1078
Why do we seek out and enjoy uncertain success in playing games? Game designers and researchers suggest that games whose challenges match player skills afford engaging experiences of achievement, competence, or effectance – of doing well. Yet, current models struggle to explain why such balanced challenges best afford these experiences and do not straightforwardly account for the appeal of high- and low-challenge game genres like Idle and Soulslike games. In this article, we show that Predictive Processing (PP) provides a coherent formal cognitive framework which can explain the fun in tackling game challenges with uncertain success as the dynamic process of reducing uncertainty surprisingly efficiently. In gameplay as elsewhere, people enjoy doing better than expected, which can track learning progress. In different forms, balanced, Idle, and Soulslike games alike afford regular accelerations of uncertainty reduction. We argue that this model also aligns with a popular practitioner model, Raph Koster’s Theory of Fun for Game Design, and can unify currently differentially modelled gameplay motives around competence and curiosity.
Journal articleJi S, Ghajari M, Mao H, et al., 2022,
Head acceleration measurement sensors are now widely deployed in the field to monitor head kinematic exposure in contact sports. The wealth of impact kinematics data provides valuable, yet challenging, opportunities to study the biomechanical basis of mild traumatic brain injury (mTBI) and subconcussive kinematic exposure. Head impact kinematics are translated into brain mechanical responses through physics-based computational simulations using validated brain models to study the mechanisms of injury. First, this article reviews representative legacy and contemporary brain biomechanical models primarily used for blunt impact simulation. Then, it summarizes perspectives regarding the development and validation of these models, and discusses how simulation results can be interpreted to facilitate injury risk assessment and head acceleration exposure monitoring in the context of contact sports. Recommendations and consensus statements are presented on the use of validated brain models in conjunction with kinematic sensor data to understand the biomechanics of mTBI and subconcussion. Mainly, there is general consensus that validated brain models have strong potential to improve injury prediction and interpretation of subconcussive kinematic exposure over global head kinematics alone. Nevertheless, a major roadblock to this capability is the lack of sufficient data encompassing different sports, sex, age and other factors. The authors recommend further integration of sensor data and simulations with modern data science techniques to generate large datasets of exposures and predicted brain responses along with associated clinical findings. These efforts are anticipated to help better understand the biomechanical basis of mTBI and improve the effectiveness in monitoring kinematic exposure in contact sports for risk and injury mitigation purposes.
Journal articleLalitharatne TD, Costi L, Hasheem R, et al., 2022,
Realtime visual feedback from consequences of actions is useful for future safety-critical human–robot interaction applications such as remote physical examination of patients. Given multiple formats to present visual feedback, using face as feedback for mediating human–robot interaction in remote examination remains understudied. Here we describe a face mediated human–robot interaction approach for remote palpation. It builds upon a robodoctor–robopatient platform where user can palpate on the robopatient to remotely control the robodoctor to diagnose a patient. A tactile sensor array mounted on the end effector of the robodoctor measures the haptic response of the patient under diagnosis and transfers it to the robopatient to render pain facial expressions in response to palpation forces. We compare this approach against a direct presentation of tactile sensor data in a visual tactile map. As feedback, the former has the advantage of recruiting advanced human capabilities to decode expressions on a human face whereas the later has the advantage of being able to present details such as intensity and spatial information of palpation. In a user study, we compare these two approaches in a teleoperated palpation task to find the hard nodule embedded in the remote abdominal phantom. We show that the face mediated human–robot interaction approach leads to statistically significant improvements in localizing the hard nodule without compromising the nodule position estimation time. We highlight the inherent power of facial expressions as communicative signals to enhance the utility and effectiveness of human–robot interaction in remote medical examinations.
Journal articleCooper SJ, Roberts SA, Liu Z, et al., 2022,
Methods—Kintsugi imaging of battery electrodes: distinguishing pores from the carbon binder domain using Pt deposition, Journal of The Electrochemical Society, Vol: 169, ISSN: 0013-4651
The mesostructure of porous electrodes used in lithium-ion batteries strongly influences cell performance. Accurate imaging of the distribution of phases in these electrodes would allow this relationship to be better understood through simulation. However, imaging the nanoscale features in these components is challenging. While scanning electron microscopy is able to achieve the required resolution, it has well established difficulties imaging porous media. This is because the flat imaging planes prepared using focused ion beam milling will intersect with the pores, which makes the images hard to interpret as the inside walls of the pores are observed. It is common to infiltrate porous media with resin prior to imaging to help resolve this issue, but both the nanoscale porosity and the chemical similarity of the resins to the battery materials undermine the utility of this approach for most electrodes. In this study, a technique is demonstrated which uses in situ infiltration of platinum to fill the pores and thus enhance their contrast during imaging. Reminiscent of the Japanese art of repairing cracked ceramics with precious metals, this technique is referred to as the kintsugi method. The images resulting from applying this technique to a conventional porous cathode are presented and then segmented using a multi-channel convolutional method. We show that while some cracks in active material particles were empty, others appear to be filled (perhaps with the carbon binder phase), which will have implications for the rate performance of the cell. Energy dispersive X-ray spectroscopy was used to validate the distribution of phases resulting from image analysis, which also suggested a graded distribution of the binder relative to the carbon additive. The equipment required to use the kintsugi method is commonly available in major research facilities and so we hope that this method will be rapidly adopted to improve the imaging of electrode materials and porous media i
Journal articleComunita M, Gerino A, Picinali L, 2022,
PlugSonic: a web- and mobile-based platform for dynamic and navigable binaural audio, Eurasip Journal on Audio, Speech, and Music Processing, ISSN: 1687-4714
PlugSonic is a series of web- and mobilebased applications designed to: edit samples and applyaudio effects (PlugSonic Sample), create and experience dynamic and navigable soundscapes and sonic narratives (PlugSonic Soundscape). The audio processingwithin PlugSonic is based on the Web Audio API whilethe binaural rendering uses the 3D Tune-In Toolkit.Exploration of soundscapes in a physical space is madepossible by adopting Apple’s ARKit. The present paperdescribes the implementation details, the signal processing chain and the necessary steps to curate and experience a soundscape. We also include some metricsand performance details. The main goal of PlugSonic isto give users a complete set of tools, without the needfor specific devices, external software and/or hardware,specialised knowledge or custom development; with theidea that spatial audio has the potential to become areadily accessible and easy to understand technology,for anyone to adopt, whether for creative or researchpurposes.
Journal articleSiripornpitak P, Engel I, Cooper S, et al., 2022,
Spatial up-sampling of HRTF sets using generative adversarial networks: a pilot study, Frontiers in Signal Processing
Headphone-based spatial audio simulations rely on Head Related Transfer Functions (HRTFs) in order to reconstruct the sound field at the entrance of the listener’s ears. A HRTF is strongly dependent on the listener’s specific anatomical structures, and it has been shown that virtual sounds recreated with someone else’s HRTF result in worse localisation accuracy, as well as altering other subjective measures such as externalisation and realism. Acoustic measurements of the filtering effects generated by ears, head and torso has proven to be one of the most reliable ways to obtain a personalised HRTF. However this requires a dedicated and expensive setup, and is time-intensive. In order to simplify the measurement setup, thereby improving the scalability of the process, we are exploring strategies to reduce the number of acoustic measurements without degrading the spatial resolution of the HRTF. Traditionally, spatial up sampling of HRTF sets is achieved through barycentric interpolation or by employing the spherical harmonics framework. However, such methods often perform poorly when the provided HRTF data is spatially very sparse. This work investigates the use of generative adversarial networks (GANs) to tackle the up-sampling problem, offering an initial insight about the suitability of this technique. Numerical evaluations based on spectral magnitude error and perceptual model outputs are presented on single spatial dimensions, therefore considering sources positioned only in one of the three main planes: horizontal, median, and frontal. Results suggest that traditional HRTF interpolation methods perform better than the proposed GAN-based one when the distance between measurements is smaller than 90°, but for the sparsest conditions (i.e. one measurement every 120° to 180°), the proposed approach outperforms the others.
Journal articleYu X, Nguyen T, Wu T, et al., 2022,
Non-lethal blasts can generate cavitation in cerebrospinal fluid while severe helmeted impacts cannot: a novel mechanism for blast brain injury, Frontiers in Bioengineering and Biotechnology, Vol: 10, ISSN: 2296-4185
Cerebrospinal fluid (CSF) cavitation is a likely physical mechanism for producing traumatic brain injury (TBI) under mechanical loading. In this study, we investigated CSF cavitation under blasts and helmeted impacts which represented loadings in battlefield and road traffic/sports collisions. We first predicted the human head response under the blasts and impacts using computational modelling and found that the blasts can produce much lower negative pressure at the contrecoup CSF region than the impacts. Further analysis showed that the pressure waves transmitting through the skull and soft tissue are responsible for producing the negative pressure at the contrecoup region. Based on this mechanism, we hypothesised that blast, and not impact, can produce CSF cavitation. To test this hypothesis, we developed a one-dimensional simplified surrogate model of the head and exposed it to both blasts and impacts. The test results confirmed the hypothesis and computational modelling of the tests validated the proposed mechanism. These findings have important implications for prevention and diagnosis of blast TBI.
Journal articleTomaszewska A, Doel R, Parkes M, et al., 2022,
Investigating Li Plating Distribution Caused By a Thermal Gradient through Modelling, Differential Voltage, and Post-Mortem Analysis, ECS Meeting Abstracts, Vol: MA2022-01, Pages: 186-186
<jats:p> Relatively slow charging speeds are often quoted as a key barrier to customer acceptance of EVs. Currently, the charging rates are limited primarily by the risk of lithium plating. While traditionally lithium plating has been associated with low temperature charging, recent reports point to the fact that thermal heterogeneity can significantly affect the plating behaviour, sometimes making it more likely or accelerated in the warmer regions in a cell . In EVs, through-plane thermal gradients often develop across individual pouch cells due to the widespread use of surface cooling, particularly during fast charging, when the heat generation rates are also increased. This work investigates the effects of such through-plane thermal gradients on the lithium plating behaviour using a multilayer 2D electrochemical-thermal model and high-rate cycling experiments. The results show that the thermal gradient can result in preferential plating in either the colder or warmer cell regions, depending on the average cell temperature and the activation energies of solid diffusion and lithium plating. While the diffusion rates are slower in the colder cell layers, warmer ones attract higher currents and either of these effects may dominate the plating behaviour. The experimental validation consists of differential voltage analysis, post-mortem visual examination and measurement of remaining capacity in coin cells harvested from Li-ion cells fast charged under uniform temperatures and under thermal gradients. The limitations of DVA as a technique to quantify lithium plating are highlighted. These stem from the fact that the quantification technique requires assuming that only lithium stripping and no de-intercalation takes place up to the differential voltage minimum. In reality, the current is divided between both reactions, and both the temperature and concentration of the metallic lithium may affect the rate of stripping, shifting the location of the minimum
Conference paperCaputo C, Cardin M-A, Korre A, et al., 2022,
Energy System Evolution Strategies for Mobile Micro-grids using Deep Reinforcement Learning Flexibility Analysis, Espoo, Finland, 32nd European Conference on Operational Research (EURO 2022)
Journal articleJedwab RM, Hutchinson AM, Manias E, et al., 2022,
Journal articleWang AA, OKane SEJ, Brosa Planella F, et al., 2022,
<jats:title>Abstract</jats:title> <jats:p>The Doyle–Fuller–Newman (DFN) framework is the most popular physics-based continuum-level description of the chemical and dynamical internal processes within operating lithium-ion-battery cells. With sufficient flexibility to model a wide range of battery designs and chemistries, the framework provides an effective balance between detail, needed to capture key microscopic mechanisms, and simplicity, needed to solve the governing equations at a relatively modest computational expense. Nevertheless, implementation requires values of numerous model parameters, whose ranges of applicability, estimation, and validation pose challenges. This article provides a critical review of the methods to measure or infer parameters for use within the isothermal DFN framework, discusses their advantages or disadvantages, and clarifies limitations attached to their practical application. Accompanying this discussion we provide a searchable database, available at <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="www.liiondb.com" xlink:type="simple">www.liiondb.com</jats:ext-link>, which aggregates many parameters and state functions for the standard DFN model that have been reported in the literature.</jats:p>
Journal articleNguyen QT, Mougenot C, 2022,
A systematic review of empirical studies on multidisciplinary design collaboration: findings, methods, and challenges, Design Studies, Vol: 81, ISSN: 0142-694X
While multidisciplinary collaboration is increasingly considered as a prerequisite for innovation in design, it is unclear what has been studied and what to investigate next. To addressthis, we conducted a systematic literature review on multidisciplinary design collaboration,focussing on what has been found, and how these studies have been implemented. Followinga PRISMA approach, 17 papers were selected for a critical review. A co-occurrence analysisfound that the selected literature covered five themes centred on communication, all highlighting the importance of shared understanding in multidisciplinary design collaboration.Further analysis revealed biases and differences between the methodological approach followed in the studies. For future research, we suggest investigating two under-explored areasof design collaboration: distributed work and digital/service-oriented design activities.
Journal articleHong F, Tendera L, Myant C, et al., 2022,
Vacuum-forming is a common manufacturing technique for constructing thin plastic shell products by pressing heated plastic sheets onto a mold using atmospheric pressure. Vacuum-forming is ubiquitous in packaging and casing products in the industry, spanning fast moving consumer goods to connected devices. Integrating advanced functionality, which may include sensing, computation and communication, within thin structures is desirable for various next-generation interactive devices. Hybrid additive manufacturing techniques like thermoforming are becoming popular for prototyping freeform surfaces owing to their design flexibility, speed and cost-effectiveness. This paper presents a new hybrid method for constructing thin, rigid and free-form interconnected surfaces via fused deposition modelling (FDM) 3D printing and vacuum-forming that builds on recent advances in thermoforming circuits. 3D printing the sheet material allows for the embedding of conductive traces within thin layers of the substrate, which can be vacuum-formed but remain conductive and insulated. This is an unexplored fabrication technique within the context of designing and manufacturing connected things. In addition to explaining the method, this paper characterizes the behavior of vacuum-formed 3D printed sheets, analyses the electrical performance of printed traces after vacuum-forming, and showcases a range of sample artefacts constructed using the technique. In addition, the paper describes a new design interface for designing conformal interconnects that allows designers to draw conductive patterns in 3D and export pre-distorted sheet models ready to be printed.
Journal articleCursi F, Bai W, Li W, et al., 2022,
Journal articleSowe J, Varela Barreras J, Schimpe M, et al., 2022,
Model-informed battery current derating strategies: Simple methods to extend battery lifetime in islanded mini-grids, Journal of Energy Storage, Vol: 51, Pages: 1-9, ISSN: 2352-152X
Islanded mini-grids with batteries are crucial to enable universal access to energy. However, batteries are still costly, and how to select and operate them in an optimal manner is often unclear. The combination of variable climates with simple and low-cost passive thermal management also poses a challenge. Many techno-economic sizing tools usually consider simple battery degradation models, which disregard the impact of climatic conditions and operating strategy on battery performance. This study uses a semi-empirical Li-ion battery degradation model alongside an open-source techno-economic model to capture key insights. These are used to inform simple state of charge and temperature-based current derating strategies to increase lifetime. We demonstrate that such strategies can increase battery lifetime by 45% or 5–7 years in commercial systems already operational. It was found that, irrespective of climatic conditions, 80–90% of capacity fade can be attributed to calendar aging, due to low C-rates. SOC-based derating was found to be the most effective strategy, with temperature-based derating being less effective at extending lifetime and also leading to increased blackout periods. These results highlight the importance of accurate degradation modelling to achieve lifetime extension through improved operational strategies.
Conference paperClark A, Baron N, Orr L, et al., 2022,
On a balanced delta robot for precise aerial manipulation: implementation, testing, and lessons for future designs, IEEE/RSJ International Conference on Intelligent Robots and Systems, Publisher: IEEE
Using a delta-manipulator for stabilisation of anend-effector to perform precise spatial positioning is a currentarea of interest in aerial manipulation. High speed precisionmovements of a manipulator can cause disturbances to theaerial platform, which hinders trajectory tracking and in somecases could be sufficient to cause a loss of control of the vehicle.In this paper, a statically balanced delta aerial manipulator isdeveloped and evaluated. The system is balanced using threecounter-masses to reduce the force imparted onto the base andthus reduce perturbations to the movement of the drone. Thesystem is thoroughly tested following trajectories while mountedto a force sensor and while on-board an aerial vehicle. Resultsshow that the forces transmitted to the base in all axes arereduced considerably, however improvements in overall flightaccuracy are not observed in aerial settings. Design lessonsto make a balanced delta-manipulator viable for practicalimplementation on an aerial vehicle are discussed in depth.A video summarising the flight testing results is available athttps://youtu.be/fXKnosnVKCk.
Journal articleZhou Y, Myant C, Stewart R, 2022,
Sensors based on electronic textiles (e-textiles) have become increasingly prominent in the field of biomechanical monitoring technology due to multiple properties such as being lightweight, flexible, and comfortable, with increasing potential in incorporating into long-term monitoring devices. Previous research has been conducted into textile strain sensors based on graphene for human motion monitoring, however most graphene e-textile strain sensors exhibit poor sensitivity and stretchability. To our knowledge, no previous research has looked at knitted graphene-based fabrics in regards to the fabric composition of the substrate. In this paper, we propose a graphene/fabric composite sensor using a cost-effective dip coating method of an acrylic/Spandex knit fabric, and further explores its mechanical, electrical, and sensing properties. The developed graphene/textile composite sensor has a wide sensing range (up to 344%) and exhibits a good sensitivity with a high gauge factor of up to 16. As a wearable sensor, our sensing fabric can detect both large and subtle human motions and is able to distinguish between various ranges of joint movements, demonstrating its ability to function as a human motion monitoring system. Our sensor further exhibits the ability to be used as a supercapacitor or capacitive pressure sensor.
Journal articleLiu X, Zhang L, Yu H, et al., 2022,
Bridging multiscale characterization technologies and digital modeling to evaluate lithium battery full lifecycle, Advanced Energy Materials, ISSN: 1614-6832
The safety, durability and power density of lithium-ion batteries (LIBs) are currently inadequate to satisfy the continuously growing demand of the emerging battery markets. Rapid progress has been made from material engineering to system design, combining experimental results and simulations to enhance LIB performance. Limited by spatial and temporal resolution, state-of-the-art advanced characterization techniques fail to fully reveal the complex multi-scale degradation mechanism in LIBs. Strengthening interaction and iteration between characterization and modeling improves the understanding of reaction mechanisms as well as design and management of LIBs. Herein, a seed cyber hierarchy and interactional network framework is demonstrated to evaluate the overall lifecycle of LIBs. The typical examples of bridging the characterization techniques and modeling are discussed. The critical parameters extracted from multi-scale characterization can serve as digital inputs for modeling. Furthermore, advanced computational techniques including cloud computing, big data, machine learning, and artificial intelligence can also promote the comprehensive understanding and precise control of the whole battery lifecycle. Digital twins techniques will be introduced enabling the real-time monitoring and control of LIBs, autonomous computer-assisted characterizations and intelligent manufacturing. It is anticipated that this work will provide a roadmap for further intensive research on developing high-performance LIBs and intelligent battery management.
Journal articleZhao Y, Ouyang M, Wang Y, et al., 2022,
The practical application of rechargeable aqueous zinc batteries is impeded by dendrite growth, especially at high areal capacities and high current densities. Here, this challenge is addressed by proposing zinc perfluoro(2-ethoxyethane)sulfonic (Zn(PES)2) as a zinc battery electrolyte. This new amphipathic zinc salt, with a hydrophobic perfluorinated tail, can form an anode protecting layer, in situ, with a biomimetic lipid-bilayer structure. The layer limits the anode contact with free H2O and offers fast Zn2+ transport pathways, thereby effectively suppressing dendrite growth while maintaining high rate capability. A stable, Zn2+-conductive fluorinated solid electrolyte interphase (SEI) is also formed, further enhancing zinc reversibility. The electrolyte enables unprecedented cycling stability with dendrite-free zinc plating/stripping over 1600 h at 1 mA cm−2 at 2 mAh cm−2, and over 380 h under an even harsher condition of 2.5 mA cm−2 and 5 mAh cm−2. Full cell tests with a high-loading VS2 cathode demonstrate good capacity retention of 78% after 1000 cycles at 1.5 mA cm−2. The idea of in situ formation of a biomimetic lipid-bilayer anode protecting layer and fluorinated SEI opens a new route for engineering the electrode–electrolyte interface toward next-generation aqueous zinc batteries with long lifetime and high areal capacities.
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.