Publications
94 results found
Niu Z, Zhao W, Wu B, et al., 2023, π learning: a performance‐Informed framework for microstructural electrode design, Advanced Energy Materials, Vol: 13, Pages: 1-14, ISSN: 1614-6832
Designing high-performance porous electrodes is the key to next-generation electrochemical energy devices. Current machine-learning-based electrode design strategies are mainly orientated toward physical properties; however, the electrochemical performance is the ultimate design objective. Performance-orientated electrode design is challenging because the current data driven approaches do not accurately extract high-dimensional features in complex multiphase microstructures. Herein, this work reports a novel performance-informed deep learning framework, termed π learning, which enables performance-informed microstructure generation, toward overall performance prediction of candidate electrodes by adding most relevant physical features into the learning process. This is achieved by integrating physics-informed generative adversarial neural networks (GANs) with convolutional neural networks (CNNs) and with advanced multi-physics, multi-scale modeling of 3D porous electrodes. This work demonstrates the advantages of π learning by employing two popular design philosophies: forward and inverse designs, for the design of solid oxide fuel cells electrodes. π learning thus has the potential to unlock performance-driven learning in the design of next generation porous electrodes for advanced electrochemical energy devices such as fuel cells and batteries.
Ruan H, Barreras JV, Engstrom T, et al., 2023, Lithium-ion battery lifetime extension: A review of derating methods, JOURNAL OF POWER SOURCES, Vol: 563, ISSN: 0378-7753
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He R, Xie W, Wu B, et al., 2023, Towards interactional management for power batteries of electric vehicles, RSC Advances: an international journal to further the chemical sciences, Vol: 13, Pages: 2036-2056, ISSN: 2046-2069
With the ever-growing digitalization and mobility of electric transportation, lithium-ion batteries are facing performance and safety issues with the appearance of new materials and the advance of manufacturing techniques. This paper presents a systematic review of burgeoning multi-scale modelling and design for battery efficiency and safety management. The rise of cloud computing provides a tactical solution on how to efficiently achieve the interactional management and control of power batteries based on the battery system and traffic big data. The potential of selecting adaptive strategies in emerging digital management is covered systematically from principles and modelling, to machine learning. Specifically, multi-scale optimization is expounded in terms of materials, structures, manufacturing and grouping. The progress on modelling, state estimation and management methods is summarized and discussed in detail. Moreover, this review demonstrates the innovative progress of machine learning based data analysis in battery research so far, laying the foundation for future cloud and digital battery management to develop reliable onboard applications.
Ai 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.
Yang S, Zhou C, Wang Q, et al., 2022, Highly‐aligned ultra‐thick gel‐based cathodes unlocking ultra‐high energy density batteries, Energy & Environmental Materials, Vol: 5, Pages: 1332-1339, ISSN: 2575-0356
Increasing electrode thickness can substantially enhance the specific energy of lithium-ion batteries, however ionic transport, electronic conductivity and ink rheology are current barriers to adoption. Here a novel approach using a mixed xanthan gum and locust bean gum binder to construct ultra-thick electrodes is proposed to address above issues. After combining aqueous binder with single walled carbon nanotubes (SWCNT), active material (LiNi0.8Co0.1Mn0.1O2) and subsequent vacuum freeze drying, highly-aligned and low tortuosity structures with a porosity of ca. 50% can be achieved with an average pore size of 10 μm, whereby the gum binder-SWCNT-NMC811 forms vertical structures supported by tissue-like binder/SWCNT networks allowing for excellent electronic conducting phase percolation. As a result, ultra-thick electrodes with a mass loading of about 511 mg·cm-2 and 99.5 wt% active materials have been demonstrated with a remarkable areal capacity of 79.3 mAh·cm−2, which is the highest value reported so far. This represents a >25x improvement compared to conventional electrodes with an areal capacity of about 3 mAh·cm-2. This route also can be expanded to other electrode materials, such as LiFePO4 and Li4Ti5O12, and thus opens the possibility for low-cost and sustainable ultra-thick electrodes with increased specific energy for future lithium-ion batteries.
Brosa Planella F, Ai W, Boyce AM, et al., 2022, A continuum of physics-based lithium-ion battery models reviewed, Progress in Energy, Vol: 4
Physics-based electrochemical battery models derived from porous electrode theory are a very powerful tool for understanding lithium-ion batteries, as well as for improving their design and management. Different model fidelity, and thus model complexity, is needed for different applications. For example, in battery design we can afford longer computational times and the use of powerful computers, while for real-time battery control (e.g. in electric vehicles) we need to perform very fast calculations using simple devices. For this reason, simplified models that retain most of the features at a lower computational cost are widely used. Even though in the literature we often find these simplified models posed independently, leading to inconsistencies between models, they can actually be derived from more complicated models using a unified and systematic framework. In this review, we showcase this reductive framework, starting from a high-fidelity microscale model and reducing it all the way down to the single particle model, deriving in the process other common models, such as the Doyle-Fuller-Newman model. We also provide a critical discussion on the advantages and shortcomings of each of the models, which can aid model selection for a particular application. Finally, we provide an overview of possible extensions to the models, with a special focus on thermal models. Any of these extensions could be incorporated into the microscale model and the reductive framework re-applied to lead to a new generation of simplified, multi-physics models.
Liu X, Zhang L, Yu H, et al., 2022, Bridging multiscale characterization technologies and digital modeling to evaluate lithium battery full lifecycle, Advanced Energy Materials, Vol: 12, 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.
Zhao Y, Ouyang M, Wang Y, et al., 2022, Biomimetic lipid-bilayer anode protection for long lifetime aqueous zinc-metal batteries, Advanced Functional Materials, Vol: 32, ISSN: 1616-301X
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.
Ruan H, Chen J, Ai W, et al., 2022, Generalised diagnostic framework for rapid battery degradation quantification with deep learning, Energy and AI, Vol: 9, Pages: 1-13, ISSN: 2666-5468
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.
Tomaszewska 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 [1][2]. 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
Sowe 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.
Tomaszewska A, Parkes M, Doel R, et al., 2022, The Effects of Temperature and Cell Parameters on Lithium-Ion Battery Fast Charging Protocols: A Model-Driven Investigation, JOURNAL OF THE ELECTROCHEMICAL SOCIETY, Vol: 169, ISSN: 0013-4651
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Ai W, Kirkaldy N, Jiang Y, et al., 2022, A composite electrode model for lithium-ion batteries with silicon/graphite negative electrodes, Journal of Power Sources, Vol: 527, Pages: 231142-231142, ISSN: 0378-7753
Silicon is a promising negative electrode material with a high specific capacity, which is desirable for com-mercial lithium-ion batteries. It is often blended with graphite to form a composite anode to extend lifetime,however, the electrochemical interactions between silicon and graphite have not been fully investigated. Here,an electrochemical composite electrode model is developed and validated for lithium-ion batteries with asilicon/graphite anode. The continuum-level model can reproduce the voltage hysteresis and demonstratethe interactions between graphite and silicon. At high states-of-charge, graphite provides the majority of thereaction current density, however this rapidly switches to the silicon phase at deep depths-of-discharge due tothe different open circuit voltage curves, mass fractions and exchange current densities. Furthermore, operationat high C-rates leads to heterogeneous current densities in the through-thickness direction, where peak reactioncurrent densities for silicon can be found at the current collector–electrode side as opposed to the separator–electrode side for graphite. Increasing the mass fraction of silicon also highlights the beneficial impacts ofreducing the peak reaction current densities. This work, therefore, gives insights into the effects of siliconadditives, their coupled interactions and provides a platform to test different composite electrodes for betterlithium-ion batteries.
O'Kane SEJ, Ai W, Madabattula G, et al., 2022, Lithium-ion battery degradation: how to model it, Publisher: Royal Society of Chemistry
Predicting lithium-ion battery degradation is worth billions to the globalautomotive, aviation and energy storage industries, to improve performance andsafety and reduce warranty liabilities. However, very few published models ofbattery degradation explicitly consider the interactions between more than twodegradation mechanisms, and none do so within a single electrode. In thispaper, the first published attempt to directly couple more than two degradationmechanisms in the negative electrode is reported. The results are used to mapdifferent pathways through the complicated path dependent and non-lineardegradation space. Four degradation mechanisms are coupled in PyBaMM, an opensource modelling environment uniquely developed to allow new physics to beimplemented and explored quickly and easily. Crucially it is possible to see'inside' the model and observe the consequences of the different patterns ofdegradation, such as loss of lithium inventory and loss of active material. Forthe same cell, five different pathways that can result in end-of-life havealready been found, depending on how the cell is used. Such information wouldenable a product designer to either extend life or predict life based upon theusage pattern. However, parameterization of the degradation models remains as amajor challenge, and requires the attention of the international batterycommunity.
Roe C, Feng X, White G, et al., 2022, Immersion cooling for lithium-ion batteries – a review, Journal of Power Sources, Vol: 525, Pages: 231094-231094, ISSN: 0378-7753
Battery thermal management systems are critical for high performance electric vehicles, where the ability to remove heat and homogenise temperature distributions in single cells and packs are key considerations. Immersion cooling, which submerges the battery in a dielectric fluid, has the potential of increasing the rate of heat transfer by 10,000 times relative to passive air cooling. In 2-phase systems, this performance increase is achieved through the latent heat of evaporation of the liquid-to-gas phase transition and the resulting turbulent 2-phase fluid flow. However, 2-phase systems require additional system complexity, and single-phase direct contact immersion cooling can still offer up to 1,000 times improvements in heat transfer over air cooled systems. Fluids which have been considered include: hydrofluoroethers, mineral oils, esters and water-glycol mixtures. This review therefore presents the current state-of-the-art in immersion cooling of lithium-ion batteries, discussing the performance implications of immersion cooling but also identifying gaps in the literature which include a lack of studies considering the lifetime, fluid stability, material compatibility, understanding around sustainability and use of immersion for battery safety. Insights from this review will therefore help researchers and developers, from academia and industry, towards creating higher power, safer and more durable electric vehicles.
Sandwell P, Candelise C, Solomon B, et al., 2022, The role of mini-grids for electricity access and climate change mitigation in India, The role of mini-grids for electricity access and climate change mitigation in India
Steinhardt M, Barreras JV, Ruan H, et al., 2022, Meta-analysis of experimental results for heat capacity and thermal conductivity in lithium-ion batteries: A critical review, Journal of Power Sources, Vol: 522, Pages: 1-25, ISSN: 0378-7753
Scenarios with rapid energy conversion for lithium-ion batteries are increasingly relevant, due to the desire for more powerful electric tools or faster charging electric vehicles. However, higher power means higher cooling requirements, affecting the battery temperature and its thermal gradients. In turn, temperature is a key quantity influencing battery performance, safety and lifetime. Therefore, thermal models are increasingly important for the design and operation of battery systems. Key parameters are specific heat capacity and thermal conductivity. For these parameters, this paper presents a comprehensive review of the experimental results in the literature, where the median values and corresponding uncertainties are summarized. Whenever available, data is analyzed from component to cell level with the discussion of dependencies on temperature, state of charge (SOC) and state of health (SOH). This meta-analysis reveals gaps in knowledge and research needs. For instance, we uncover inconsistencies between the specific heat capacity of electrode-separator stacks and full-cells. For the thermal conductivity, we found that thermal contact resistance and dependencies on battery states have been poorly studied. There is also a lack of measurements at high temperatures, which are required for safety studies. Overall, this study serves as a valuable reference material for both modellers and experimenters.
Chen J, Naylor Marlow M, Jiang Q, et al., 2022, Peak-tracking method to quantify degradation modes in lithium-ion batteries via differential voltage and incremental capacity, Journal of Energy Storage, Vol: 45, Pages: 1-12, ISSN: 2352-152X
Incremental capacity (IC) and differential voltage (DV) analyses are effective for monitoring battery health, however, the diagnosis often requires considerable parameterisation efforts and a low scan rate. In this work, a simple-to-parameterise quantitative diagnostic approach is presented, which differentiates between loss of lithium inventory and loss of active materials in the anode and cathode. With an open-circuit voltage model and a genetic algorithm optimisation routine, peak signatures in voltage and capacity differentials are used to quantify degradation modes as opposed to traditional approaches of matching the whole voltage and capacity spectra. The outputs are validated with synthetic IC-DV spectra and achieve a low root-mean-square error of ± 2.0 %. A similar level of accuracy is achieved when heterogeneity is introduced in the synthetic degradation data and also with partial discharge data. Experiments from pouch cells under 5 C discharge and 0.3 C charge cycling at 25 °C and 45 °C, together with post-mortem measurements, confirm the accuracy of this approach with diagnosis scan taken at 0.3 C. The IC-DV peak-tracking quantitative diagnostic code demonstrates a reliable and easy-to-implement means of extracting deeper insights into battery degradation and is shared alongside this manuscript to help academia and industry develop better lifetime predictions.
Qin Y, Chen X, Tomaszewska A, et al., 2021, Lithium-ion batteries under pulsed current operation to stabilize future grids, Cell Reports Physical Science, Vol: 3, ISSN: 2666-3864
The large-scale utilization of renewable energy sources can lead to grid instability due to dynamic fluctuations in generation and load. Operating lithium-ion batteries (LIBs) under pulsed operation can effectively address these issues, owing to LIBs providing the rapid response and high energy density required. LIB deployment is also expected to reach 20 TWh from a vehicle-to-grid application by 2030. This review therefore highlights pulsed operation on LIBs for future grids, covering mechanisms, effects, and supporting hardware. Specific attention is paid to the fundamental mechanisms of pulsed operation on the stability of electric power system and micro-evolution in cells. The pulsed operation with appropriate parameters can provide superior effects for LIBs even under high-power charging and low-temperature operation. The hardware that supports bidirectional pulse is also introduced. This review presents the potential of LIBs participating in grid service via pulsed operation and may provide forward-looking guidance for the community.
Pang M-C, Yang K, Brugge R, et al., 2021, Interactions are important: Linking multi-physics mechanisms to the performance and degradation of solid-state batteries, MATERIALS TODAY, Vol: 49, Pages: 145-183, ISSN: 1369-7021
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- Citations: 28
Tomaszewska A, Parkes M, Doel R, et al., 2021, Lithium Plating Heterogeneity Caused by Realistic Thermal Gradients, ECS Meeting Abstracts, Vol: MA2021-02, Pages: 460-460
Chakrabarti BK, Kalamaras E, Ouyang M, et al., 2021, Trichome-like Carbon-Metal Fabrics Made of Carbon Microfibers, Carbon Nanotubes, and Fe-Based Nanoparticles as Electrodes for Regenerative Hydrogen/Vanadium Flow Cells, ACS APPLIED NANO MATERIALS, Vol: 4, Pages: 10754-10763
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Yang S, Zhang Z, Cao R, et al., 2021, Implementation for a cloud battery management system based on the CHAIN framework, Energy and AI, Vol: 5, Pages: 100088-100088, ISSN: 2666-5468
An intelligent battery management system is a crucial enabler for energy storage systems with high power output, increased safety and long lifetimes. With recent developments in cloud computing and the proliferation of big data, machine learning approaches have begun to deliver invaluable insights, which drives adaptive control of battery management systems (BMS) with improved performance. In this paper, a general framework utilizing an end-edge-cloud architecture for a cloud-based BMS is proposed, with the composition and function of each link described. Cloud-based BMS leverages from the Cyber Hierarchy and Interactional Network (CHAIN) framework to provide multi-scale insights, more advanced and efficient algorithms can be used to realize the state-of-X estimation, thermal management, cell balancing, fault diagnosis and other functions of traditional BMS system. The battery intelligent monitoring and management platform can visually present battery performance, store working-data to help in-depth understanding of the microscopic evolutionary law, and provide support for the development of control strategies. Currently, the cloud-based BMS requires more effects on the multi-scale integrated modeling methods and remote upgrading capability of the controller, these two aspects are very important for the precise management and online upgrade of the system. The utility of this approach is highlighted not only for automotive applications, but for any battery energy storage system, providing a holistic framework for future intelligent and connected battery management.
Schimpe M, Varela Barreras J, Wu B, et al., 2021, Battery degradation-aware current derating: an effective method to prolong lifetime and ease thermal management, Journal of The Electrochemical Society, Vol: 168, Pages: 1-13, ISSN: 0013-4651
To ensure the safe and stable operation of lithium-ion batteries in battery energy storage systems (BESS), the power/current is de-rated to prevent the battery from going outside the safe operating range. Most derating strategies use static limits for battery current, voltage, temperature and state-of-charge, and do not account for the complexity of battery degradation. Progress has been made with models of lithium plating for fast charging. However, this is a partial solution, does not consider other degradation mechanisms, and still requires complex optimization work, limiting widespread adoption. In this work, the calendar and cycle degradation model is analysed offline to predetermine the degradation rates. The results are integrated into the current-derating strategy. This framework can be adapted to any degradation model and allows flexible tuning. The framework is evaluated in simulations of an outdoors-installed BESS with passive thermal management, which operates in a residential photovoltaic application. In comparison to standard derating, the degradation-aware derating achieves: (1) increase of battery lifetime by 65%; (2) increase in energy throughput over lifetime by 49%, while III) energy throughput per year is reduced by only 9.5%. These results suggest that the derating framework can become a new standard in current derating.
Ojha M, Wu B, Deepa M, 2021, Cost-Effective MIL-53(Cr) metal–organic framework-based supercapacitors encompassing fast-ion (Li+/H+/Na+) conductors, ACS Applied Energy Materials, Vol: 4, Pages: 4729-4743, ISSN: 2574-0962
A chromium-based low-cost metal–organic framework (MOF) cathode, MIL (Matériaux de l′Institut Lavoisier)-53(Cr), is coupled with a bioderived porous carbon (BPC) anode, produced from abundantly available agricultural waste betel nut shells in an asymmetric supercapacitor, for the first time. The impact of the electrolyte on the electrochemical behavior of an asymmetric BPC//MIL-53(Cr) supercapacitor was assessed by constructing cells with the following electrolytes: proton-conducting camphorsulfonic acid (CSA), Li+-ion-conducting solutions of LiClO4, Na+-ion-conducting sodium poly(4-styrene sulfonate) solution, and ionic liquid (IL:1-butyl-1-methyl-pyrrolidinium trifluoromethanesulfonate)-based solutions. The aqueous H+-ion-based CSA electrolyte shows a superior ionic conductivity (270 mS cm–1) and an enhanced transport number (0.96), carries larger ionic currents, and retains high conductivity even at subambient temperatures, clearly outperforming all the other Li+/Na+/IL electrolytes. The BPC/aqueous CSA or LiClO4/MIL-53(Cr) supercapacitors show enhanced storage performances, with the H+ cell having a specific capacitance of 70 F g–1 and energy and power density maxima of 9.7 Wh kg–1 and 0.25 kW kg–1 and enduring 104 cycles. A detailed account of the dependence of the electrolyte cation/anion- and solvent-type on electrochemical charge storage provides a basis for adapting these design principles to developing high-performance MOF-based supercapacitors.
Ouyang M, Bertei A, Cooper SJ, et al., 2021, Model-guided design of a high performance and durability Ni nanofiber/ceria matrix solid oxide fuel cell electrode, Journal of Energy Chemistry, Vol: 56, Pages: 98-112, ISSN: 2095-4956
Mixed ionic electronic conductors (MIECs) have attracted increasing attention as anode materials for solid oxide fuel cells (SOFCs) and they hold great promise for lowering the operation temperature of SOFCs. However, there has been a lack of understanding of the performance-limiting factors and guidelines for rational design of composite metal-MIEC electrodes. Using a newly-developed approach based on 3D-tomography and electrochemical impedance spectroscopy, here for the first time we quantify the contribution of the dual-phase boundary (DPB) relative to the three-phase boundary (TPB) reaction pathway on real MIEC electrodes. A new design strategy is developed for Ni/gadolinium doped ceria (CGO) electrodes (a typical MIEC electrode) based on the quantitative analyses and a novel Ni/CGO fiber–matrix structure is proposed and fabricated by combining electrospinning and tape-casting methods using commercial powders. With only 11.5 vol% nickel, the designer Ni/CGO fiber–matrix electrode shows 32% and 67% lower polarization resistance than a nano-Ni impregnated CGO scaffold electrode and conventional cermet electrode respectively. The results in this paper demonstrate quantitatively using real electrode structures that enhancing DPB and hydrogen kinetics are more efficient strategies to enhance electrode performance than simply increasing TPB.
Niu Z, Pinfield V, Wu B, et al., 2021, Towards the digitalisation of porous energy materials: evolution of digital approaches for microstructural design, Energy and Environmental Science, Vol: 14, Pages: 2549-2576, ISSN: 1754-5692
Porous energy materials are essential components of many energy devices and systems, the development of which have been long plagued by two main challenges. The first is the ‘curse of dimensionality’, i.e. the complex structure–property relationships of energy materials are largely determined by a high-dimensional parameter space. The second challenge is the low efficiency of optimisation/discovery techniques for new energy materials. Digitalisation of porous energy materials is currently being considered as one of the most promising solutions to tackle these issues by transforming all material information into the digital space using reconstruction and imaging data and fusing this with various computational methods. With the help of material digitalisation, the rapid characterisation, the prediction of properties, and the autonomous optimisation of new energy materials can be achieved by using advanced mathematical algorithms. In this paper, we review the evolution of these computational and digital approaches and their typical applications in studying various porous energy materials and devices. Particularly, we address the recent progress of artificial intelligence (AI) in porous energy materials and highlight the successful application of several deep learning methods in microstructural reconstruction and generation, property prediction, and the performance optimisation of energy materials in service. We also provide a perspective on the potential of deep learning methods in achieving autonomous optimisation and discovery of new porous energy materials based on advanced computational modelling and AI techniques.
Gao X, Liu X, He R, et al., 2021, Designed high-performance lithium-ion battery electrodes using a novel hybrid model-data driven approach, Energy Storage Materials, Vol: 36, Pages: 435-458, ISSN: 2405-8297
Lithium-ion batteries (LIBs) have been widely recognized as the most promising energy storage technology due to their favorable power and energy densities for applications in electric vehicles (EVs) and other related functions. However, further improvements are needed which are underpinned by advances in conventional electrode designs. This paper reviews conventional and emerging electrode designs, including conventional LIB electrode modification techniques and electrode design for next-generation energy devices. Thick electrode designs with low tortuosity are the most conventional approach for energy density improvement. Chemistries such as lithium-sulfur, lithium-air and solid-state batteries show great potential, yet many challenges remain. Microscale structural modelling and macroscale functional modelling methods underpin much of the electrode design work and these efforts are summarized here. More importantly, this paper presents a novel framework for next-generation electrode design termed: Cyber Hierarchy And Interactional Network based Multiscale Electrode Design (CHAIN-MED), a hybrid solution combining model-based and data-driven techniques for optimal electrode design, which significantly shortens the development cycle. This review, therefore, provides novel insights into combining existing design approaches with multiscale models and machine learning techniques for next-generation LIB electrodes.
Edge JS, O'Kane S, Prosser R, et al., 2021, Lithium ion battery degradation: what you need to know, Physical Chemistry Chemical Physics, Vol: 23, Pages: 8200-8221, ISSN: 1463-9076
The expansion of lithium-ion batteries from consumer electronics to larger-scale transport and energy storage applications has made understanding the many mechanisms responsible for battery degradation increasingly important. The literature in this complex topic has grown considerably; this perspective aims to distil current knowledge into a succinct form, as a reference and a guide to understanding battery degradation. Unlike other reviews, this work emphasises the coupling between the different mechanisms and the different physical and chemical approaches used to trigger, identify and monitor various mechanisms, as well as the various computational models that attempt to simulate these interactions. Degradation is separated into three levels: the actual mechanisms themselves, the observable consequences at cell level called modes and the operational effects such as capacity or power fade. Five principal and thirteen secondary mechanisms were found that are generally considered to be the cause of degradation during normal operation, which all give rise to five observable modes. A flowchart illustrates the different feedback loops that couple the various forms of degradation, whilst a table is presented to highlight the experimental conditions that are most likely to trigger specific degradation mechanisms. Together, they provide a powerful guide to designing experiments or models for investigating battery degradation.
Yang S, Zhou S, Hua Y, et al., 2021, A parameter adaptive method for state of charge estimation of lithium-ion batteries with an improved extended Kalman filter., Scientific Reports, Vol: 11, Pages: 1-15, ISSN: 2045-2322
An accurate state of charge (SOC) estimation in battery management systems (BMS) is of crucial importance to guarantee the safe and effective operation of automotive batteries. However, the BMS consistently suffers from inaccuracy of SOC estimation. Herein, we propose a SOC estimation approach with both high accuracy and robustness based on an improved extended Kalman filter (IEKF). An equivalent circuit model is established, and the simulated annealing-particle swarm optimization (SA-PSO) algorithm is used for offline parameter identification. Furthermore, improvements have been made with noise adaptation, a fading filter and a linear-nonlinear filtering based on the traditional EKF method, and rigorous mathematical proof has been carried out accordingly. To deal with model mismatch, online parameter identification is achieved by a dual Kalman filter. Finally, various experiments are performed to validate the proposed IEKF. Experimental results show that the IEKF algorithm can reduce the error to 2.94% under dynamic stress test conditions, and robustness analysis is verified with noise interference, hence demonstrating its practicability for extending to state estimation of battery packs applied in real-world operating conditions.
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