Publications
134 results found
Ma B, Zhang L, Wang W, et al., 2024, Application of deep learning for informatics aided design of electrode materials in metal-ion batteries, Green Energy and Environment, Vol: 9, Pages: 877-889, ISSN: 2096-2797
To develop emerging electrode materials and improve the performances of batteries, the machine learning techniques can provide insights to discover, design and develop battery new materials in high-throughput way. In this paper, two deep learning models are developed and trained with two feature groups extracted from the Materials Project datasets to predict the battery electrochemical performances including average voltage, specific capacity and specific energy. The deep learning models are trained with the multilayer perceptron as the core. The Bayesian optimization and Monte Carlo methods are applied to improve the prediction accuracy of models. Based on 10 types of ion batteries, the correlation coefficients are maintained above 0.9 compared to DFT calculation results and the mean absolute error of the prediction results for voltages of two models can reach 0.41 V and 0.20 V, respectively. The electrochemical performance prediction times for the two trained models on thousands of batteries are only 72.9 ms and 75.7 ms. Besides, the two deep learning models are applied to approach the screening of emerging electrode materials for sodium-ion and potassium-ion batteries. This work can contribute to a high-throughput computational method to accelerate the rational and fast materials discovery and design.
Li Y, Gao X, Wang H, et al., 2024, Direct venting during fast charging of lithium-ion batteries, Journal of Power Sources, Vol: 592, ISSN: 0378-7753
Lithium-ion batteries pose high risks of failure when subjected to fast charging due to accumulated degradation from side reactions. Venting is a common failure behaviour that results in the release of gases and depressurization. However, the connection between fast-charging degradation and venting characteristics remains unclear. In this study, fast-charging experiments are conducted on LiNi0.6Mn0.2Co0.2O2 (NMC622)/graphite prismatic cells (51 Ah) with controlled cooling, followed by adiabatic thermal runaway tests. The degraded cell, after 4C charging, shows venting at a temperature 34.4 °C lower than a fresh cell. Gas analysis of the degraded anode-electrolyte partially reactive system in hot-box tests reveals that the reaction between plated lithium and electrolyte was a primary contributor to the additional gases. The fast-charging protocols are conducted under natural convection with less heat dissipation, causing battery swelling and venting without thermal runaway when subjected to excessive cycling at off-limit charge rates (5C and 5.6C). The temperature decreasing rates during rest after each charging process slow down or stabilize for a certain period, highlighting the role of exothermic side reactions. This experimentally demonstrates that venting failures can be directly triggered by the exothermic, gas generating reaction during fast charging. These findings inform future designs for safe and rapid battery charging.
Tongsh C, Wu S, Jiao K, et al., 2024, Fuel cell stack redesign and component integration radically increase power density, Joule, Vol: 8, Pages: 175-192
The drawbacks of conventional channel-rib flow fields and gas diffusion layers (GDLs) significantly limit the mass transfer and water management capability of proton exchange membrane fuel cells (PEMFCs), impacting volumetric power density. We report a GDL-less design of electrode-flow field integration comprised of graphene-coated Ni foam and ultrathin (9.1 μm) carbon nanofiber film as an alternative to conventional channel-rib flow fields and GDLs, which substantially reduces membrane electrode assembly volume (90%), reactant transport distance (96%), and concentration impedance (88.6%), resulting in a remarkable 50% power density increase. The GDL-less design provides an effective strategy for the rational design of integrated electrode-flow field and will guide the future development of PEMFCs for their practical applications in energy conversion technologies. We estimate that the peak volumetric power density a PEMFC stack employing GDL-less design can achieve is 9.8 kW L−1, representing an increase of more than 80% compared with the state-of-the-art commercial PEMFC stack.
Roy T, Goel S, Costa LT, et al., 2023, Strain induced electrochemical behaviors of ionic liquid electrolytes in an electrochemical double layer capacitor: Insights from molecular dynamics simulations., J Chem Phys, Vol: 159
Electrochemical Double Layer Capacitors (EDLCs) with ionic liquid electrolytes outperform conventional ones using aqueous and organic electrolytes in energy density and safety. However, understanding the electrochemical behaviors of ionic liquid electrolytes under compressive/tensile strain is essential for the design of flexible EDLCs as well as normal EDLCs, which are subject to external forces during assembly. Despite many experimental studies, the compression/stretching effects on the performance of ionic liquid EDLCs remain inconclusive and controversial. In addition, there is hardly any evidence of prior theoretical work done in this area, which makes the literature on this topic scarce. Herein, for the first time, we developed an atomistic model to study the processes underlying the electrochemical behaviors of ionic liquids in an EDLC under strain. Constant potential non-equilibrium molecular dynamics simulations are conducted for EMIM BF4 placed between two graphene walls as electrodes. Compared to zero strain, low compression of the EDLC resulted in compromised performance as the electrode charge density dropped by 29%, and the performance reduction deteriorated significantly with a further increase in compression. In contrast, stretching is found to enhance the performance by increasing the charge storage in the electrodes by 7%. The performance changes with compression and stretching are due to changes in the double-layer structure. In addition, an increase in the value of the applied potential during the application of strain leads to capacity retention with compression revealed by the newly performed simulations.
Bonkile MP, Jiang Y, Kirkaldy N, et al., 2023, Coupled electrochemical-thermal-mechanical stress modelling in composite silicon/graphite lithium-ion battery electrodes, Journal of Energy Storage, Vol: 73, ISSN: 2352-152X
Silicon is often added to graphite battery electrodes to enhance the electrode-specific capacity, but it undergoes significant volume changes during (de)lithiation, which results in mechanical stress, fracture, and performance degradation. To develop long-lasting and energy-dense batteries, it is critical to understand the non-linear stress behaviour in composite silicon-graphite electrodes. In this study, we developed a coupled electrochemical-thermal-mechanical model of a composite silicon/graphite electrode in PyBaMM (an open-source physics-based modelling platform). The model is experimentally validated against a commercially available LGM50T battery, and the effects of C-rates, depth-of-discharge (DoD), and temperature are investigated. The developed model can reproduce the voltage hysteresis from the silicon and provide insights into the stress response and crack growth/propagation in the two different phases. The stress in the silicon is relatively low at low DoD but rapidly increases at a DoD >~80%, whereas the stress in the graphite increases with decreasing temperature and DoD. At higher C-rates, peak stress in the graphite increases as expected, however, this decreases for silicon due to voltage cut-offs being hit earlier, leading to lower active material utilisation since silicon is mostly active at high DoD. Therefore, this work provides an improved understanding of stress evolution in composite silicon/graphite lithium-ion batteries.
Wang J, Jiang H, Chen G, et al., 2023, Integration of multi-physics and machine learning-based surrogate modelling approaches for multi-objective optimization of deformed GDL of PEM fuel cells, Energy and AI, Vol: 14, Pages: 1-14, ISSN: 2666-5468
The development of artificial intelligence (AI) greatly boosts scientific and engineering innovation. As one of the promising candidates for transiting the carbon intensive economy to zero emission future, proton exchange membrane (PEM) fuel cells has aroused extensive attentions. The gas diffusion layer (GDL) strongly affects the water and heat management during PEM fuel cells operation, therefore multi-variable optimization, including thickness, porosity, conductivity, channel/rib widths and compression ratio, is essential for the improved cell performance. However, traditional experiment-based optimization is time consuming and economically unaffordable. To break down the obstacles to rapidly optimize GDLs, physics-based simulation and machine-learning-based surrogate modelling are integrated to build a sophisticated M5 model, in which multi-physics and multi-phase flow simulation, machine-learning-based surrogate modelling, multi-variable and multi-objects optimization are included. Two machine learning methodologies, namely response surface methodology (RSM) and artificial neural network (ANN) are compared. The M5 model is proved to be effective and efficient for GDL optimization. After optimization, the current density and standard deviation of oxygen distribution at 0.4 V are improved by 20.8% and 74.6%, respectively. Pareto front is obtained to trade off the cell performance and homogeneity of oxygen distribution, e.g., 20.5% higher current density is achieved when sacrificing the standard deviation of oxygen distribution by 26.0%.
Leong KW, Pan W, Yi X, et al., 2023, Next-generation magnesium-ion batteries: The quasi-solid-state approach to multivalent metal ion storage, SCIENCE ADVANCES, Vol: 9, ISSN: 2375-2548
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.
Zhao W, Pinfield VJ, Wang H, et al., 2023, An open source framework for advanced multi-physics and multiscale modelling of solid oxide fuel cells, Energy Conversion and Management, Vol: 280, Pages: 1-15, ISSN: 0196-8904
Solid oxide fuel cells are high-efficiency renewable energy devices and considered one of the most promising net-zero carbon energy technologies. Numerical modelling is a powerful tool for the virtual design and optimisation of the next-generation solid oxide fuel cells but needs to tackle issues for incorporating the multi-scale character of the cell and further improving the accuracy and computational efficiency. While most of solid oxide fuel cell models were developed based on closed source platforms which limit the freedom of customisation in numerical discretization schemes and community participation. Here, an open source multi-physics and multiscale platform for advanced SOFC simulations consisting of both cell- and pore-scale performance models was developed using OpenFOAM. The modelling aspects are elucidated in detail, involving the coupling of various physical equations and the implementation of the pore-scale electrode in the performance model. The entire platform was carefully validated against experimental data and the other numerical models which were implemented in commercial software ANSYS Fluent and based on the lattice Boltzmann method. The cell-scale model is subsequently employed to study the effects of different fuels, i.e., pure hydrogen and different ratios of pre-reformed methane gas under various operating temperatures. It is found that the cell-scale model reasonably predicts the effects of these parameters on the cell performance, aligning well with the Fluent model. This study further identified the size of representative element volume with respect to the current density for the anode via the pore-scale model where the realistic microstructures reconstructed by a Xe plasma focused ion beam–scanning electron microscopy are employed as computational domains. It is found that a volume element size of 1243 voxels is sufficient to yield the representative current density of the whole. All these numerical investigations show that OpenF
Zhang Y, Zhao S, Zhou T, et al., 2023, Experimental and Numerical Investigations of a Thermal Management System Using Phase-Change Materials and Forced-Air Cooling for High-Power Li-Ion Battery Packs, BATTERIES-BASEL, Vol: 9
Zhang G, Wu L, Tongsh C, et al., 2023, Structure Design for Ultrahigh Power Density Proton Exchange Membrane Fuel Cell., Small Methods, Vol: 7
Next-generation ultrahigh power density proton exchange membrane fuel cells rely not only on high-performance membrane electrode assembly (MEA) but also on an optimal cell structure. To this end, this work comprehensively investigates the cell performance under various structures, and it is revealed that there is unexploited performance improvement in structure design because its positive effect enhancing gas supply is often inhibited by worse proton/electron conduction. Utilizing fine channel/rib or the porous flow field is feasible to eliminate the gas diffusion layer (GDL) and hence increase the power density significantly due to the decrease of cell thickness and gas/electron transfer resistances. The cell structure combining fine channel/rib, GDL elimination and double-cell structure is believed to increase the power density from 4.4 to 6.52 kW L-1 with the existing MEA, showing nearly equal importance with the new MEA development in achieving the target of 9.0 kW L-1 .
Gao X, Li Y, Wang H, et al., 2023, Probing inhomogeneity of electrical-thermal distribution on electrode during fast charging for lithium-ion batteries, Applied Energy, ISSN: 0306-2619
Liu Y, Pan Z, Esan OC, et al., 2022, Efficient electrocatalytic nitrogen reduction to ammonia with FeNi-Co/ carbon mat electrodes, JOURNAL OF ALLOYS AND COMPOUNDS, Vol: 927, ISSN: 0925-8388
Zhang L, Chen S, Wang W, et al., 2022, Enabling dendrite-free charging for lithium batteries based on transport-reaction competition mechanism in CHAIN framework, Journal of Energy Chemistry, Vol: 75, Pages: 408-421, ISSN: 2095-4956
Worldwide trends in mobile electrification will skyrocket demands for lithium-based battery production, driven by the popularity of electric vehicles. However, both lithium metal batteries and lithium ion batteries face severe safety issues due to dendrite nucleation and growth process. Li deposition is significantly influenced by interfacial factors and charging conditions. In this paper, an electrochemical model considering the internal and external factors is proposed based on Monte Carlo method. The influence of internal solid electrolyte interphase (SEI) porosity, thickness and the external conditions on dendrite growth process is systematically described. The simulation results support that the three factors investigated in this model could synergistically regulate the dendrite growth process. Three competition mechanisms are proposed to tailor lithium deposition for Li-based batteries and numerical solutions for variation pattern of dendrite growth with time are fitted. A three-step process describing kinetic process of lithium deposition is proposed. To achieve dendrite-free charging process, charging strategies and emerging materials design should be considered, including physicochemical materials engineering, artificial SEI, and design for dynamic safety boundary. This work could contribute to the foundation for insights of Li deposition mechanism, which is promising to provide guidelines for next-generation high-energy-density and safe batteries in CHAIN framework.
Jiang Y, Zhang L, Offer G, et al., 2022, A user-friendly lithium battery simulator based on open-source CFD, Digital Chemical Engineering, Vol: 5, Pages: 1-14, ISSN: 2772-5081
The growing use of lithium-ion batteries (LIBs) for automotive and stationary storage applications has put increasingly stringent requirements on battery thermal management and battery safety. An open-source platform that can bridge battery electrochemical models and computational fluid dynamics (CFD) can be of great benefit for designing advanced battery thermal management systems and safety countermeasures by allowing the simulation and prediction of battery responses to various thermofluidic environments and thermal boundaries. Here we develop a user-friendly battery simulator based on the open-source CFD code OpenFOAM. The simulator contains the in-house solvers for the two mostly used physics-based battery models, the single particle model, and the pseudo-two-dimensional model. GUIs are also developed based on Qt for simulation automation and ease of use. To demonstrate the functionality of the developed simulator, the electrochemical performance and internal states of half LIB cells and full LIB cells with different chemistries at different operating conditions are simulated. The obtained results agree well with other existing battery simulators. Due to its native integration with OpenFOAM, the new battery simulator is readily extendable to incorporate various CFD models and other physics to meet the simulation needs of thermal management and safety design for LIBs.
Pan Y, Wang H, Brandon NP, 2022, A fast two-phase non-isothermal reduced-order model for accelerating PEM fuel cell design development, International Journal of Hydrogen Energy, Vol: 47, Pages: 38774-38792, ISSN: 0360-3199
A reduced-order model (ROM) is developed for proton exchange membrane fuel cells (PEMFCs) considering the non-isothermal two-phase effects, with the goal of enhancing computational efficiency and thus accelerating fuel cell design development. Using analytical order reduction and approximation methods, the fluxes and source terms in conventional 1D conservation equations are reduced to six computing nodes at the interfaces between each cell component. The errors associated with order reduction are minimized by introducing new approximation methods for the potential distribution, the transport properties, and the membrane hydration status. The trade-off between model accuracy and computational efficiency is studied by comparing the simulation results and computational times of the new model with a full 1D model. The new model is nearly two orders of magnitude faster without sacrificing too much accuracy (<4% difference) compared to the 1D model. The new model is then used to analyze the influence of the membrane electrode assembly (MEA) design on cell performance and internal state distributions, offering insights into MEA structural optimization. The model can be readily extended to account for more detailed physico-chemical processes, such as Knudsen diffusion or the influence of micro-porous layers, and it can be an effective tool for understanding and designing PEMFCs.
Ma B, Yang S, Zhang L, et al., 2022, Remaining useful life and state of health prediction for lithium batteries based on differential thermal voltammetry and a deep-learning model, Journal of Power Sources, Vol: 548, ISSN: 0378-7753
Lithium-ion batteries (LIBs) are widely used in the assembly of battery packs for electric vehicles and energy storage grids due to their high power density, low self-discharge rate and reasonable costs. Accurate estimation of state of health (SOH) and remaining useful life (RUL) are crucial challenges in developing battery management systems (BMS). In this paper, differential thermal voltammetry (DTV) signal analysis methods and recursive neural networks data-driven methods are combined to approach battery degradation tracking. Firstly, with the Savitzky-Golay (SG) method and Pearson correlation analysis, the DTV curve is smoothed, and three useful feature variables are extracted from different dimensions, bridging signaling characteristics and phase transition characteristics. Then four recursive neural networks are constructed and compared based on NASA databases. The Bayesian optimization method is applied to improve hyperparameter values and the Monte Carlo (MC) simulation is used to quantify uncertainties. The proposed data-driven method can predict the RUL and estimate the SOH of battery accurately. The root mean square error (RMSE) for prediction results could reach below 1% and the capacity rebound phenomenon could be captured as well. The proposed integrated degradation model can contribute to the real-time prediction and optimization of battery health conditions based on cloud computing platform, promoting the continuous development of cloud battery management systems in framework of Cyber Hierarchy and Interactional Network (CHAIN).
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.
Xue S, Fu Y, Song Z, et al., 2022, Coil‐to‐stretch transition of binder chains enabled by “Nano‐Combs” to facilitate highly stable SiOx anode, Energy & Environmental Materials, Vol: 5, Pages: 1310-1316, ISSN: 2575-0356
The commercialized binder carboxymethyl cellulose sodium (CMC-Na) is considered unsuitable for micro-sized SiOx anode as it cannot endure the large volume change to retain the conductive network during repeated charge/discharge cycles. Herein, a small amount of silicon nano particles (SiNPs) is added during slurry preparation process as “nano-combs” to unfold the convoluted CMC-Na polymer chains so that they undergo a coil-to-stretch transition by interaction between polar groups (e.g -OH, -COONa) of polymer and SiNPs’ large surface. Through maximizing the utilization of binders, a uniform conductive network is constructed with increased interfacial contact with micro-sized SiOx. As a result, the SiOx electrode with optimized (10 wt%) SiNPs addition shows significantly improved initial capacity as well as cycling performance. Through revisiting CMC-Na, a currently deemed unqualified binder in SiOx anode, this work gives a brand-new perspective on the failing mechanism of Si-based anode materials as well as an improving strategy for electrode preparation.
Wang W, Zhang L, Yu H, et al., 2022, Early Prediction of the Health Conditions for Battery Cathodes Assisted by the Fusion of Feature Signal Analysis and Deep-Learning Techniques, BATTERIES-BASEL, Vol: 8
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- Citations: 4
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.
Pan Z, Zhang Z, Tahir A, et al., 2022, Ultralow loading FeCoNi alloy nanoparticles decorated carbon mat for hydrogen peroxide reduction reaction and its application in direct ethylene glycol fuel cells, International Journal of Energy Research, Vol: 46, Pages: 13820-13831, ISSN: 0363-907X
Hydrogen peroxide has been an attractive oxidant in direct liquid fuel cells. However, hydrogen peroxide reduction reaction heavily relies on noble metal-based electrocatalysts. In this work, a carbon mat decorated with FeCoNi alloy nanoparticles (namely FeCoNi/CM) of an ultralow loading, that is, 0.146 mg cm−2, for hydrogen peroxide reduction is designed, fabricated, and applied as a free-standing cathode in a passive alkaline-acid direct ethylene glycol fuel cell. A piece of Pd/C coated carbon cloth (1.0 mg cm−2) is used as the anode and a pre-treated Nafion 211 membrane as the membrane. This passive fuel cell yields a peak power density of 17.4 mW cm−2 at 23°C, which is comparable to an Au/C-based cathode (17.0 mW cm−2). The new electrode shows significantly enhanced mass transfer and allows a current density of 60.0 mA cm−2, which is 1.5 times the value achieved with the Au/C-based cathode. This can be attributed to the much thinner thickness of FeCoNi/CM (50.0 μm) than Au/C-based cathode (380.0 μm). With the thinner thickness, the oxygen derived from the self-decomposition of hydrogen peroxide can be effectively removed from the cathode, which is beneficial for the transport of hydrogen peroxide to the catalyst surface. Moreover, with the use of this free-standing cathode, the passive fuel cell attains a continuous operation at a constant discharging current of 20.0 mA for more than 9 h, exceeding the 5 h achieved with the Au/C-based cathode at the same discharging current density.
Penjuru NMH, Reddy GV, Nair MR, et al., 2022, Machine Learning Aided Predictions for Capacity Fade of Li-Ion Batteries, 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.
Jiang Y, Niu Z, Offer G, et al., 2022, Insights into the role of silicon and graphite in the electrochemical performance of silicon/graphite blended electrodes with a multi-material porous electrode model, Journal of The Electrochemical Society, Vol: 169, Pages: 020568-020568, ISSN: 0013-4651
Silicon/graphite blended electrodes are promising candidates to replace graphite in lithium ion batteries, benefiting from the high capacity of silicon and the good structural stability of carbon. Models have proven essential to understand and optimise batteries with new materials. However, most previous models treat silicon/graphite blends as a single “lumped” material, offering limited understanding of the behaviors of the individual materials and thus limited design capability. Here, we present a multi-material model for silicon/graphite electrodes with detailed descriptions of the contributions of the individual active materials. The model shows that silicon introduces voltage hysteresis to silicon/graphite electrodes and consequently a “plateau shift” during delithiation of the electrodes. There will also be competition between the silicon and graphite lithiation reactions depending on silicon/graphite ratio. A dimensionless competing factor is derived to quantify the competition between the two active materials. This is demonstrated to be a useful indicator for active operating regions for each material and we demonstrate how it can be used to design cycling protocols for mitigating electrode degradation. The multi-material electrode model can be readily implemented into full-cell models and coupled with other physics to guide further development of lithium ion batteries with silicon-based electrodes.
Pang M-C, Marinescu M, Wang H, et al., 2021, Mechanical behaviour of inorganic solid-state batteries: can we model the ionic mobility in the electrolyte with Nernst-Einstein's relation?, PHYSICAL CHEMISTRY CHEMICAL PHYSICS, Vol: 23, Pages: 27159-27170, ISSN: 1463-9076
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Pan Y, Wang H, Brandon N, 2021, Gas diffusion layer degradation in proton exchange membrane fuel cells: Mechanisms, characterization techniques and modelling approaches, Journal of Power Sources, Vol: 513, ISSN: 0378-7753
Proton exchange membrane fuel cells (PEMFCs) have been considered as a promising power source for electric vehicles. However, the widespread use of PEMFCs requires a significant improvement in durability. As a key component of PEMFCs, gas diffusion layer (GDL) does not only provide a mechanical support for other fuel cell components, but also governs the mass, heat, and electron transport that directly affect cell performance. In this paper, the latest research progress of GDL durability is reviewed from three aspects: degradation mechanisms, experimental methods, and modelling approaches. The six degradation modes of GDLs, namely chemical oxidation, electrochemical carbon corrosion, freezing/thawing, mechanical degradation, material dissolution and erosion by gas flow are discussed under different cell operating conditions. Experimental techniques, including the long-term and accelerated stress tests (AST) and methods for measuring property deterioration are then introduced. Several AST protocols have been developed to decouple the above degradation modes, but few have tried to relate these tests with GDL degradation in practice. Modelling approaches relating to GDL degradation are also covered. Although various types of models have been developed for multiple purposes, a complete model from the mechanistic level to the cell performance is still missing.
Pan W, Zhao Y, Mao J, et al., 2021, High‐energy SWCNT cathode for aqueous Al‐Ion battery boosted by multi‐ion intercalation chemistry, Advanced Energy Materials, Vol: 11, Pages: 1-13, ISSN: 1614-6832
The aqueous Al-ion battery has achieved great progress in recent years. It now shows comparable performance to that of even non-aqueous Al-ion batteries. However, it also shows relatively low energy output and there is limited general understanding of the mechanism behind this restriction to its practical application. Thus, the development of a high-performance cathode material is in great demand. Herein, a high-capacity single-walled carbon nanotube (SWCNT) is developed as a cathode for the water-in-salt electrolyte-based aqueous Al-ion battery, which provides an ultra-high specific capacity of 790 mAh g–1 (based on the mass of SWCNT) at a high current density of 5 A g–1 even after 1000 cycles. Moreover, the SWCNT/Al battery shows a complicated multi-ion intercalation mechanism, where AlCl4–, Cl–, Al3+, and H+ can function at the same time, improving the battery output. Beyond recently revealed H+ and metal ion co-intercalation, the Cl-assisted intercalation of Al3+ ions mechanism is also studied by experimental characterization and modeling for the first time, which significantly boosts the Al3+ storage capacity. This multi-ion intercalation mechanism combines the high-voltage anion deintercalation and the high-capacity cation intercalation, and thus, benefits the development and application of high-energy Al-ion batteries in the future.
Xie W, He R, Gao X, et al., 2021, Degradation identification of LiNi<sub>0.8</sub>Co<sub>0.1</sub>Mn<sub>0.1</sub>O<sub>2</sub>/graphite lithium-ion batteries under fast charging conditions, ELECTROCHIMICA ACTA, Vol: 392, ISSN: 0013-4686
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- Citations: 23
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