Publications
105 results found
Ruan H, Kirkaldy N, Offer G, et al., 2024, Diagnosing health in composite battery electrodes with explainable deep learning and partial charging data, Energy and AI, Vol: 16, ISSN: 2666-5468
Lithium-ion batteries with composite anodes of graphite and silicon are increasingly being used. However, their degradation pathways are complicated due to the blended nature of the electrodes, with graphite and silicon degrading at different rates. Here, we develop a deep learning health diagnostic framework to rapidly quantify and separate the different degradation rates of graphite and silicon in composite anodes using partial charging data. The convolutional neural network (CNN), trained with synthetic data, uses experimental partial charging data to diagnose electrode-level health of tested batteries, with errors of less than 3.1% (corresponding to the loss of active material reaching ∼75%). Sensitivity analysis of the capacity-voltage curve under different degradation modes is performed to provide a physically informed voltage window for diagnostics with partial charging data. By using the gradient-weighted class activation mapping approach, we provide explainable insights into how these CNNs work; highlighting regions of the voltage-curve to which they are most sensitive. Robustness is validated by introducing noise to the data, with no significant negative impact on the diagnostic accuracy for noise levels below 10 mV, thus highlighting the potential for deep learning approaches in the diagnostics of lithium-ion battery performance under real-world conditions. The framework presented here can be generalised to other cell formats and chemistries, providing robust and explainable battery diagnostics for both conventional single material electrodes, but also the more challenging composite electrodes.
Tu Y, Wu B, Ai W, et al., 2024, Influence of concentration-dependent material properties on the fracture and debonding of electrode particles with core–shell structure, Journal of Power Sources, Vol: 603, Pages: 234395-234395, ISSN: 0378-7753
Kallitsis E, Lindsay JJ, Chordia M, et al., 2024, Think global act local: The dependency of global lithium-ion battery emissions on production location and material sources, Journal of Cleaner Production, Vol: 449, ISSN: 0959-6526
The pursuit of low-carbon transport has significantly increased demand for lithium-ion batteries. However, the rapid increase in battery manufacturing, without adequate consideration of the carbon emissions associated with their production and material demands, poses the threat of shifting the bulk of emissions upstream. In this article, a life cycle assessment (LCA) model is developed to account for the cradle-to-gate carbon footprint of lithium-ion batteries across 26 Chinese provinces, 20 North American locations and 19 countries in Europe and Asia. Analysis of published LCA data reveals significant uncertainty associated with the carbon emissions of key battery materials; their overall contribution to the carbon footprint of a LIB varies by a factor of ca. 4 depending on production route and source. The links between production location and the gate-to-gate carbon footprint of battery manufacturing are explored, with predicted median values ranging between 0.1 and 69.5 kg CO2-eq kWh−1. Leading western-world battery manufacturing locations in the US and Europe, such as Kentucky and Poland are found to have comparable carbon emissions to Chinese rivals, even exceeding the carbon emissions of battery manufacturing in several Chinese provinces. Such resolution on material and energy contributions to the carbon footprint of LIBs is essential to inform policy- and decision-making to minimise the carbon emissions of the battery value chain. Given the current status quo, the global carbon footprint of the lithium-ion battery industry is projected to reach up to 1.0 Gt CO2-eq per year within the next decade. With material supply chain decarbonisation and energy savings in battery manufacturing, a lower estimate of 0.5 Gt CO2-eq per year is possible.
Naylor Marlow M, Chen J, Wu B, 2024, Degradation in parallel-connected lithium-ion battery packs under thermal gradients, Communications Engineering, Vol: 3, ISSN: 2731-3395
Practical lithium-ion battery systems require parallelisation of tens to hundreds of cells, however understanding of how pack-level thermal gradients influence lifetime performance remains a research gap. Here we present an experimental study of surface cooled parallel string battery packs (temperature range 20–45 °C), and identify two main operational modes; convergent degradation with homogeneous temperatures, and (the more detrimental) divergent degradation driven by thermal gradients. We attribute the divergent case to the, often overlooked, cathode impedance growth. This was negatively correlated with temperature and can cause positive feedback where the impedance of cells in parallel diverge over time; increasing heterogeneous current and state-of-charge distributions. These conclusions are supported by current distribution measurements, decoupled impedance measurements and degradation mode analysis. From this, mechanistic explanations are proposed, alongside a publicly available aging dataset, which highlights the critical role of capturing cathode degradation in parallel-connected batteries; a key insight for battery pack developers
Wang Z, Acha S, Bird M, et al., 2024, A total cost of ownership analysis of zero emission powertrain solutions for the heavy goods vehicle sector, Journal of Cleaner Production, Vol: 434, ISSN: 0959-6526
Transport-related activities represented 34% of the total carbon emissions in the UK in 2022 and heavy-duty vehicles (HGVs) accounted for one-fifth of the road transport greenhouse gas (GHG) emissions. Currently, battery electric vehicles (BEVs) and hydrogen fuel cell electric vehicles (FCEVs) are considered as suitable replacements for diesel fleets. However, these technologies continue to face techno-economic barriers, creating uncertainty for fleet operators wanting to transition away from diesel-powered internal combustion engine vehicles (ICEVs). This paper assesses the performance and cost competitiveness of BEV and FCEV powertrain solutions in the hard-to-abate HGV sector. The study evaluates the impact of battery degradation and a carbon tax on the cost of owning the vehicles. An integrated total cost of ownership (TCO) model, which includes these factors for the first time, is developed to study a large retailer's HGV fleet operating in the UK. The modelling framework compares the capital expenditures (CAPEX) and operating expenses (OPEX) of alternative technologies against ICEVs. The TCO of BEVs and FCEVs are 11% to 33% and 37% to 78% higher than ICEVs; respectively. Despite these differences, by adopting a longer lifetime for the vehicle it can effectively narrow the cost gap. Alternatively, cost parity with ICEVs could be achieved if BEV battery cost reduces by 56% or if FCEV fuel cell cost reduces by 60%. Besides, the pivot point for hydrogen price is determined at £2.5 per kg. The findings suggest that BEV is closer to market as its TCO value is becoming competitive, whereas FCEV provides a more viable solution than BEV for long-haul applications due to shorter refuelling time and lower load capacity penalties. Furthermore, degradation of performance in lithium-ion batteries is found to have a minor impact on TCO if battery replacement is not required. However, critical component replacement and warranty can influence commercial viability. Given
Pan Y, Ruan H, Regmi YN, et al., 2023, A Machine Learning Accelerated Hierarchical 3D+1D Model for Proton Exchange Membrane Fuel Cells, ECS Meeting Abstracts, Vol: MA2023-02, Pages: 1706-1706
<jats:p> Physics-based continuum models for proton exchange membrane fuel cells (PEMFCs) are an essential tool for fuel cell design and management. To date, many continuum models, ranging from 1D to 3D, have been developed for PEMFCs. Although computationally efficient, 1D models do not account for heterogeneity in flow fields, which negatively impact their accuracy. In contrast, 2D and 3D models are usually more representative of actual operating conditions but computationally intensive due to the coupled partial differential equations and large number of mesh elements involved. To overcome these issues, a hierarchical approach that combines a 2D/3D description of flow fields, gas diffusion layers (GDLs) and a simplified microporous layer (MPL)/catalyst layer (CL)/membrane sub-model has been proposed in the literature. However, studies based on this method often use a simplified or 0D MPL/CL/membrane sub-model, whose results may deviate from a full 1D description due to the neglected nonlinearity, especially at higher loads.</jats:p> <jats:p>In this study, we present a computationally efficient 3D+1D hierarchical model for PEMFCs accelerated by machine learning. The 3D model, which captures the two-phase flow in the gas channels and GDLs, is coupled with a full 1D description of the MPLs, membrane, CLs, and CL agglomerates by exchanging boundary values and fluxes, as shown in the figure. To avoid the high computing cost increase associated with the full 1D description, we develop a physics-informed neural network to replace the 1D sub-model for coupling with the 3D model, while maintaining the full description of fuel cell internal states. Large synthetic datasets are generated using the 1D model for training the neural network, ensuring the accuracy of the model. The proposed 3D+1D model is validated against experimentally obtained polarization curves and high frequency resistances under different relative humidities. The proposed
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.
Ruan H, Barreras JV, Steinhardt M, et al., 2023, The heating triangle: A quantitative review of self-heating methods for lithium-ion batteries at low temperatures, Journal of Power Sources, Vol: 581, Pages: 1-16, ISSN: 0378-7753
Lithium-ion batteries at low temperatures have slow recharge times alongside reduced available power and energy. Battery heating is a viable way to address this issue, and self-heating techniques are appealing due to acceptable efficiency and speed. However, there are a lack of studies quantitatively comparing self-heating methods rather than qualitatively, because of the existence of many different batteries with varied heating parameters. In this work, we review the current state-of-the-art self-heating methods and propose the heating triangle as a new quantitative indicator for comparing self-heating methods, towards identifying/developing effective heating approaches. We define the heating triangle which considers three fundamental metrics: the specific heating rate (°C·g·J−1), coefficient of performance (COP) (−), and specific temperature difference (°C·hr), enabling a quantitative assessment of self-heating methods using data reported in the literature. Our analysis demonstrates that very similar metrics are observed for the same type of self-heating method, irrespective of the study case, supporting the universality of the proposed indicator. With the comparison insights, we identify research gaps and new avenues for developing advanced self-heating methods. This work demonstrates the value of the proposed heating triangle as a standardised approach to compare heating methods and drive innovation.
Wu B, Ai W, Kirkaldy N, et al., 2023, (Invited) Multi-Scale Battery Modelling: Understanding Coupled Electrochemical and Mechanical Effects, ECS Meeting Abstracts, Vol: MA2023-01, Pages: 1634-1634
<jats:p> Lithium-ion batteries are a key enabler for a low-carbon future, however their performance and lifetime are influenced by complex and coupled electrochemical, thermal and mechanical factors across different length and time scales. In this talk, we explore the key degradation modes which limit battery lifetime and how multi-scale models can help describe these effects towards improved cell designs and device control. At the particle level, we explore how phase field fatigue models of cathode particles can be used to understand crack growth, leading to loss of active material [1]. Here, non-linear crack growth is observed due to the fatigue of material properties and crack merger, leading to a transition from slow to rapid crack growth rate. Use of these models can identify critical C-rates and particle sizes which mitigate cracking.</jats:p> <jats:p>At the continuum scale, we then investigate how these stresses evolve during cycling, with stress heterogeneities arising initially at the electrode-separator interface, but later propagating to the electrode-current collector interface [2]. We then extend this study to composite anodes of graphite and silicon, where highly non-linear behaviour is observed [3]. Here, the graphite phase provides the majority of the reaction current density at high state-of-charge operation, with this then shifting to the silicon phase at low state-of-charge operation, with hysteresis effects observed due to the silicon. This behaviour is attributed mostly to the different open circuit potentials of the two different phases. Finally, we explore how these effects propagate to the battery pack scale and highlight the divergence of material level performance from their real-world implementation [4].</jats:p> <jats:p>References</jats:p> <jats:p>[1] A coupled phase field formulation for modelling fatigue cracking in lithium-ion battery electrode partic
Dubarry M, Howey D, Wu B, 2023, Enabling battery digital twins at the industrial scale, Joule, Vol: 7, Pages: 1134-1144, ISSN: 2542-4351
Digital twins are cyber-physical systems that fuse real-time sensor data with models to make accurate, asset-specific predictions and optimal decisions. For batteries, this concept has been applied across length scales, from materials to systems. However, a holistic approach with a strong conceptual and mathematical framework is needed for battery digital twins to achieve their full potential at the industrial scale. Developing a standardized and transparent approach for data sharing between stakeholders that respects confidentiality is essential. Industrial battery digital twins also need principled methods to quantify and propagate uncertainty from sensors and models to predictions. Ensuring retention of physical understanding is important for the identification of “stiff” parameters, which require careful measurement. Combined with uncertainty analysis, this can unlock optimal data-driven sensor selection and placement and improved root-cause analysis. However, better physical modeling and sensing approaches for battery manufacturing and thermal runaway are needed. Furthermore, immutability of data is also necessary for industrial uptake, with digital ledger technology providing new avenues of research. We believe that digital twins could be transformative for the current lithium-ion battery technologies and also as an enabler for emerging new battery technologies, optimizing lifetime and value through asset-specific control.
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, Pages: 1-17, ISSN: 0378-7753
Extending lithium-ion battery lifetime is essential for mainstream uptake of electric vehicles. However, battery degradation is complex and involves coupling of underpinning electrochemical, thermal and mechanical processes, with behaviours varying based on chemistry, operating conditions and design. Derating is an attractive approach for extending lifetime due to ease of implementation, however, uncertainties remain around the optimal approach and their impacts. In this paper, we present a critical review of derating methods; dividing approaches into dynamic or static approaches based on whether the derated parameters changed with battery aging or not. Furthermore, we analyse and comment on approaches which are classified as being either heuristic or model-based. Analysis, comparison, and discussion around the derating sub-categories are presented towards highlighting underpinning insights of derating. Benefits and impacts of derating are quantified, and challenges with implementation are identified along with identification of research gaps, practical considerations and perspectives for future directions.
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.
Planella FB, Ai W, Boyce AM, et al., 2022, A continuum of physics-based lithium-ion battery models reviewed, PROGRESS IN ENERGY, Vol: 4
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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.
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.
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.
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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