Project description:Photovoltaics supply a growing share of power to the electric grid worldwide. To mitigate resource intermittency issues, these systems are increasingly being paired with electrochemical energy storage devices, e.g., Li-ion batteries, for which ensuring long and safe operation is critical. However, in this operation framework, secondary Li-ion batteries undergo sporadic usage, which prevents the application of standard diagnostic methods. Here, we propose a diagnostic methodology that uses machine learning algorithms trained directly on data obtained from photovoltaic charging of Li-ion batteries. The training is carried out on synthetic voltage data at various degradation conditions calculated from clear sky model irradiance data. The method is validated using synthetic voltage responses calculated from plane of array irradiance observations for a photovoltaic system located in Maui, HI, USA. We report an average root mean square error of 2.75% obtained for more than 10,000 different degradation paths with 25% or less degradation on the Li-ion cells.
Project description:While little success has been obtained over the past few years in attempts to increase the capacity of Li-ion batteries, significant improvement in the power density has been achieved, opening the route to new applications, from hybrid electric vehicles to high-power electronics and regulation of the intermittency problem of electric energy supply on smart grids. This success has been achieved not only by decreasing the size of the active particles of the electrodes to few tens of nanometers, but also by surface modification and the synthesis of new multi-composite particles. It is the aim of this work to review the different approaches that have been successful to obtain Li-ion batteries with improved high-rate performance and to discuss how these results prefigure further improvement in the near future.
Project description:Li-ion battery mishaps are primarily attributed to short circuits, which missed early detection. In this study, a method is introduced to address this issue by analyzing the voltage relaxation, after initiating a rest period. The voltage equilibration arising from solid-concentration profile relaxation is expressed by a double-exponential model, whose time constants, τ1 & τ2, capture the initial, rapid exponential contour and the long-term relaxation, respectively. By tracking τ2, which is very sensitive to small leakage currents, it is possible to detect a short early on and estimate the short resistance. This method, validated with experiments on commercial batteries induced with short circuits of varying extents, has >90% prediction accuracy and enables clear differentiation between different short severities, while factoring in the influence of temperature, state of charge (SOC), state of health (SOH), and idle currents. The method is applicable across different battery chemistries and form factors, offering precise and robust nascent-stage short detection-estimation for on-device implementation.
Project description:For the intricate and infrequent safety issues of batteries, online safety fault diagnosis over stochastic working conditions is indispensable. In this work, we employ deep learning methods to develop an online fault diagnosis network for lithium-ion batteries operating under unpredictable conditions. The network integrates battery model constraints and employs a framework designed to manage the evolution of stochastic systems, thereby enabling fault real-time determination. We evaluate the performance using a dataset of 18.2 million valid entries from 515 vehicles. The results demonstrate our proposed algorithm outperforms other relevant approaches, enhancing the true positive rate by over 46.5% within a false positive rate range of 0 to 0.2. Meanwhile, we identify the trigger probability for four safety fault samples, namely, electrolyte leakage, thermal runaway, internal short circuit, and excessive aging. The proposed network is adaptable to packs of varying structures, thereby reducing the cost of implementation. Our work explores the application of deep learning for real-state prediction and diagnosis of batteries, demonstrating potential improvements in battery safety and economic benefits.
Project description:The pulverization of lithium metal electrodes during cycling recently has been suppressed through various techniques, but the issue of irreversible consumption of the electrolyte remains a critical challenge, hindering the progress of energy-dense lithium metal batteries. Here, we design a single-ion-conductor-based composite layer on the lithium metal electrode, which significantly reduces the liquid electrolyte loss via adjusting the solvation environment of moving Li+ in the layer. A Li||Ni0.5Mn0.3Co0.2O2 pouch cell with a thin lithium metal (N/P of 2.15), high loading cathode (21.5 mg cm-2), and carbonate electrolyte achieves 400 cycles at the electrolyte to capacity ratio of 2.15 g Ah-1 (2.44 g Ah-1 including mass of composite layer) or 100 cycles at 1.28 g Ah-1 (1.57 g Ah-1 including mass of composite layer) under a stack pressure of 280 kPa (0.2 C charge with a constant voltage charge at 4.3 V to 0.05 C and 1.0 C discharge within a voltage window of 4.3 V to 3.0 V). The rational design of the single-ion-conductor-based composite layer demonstrated in this work provides a way forward for constructing energy-dense rechargeable lithium metal batteries with minimal electrolyte content.
Project description:The exfoliation of tridimensional crystal structures has recently been considered a new source of bidimensional materials. The new approach offers the possibility of dramatically enlarging the library of bidimensional materials, but the number of nanolayers produced so far is still limited. Here, we report for the first time the use of a new type of material, α-germanium nanolayers (2D α-Ge). The 2D α-Ge is obtained by exfoliating crystals of α-germanium in a simple one-step procedure assisted by wet ball-milling (gram-scale fabrication). The α-germanium nanolayers have been tested as anode material for high-performance LIBs. The results show excellent performance in semi-cell configuration with a high specific capacity of 1630 mAh g-1 for mass loading of 1 mg cm-2 at 0.1 C. The semi-cell was characterized by a constant current rate of 0.5 C during 400 cycles and different scan rates (0.1 C, 0.5 C, and 1 C). Interestingly, the structural characterization, including Raman spectroscopy, XRPD, and XPS, concludes that 2D α-Ge largely retains its crystallinity after continuous cycling. These results can be used to potentially apply these novel 2D germanium nanolayers to high-performance Li-ion batteries.
Project description:Highly ordered mesoporous Co3O4 materials have been prepared via a nanocasting route with three-dimensional KIT-6 and two-dimensional SBA-15 ordered mesoporous silicas as templates and Co(NO3)2 · 6H2O as precursor. Through changing the hydrothermal treating temperature of the silica template, ordered mesoporous Co3O4 materials with hierarchical structures have been developed. The larger pores around 10 nm provide an efficient transport for Li ions, while the smaller pores between 3-5 nm offer large electrochemically active areas. Electrochemical impedance analysis proves that the hierarchical structure contributes to a lower charge transfer resistance in the mesoporous Co3O4 electrode than the mono-sized structure. High reversible capacities around 1141 mAh g(-1) of the hierarchically mesoporous Co3O4 materials are obtained, implying their potential applications for high performance Li-ion batteries.
Project description:Here we explore the electrochemical performance of pyrolyzed skins from the species A. bisporus, also known as the Portobello mushroom, as free-standing, binder-free, and current collector-free Li-ion battery anodes. At temperatures above 900 °C, the biomass-derived carbon nanoribbon-like architectures undergo unique processes to become hierarchically porous. During heat-treatment, the oxygen and heteroatom-rich organics and potassium compounds naturally present in the mushroom skins play a mutual role in creating inner void spaces throughout the resulting carbon nanoribbons, which is a process analogous to KOH-activation of carbon materials seen in literature. The pores formed in the pyrolytic carbon nanoribbons range in size from sub-nanometer to tens of nanometers, making the nanoribbons micro, meso, and macroporous. Detailed studies were conducted on the carbon nanoribbons using SEM and TEM to study morphology, as well as XRD and EDS to study composition. The self-supporting nanoribbon anodes demonstrate significant capacity increase as they undergo additional charge/discharge cycles. After a pyrolysis temperature of 1100 °C, the pristine anodes achieve over 260 mAh/g after 700 cycles and a Coulombic efficiency of 101.1%, without the use of harmful solvents or chemical activation agents.
Project description:The industrial lignin used here is a byproduct from Kraft pulp mills, extracted from black liquor. Since lignin is inexpensive, abundant and renewable, its utilization has attracted more and more attention. In this work, lignin was used for the first time as binder material for LiFePO₄ positive and graphite negative electrodes in Li-ion batteries. A procedure for pretreatment of lignin, where low-molecular fractions were removed by leaching, was necessary to obtain good battery performance. The lignin was analyzed for molecular mass distribution and thermal behavior prior to and after the pretreatment. Electrodes containing active material, conductive particles and lignin were cast on metal foils, acting as current collectors and characterized using scanning electron microscopy (SEM), electrochemical impedance spectroscopy (EIS) and galvanostatic charge-discharge cycles. Good reversible capacities were obtained, 148 mAh·g-1 for the positive electrode and 305 mAh·g-1 for the negative electrode. Fairly good rate capabilities were found for both the positive electrode with 117 mAh·g-1 and the negative electrode with 160 mAh·g-1 at 1C. Low ohmic resistance also indicated good binder functionality. The results show that lignin is a promising candidate as binder material for electrodes in eco-friendly Li-ion batteries.
Project description:Elevating the charging cut-off voltage is one of the efficient approaches to boost the energy density of Li-ion batteries (LIBs). However, this method is limited by the occurrence of severe parasitic reactions at the electrolyte/electrode interfaces. Herein, to address this issue, we design a non-flammable fluorinated sulfonate electrolyte by multifunctional solvent molecule design, which enables the formation of an inorganic-rich cathode electrolyte interphase (CEI) on high-voltage cathodes and a hybrid organic/inorganic solid electrolyte interphase (SEI) on the graphite anode. The electrolyte, consisting of 1.9 M LiFSI in a 1:2 v/v mixture of 2,2,2-trifluoroethyl trifluoromethanesulfonate and 2,2,2-trifluoroethyl methanesulfonate, endows 4.55 V-charged graphite||LiCoO2 and 4.6 V-charged graphite||NCM811 batteries with capacity retentions of 89% over 5329 cycles and 85% over 2002 cycles, respectively, thus resulting in energy density increases of 33% and 16% compared to those charged to 4.3 V. This work demonstrates a practical strategy for upgrading the commercial LIBs.