Analysis Of Degradation Modes Research Articles

Non-destructive electrode potential and open-circuit voltage aging estimation for lithium-ion batteries

In this publication we extend a state-of-the-art electrode open circuit potential model for blend electrodes and inhomogeneous lithiation. We introduce a bi-level optimization algorithm to estimate the open parameters of the electrode model using measurements conducted on the full-cell level with state-of-the-art testing equipment. As input for the optimization algorithm, we use data from a pseudo open-circuit voltage aging study to estimate the electrode potential curves, the electrode capacities, and the capacity of cyclable lithium over lifetime without opening the cell. We validate our method against measurements on commercial lithium-ion cells with the electrode materials LFP-C, NMC-SiC and NCA-SiC, and compare the estimated electrode pseudo open-circuit potential curves to measurements conducted on harvested half-cells. We conclude that the presented non-destructive method of half-cell open-circuit potential modeling offers an alternative to complex cell disassembly and electrode material harvesting for degradation mode analysis.

Degradation in parallel-connected lithium-ion battery packs under thermal gradients

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.

Active polysaccharides from Lentinula edodes and Pleurotus ostreatus by addition of corn straw and xylosma sawdust through solid-state fermentation

In this study, Lentinula edodes and Pleurotus ostreatus were selected as representatives to explore the effects of corn straw and xylosma sawdust on the production and activity of polysaccharides by edible fungi during solid-state fermentation. The results clearly indicated that the addition of lignocellulose could promote the polysaccharide content compared to the control group. Meanwhile, the hydroxyl radical scavenging activity of polysaccharides reached the maximum when the glucose concentration was 1.5 %, and among them, the xylosma sawdust groups for two fungi (L. edodes-32.37 %, P. ostreatus-25.86 %) both performed better than corn straw groups (L. edodes-24.96 %, P. ostreatus-20.80 %). In addition, structural characterization and degradation mode analysis were carried out. The results showed that the structure of the xylosma had a stronger destruction than corn straw. The activities of lignocellulolytic enzymes such as carboxymethyl cellulase, filter paper enzyme, β-glucosidase and xylanase increased rapidly in the early stage of fermentation, which could degrade the cellulose and hemicellulose as raw materials for the synthesis of active polysaccharides.

Battery degradation diagnosis with field data, impedance-based modeling and artificial intelligence

• Electrode-level degradation diagnosis approach for lithium-ion batteries • Hybrid model integrates the battery impedance and OCV-reconstruction • Accurate parameter identification with artificial intelligence and field data • Accurate estimation of capacity fade, power fade, and degradation modes • Robustness analysis and data requirements analysis for cloud applications By collecting battery data from the field and building up the battery digital twin in the cloud, the degradation of batteries can be monitored online on the electrode level and the information regarding the degradation modes can be extracted from the data. Here, we present a degradation diagnosis framework for lithium-ion batteries by integrating field data, impedance-based modeling, and artificial intelligence, revolutionizing the degradation identification with accurate and robust estimation of both capacity and power fade together with degradation mode analysis. By integrating an impedance-based model and an open-circuit voltage reconstruction model, the hybrid model consists of parameters representing the change of impedance in a wide frequency domain and the change of open-circuit voltage during degradation. Based on the field data with low and high dynamics, the data-driven parameter identification method using a multi-step cuckoo search algorithm considering parameter sensitivity differences shows high accuracy and robustness in aging parameter estimation and degradation mode identification even under sensor noise. Furthermore, the data requirement for the battery digital twin in the sense of sampling rate was investigated considering degradation identification accuracy, computational cost, and data storage cost. This work highlights the opportunity in online electrode-level degradation diagnosis in the field through battery modeling and artificial intelligence.

Lithium-Ion Battery Degradation: Measuring Rapid Loss of Active Silicon in Silicon-Graphite Composite Electrodes.

To increase the specific energy of commercial lithium-ion batteries, silicon is often blended into the graphite negative electrode. However, due to large volumetric expansion of silicon upon lithiation, these silicon-graphite (Si-Gr) composites are prone to faster rates of degradation than conventional graphite electrodes. Understanding the effect of this difference is key to controlling degradation and improving cell lifetimes. Here, the effects of state-of-charge and temperature on the aging of a commercial cylindrical cell with a Si-Gr electrode (LG M50T) are investigated. The use of degradation mode analysis enables quantification of separate rates of degradation for silicon and graphite and requires only simple in situ electrochemical data, removing the need for destructive cell teardown analyses. Loss of active silicon is shown to be worse than graphite under all operating conditions, especially at low state-of-charge and high temperature. Cycling the cell over 0-30% state-of-charge at 40 °C resulted in an 80% loss in silicon capacity after 4 kA h of charge throughput (∼400 equiv full cycles) compared to just a 10% loss in graphite capacity. The results indicate that the additional capacity conferred by silicon comes at the expense of reduced lifetime. Conversely, reducing the utilization of silicon by limiting the depth-of-discharge of cells containing Si-Gr will extend their lifetime. The degradation mode analysis methods described here provide valuable insight into the causes of cell aging by separately quantifying capacity loss for the two active materials in the composite electrode. These methods provide a suitable framework for any experimental investigations involving composite electrodes.

Three Cases of Gradual Degradation Mode Analysis of Semiconductor Laser Diodes

Semiconductor laser diodes are important components for various applications such as 5G wireless, datacenter, passive optical network, and aerospace applications. High reliability has emerged to be the universal requirement for all optical applications. To achieve high reliability, fundamental understanding of the laser degradation behavior is crucial. In this paper, we study three cases of gradual degradataion modes of laser diodes including (1) Pattern-A that is associated with threshold current change only, (2) Pattern-B that involve both threshold current and power changes, and (3) Pattern-C that is associated with merely power change. We have instituted reliability equations for the degradation processes. The new reliability models could provide estimation on the laser end-of-life based on the degradation rate and device performance specification.

(Invited) Metal-Supported Solid Oxide Fuel Cells and Electrolyzers for Low-Cost, Robust, Rapid-Start Systems

This talk will provide an overview of performance, durability, and applications of metal-supported solid oxide fuel cell and electrolysis cell technology developed at Lawrence Berkeley National Laboratory (LBNL). The unique LBNL symmetric cell architecture design, with thin zirconia ceramic backbones and electrolyte sandwiched between porous metal supports, offers a number of advantages over conventional all-ceramic cells, including low-cost structural materials (e.g. stainless steel), mechanical ruggedness, excellent tolerance to redox cycling, and extremely fast start-up capability.MS-SOFC performance with a variety of fuels will be presented, including hydrogen, ethanol, natural gas, and simulated reformates. The impact of the presence of carbon and internal reforming catalysts on the durability of the cells will be examined. In particular, oxidation of the stainless steel supports in the presence of carbon is analyzed. Scale-up from button cells to 50cm2 will be presented.The case for use of MS-SOECs for dynamic electrolysis operation will be made. Detailed analysis of degradation modes over 1000h of operation with 50/50 steam/H2 informed efforts to improve durability through the use of coatings and catalyst processing techniques.In addition to our long-standing development of zirconia-based metal-supported cells, recent efforts on metal-supported proton-conducting cells are ongoing. Co-sintering of 400-series stainless steel and BZCY-type proton conductors presents a number of challenges, including: Ba evaporation in reducing atmosphere, over-densification of steel at the high temperatures (>1400°C) required for BZCY processing, mismatch of metal and ceramic sintering behavior, and migration of Cr and Si from the steel support into the BZCY layers. Feasibility of new approaches to overcome these challenges will be discussed.

Quantitative Analysis of Degradation Modes of Lithium-Ion Battery under Different Operating Conditions

The degradation mode is of great significance for reducing the complexity of research on the aging mechanisms of lithium-ion batteries. Previous studies have grouped the aging mechanisms into three degradation modes: conductivity loss (CL), loss of lithium inventory (LLI) and loss of active material (LAM). Combined with electrochemical impedance spectroscopy (EIS), degradation modes can be identified and quantified non-destructively. This paper aims to extend the application of this method to more operating conditions and explore the impact of external factors on the quantitative results. Here, we design a quantification method using two equivalent circuit models to cope with the different trends of impedance spectra during the aging process. Under four conditions, the changing trends of the quantitative values of the three degradation modes are explored and the effects of the state of charge (SoC) and excitation current during EIS measurement are statistically analyzed. It is verified by experiments that LLI and LAM are the most critical aging mechanisms under various conditions. The selection of SoC has a significant effect on the quantitative results, but the influence of the excitation current is not obvious.

Multiple degradation mode analysis via gated recurrent unit mode recognizer and life predictors for complex equipment

In order to ensure the reliability and safety of industrial complex equipment, it is necessary to predict and manage the health status of the equipment. Remaining useful life (RUL) prediction is the decision basis of condition-based maintenance (CBM) and one of the main tasks in prognostics and health management (PHM). Complex systems tend to have multiple degradation modes, while similar degradation features may have significantly different RUL labels in different degradation modes, which can be called feature multi-label problem. To solve the problem, a novel RUL prediction method was proposed, which first analyzed the degradation mode and then utilized the predictor for RUL prediction under the specific mode. In particular, a modified de-noising auto-encoder (DAE) was proposed for nonlinear feature extraction and noise reduction. Mode recognizer and life predictors based on gated recurrent unit (GRU) and fuzzy k-means were proposed as the core modules. Case studies of commercial modular aero-propulsion system simulation data and the life cycle data of bearing were conducted to verify the effectiveness of the proposed method. Results show that the proposed method achieved much higher prediction accuracy than other methods.

Long term durability test and post mortem for metal-supported solid oxide electrolysis cells

Hydrogen is a renewable energy carrier, and electrolysis to split water is the most environmentally friendly method to produce hydrogen. This work reports long-term durability and degradation mode analysis for metal-supported solid oxide electrolysis cells (MS-SOECs). Catalyst screening showed that MS-SOECs with composite electrode catalysts (samarium-doped ceria-nickel [SDC-Ni] serving as a fuel electrode catalyst, and praseodymium oxide [PrOx]-SDC or La0.6Sr0.4Co0.2Fe0.8O3 [LSCF]-SDC serving as an air electrode catalyst) exhibit the highest electrochemical performance at 700 °C. The degradation rate of cells with LSCF-SDC as the air electrode catalyst was as low as 1.3%/100 h in long term durability tests at a current density of 0.33 A cm−2, in contrast to rapid degradation observed for a cell with a PrOx-SDC air electrode. Post-mortem analysis reveals the degradation is dependent on the primary modes of fuel electrode catalyst coarsening and Cr poisoning on the air electrode catalyst, as well as secondary modes of oxidation of the metal support and local elemental accumulation of Ni. Other degradation modes reported in conventional anode-supported SOECs, such as Ni migration, foreign element contamination, delamination of the cell, and nano-voids on the electrolyte, are not observed in the present MS-SOECs.

Synthetic Vs. Real Driving Cycles: A Comparison of EV Battery Degradation

Automobile dependency and the inexorable proliferation of electric vehicles (EV) compels accurate predictions of cycle life across multiple usage conditions and for multiple lithium-ion battery systems. Synthetic driving cycles have been essential in accumulating data on EV battery lifetimes. However, since battery deterioration is path-dependent, the representability of synthetic cycles must be questioned. Hence, this work compared three different synthetic driving cycles to real-driving data in terms of mimicking actual EV battery degradation. It was found that the average current and charge capacity during discharge were important parameters in determining the appropriate synthetic profile and traffic conditions have a significant impact on cell lifetimes. In addition, a stage of accelerated capacity fade was observed and shown to be induced by an increased loss of lithium inventory (LLI) resulting from irreversible Li plating. New metrics, the ratio of the loss of active material at the negative electrode (LAMNE) to the LLI and the plating threshold, were proposed as possible predictors for a stage of accelerated degradation. The results presented here demonstrate tracking properties such as capacity loss and resistance increase were insufficient in predicting cell lifetimes and support the adoption of metrics based on the analysis of degradation modes.

Synthetic vs. Real Driving Cycles: A Comparison of Electric Vehicle Battery Degradation

Automobile dependency and the inexorable proliferation of electric vehicles (EVs) compels accurate predictions of cycle life across multiple usage conditions and for multiple lithium-ion battery systems. Synthetic driving cycles have been essential in accumulating data on EV battery lifetimes. However, since battery deterioration is path-dependent, the representability of synthetic cycles must be questioned. Hence, this work compared three different synthetic driving cycles to real driving data in terms of mimicking actual EV battery degradation. It was found that the average current and charge capacity during discharge were important parameters in determining the appropriate synthetic profile, and traffic conditions have a significant impact on cell lifetimes. In addition, a stage of accelerated capacity fade was observed and shown to be induced by an increased loss of lithium inventory (LLI) resulting from irreversible Li plating. New metrics, the ratio of the loss of active material at the negative electrode (LAMNE) to the LLI and the plating threshold, were proposed as possible predictors for a stage of accelerated degradation. The results presented here demonstrated tracking properties, such as capacity loss and resistance increase, were insufficient in predicting cell lifetimes, supporting the adoption of metrics based on the analysis of degradation modes.

Effective degradation of curdlan powder by a novel endo-β-1→3-glucanase

Curdlan is a water-insoluble microbial exo-polysaccharide that is hardly degraded. The gene CcGluE encoding an endo-β-1→3-glucanase consisting of 412 amino acids (44 kDa) from Cellulosimicrobium cellulans E4-5 was cloned and expressed in Escherichia coli. The recombinant CcGluE hydrolysed curdlan powder effectively. CcGluE shows high endo-β-1→3 glucanase activity and low β-1,4 and β-1,6 glucanase activities with broad substrate specificity for glucan, including curdlan, laminarin and β-1→3/1→6-glucan, and the highest catalytic activity for curdlan. Moreover, the hydrolytic products of curdlan were glucan oligosaccharides with degrees of polymerisation of 2–13, and the main products were glucobiose and glucotriose. Degradation mode analysis indicated that CcGluE is more likely to hydrolyse glucopentaose and revealed that CcGluE was an endo-glucanase. Furthermore, upon combination with a homogenising pre-treatment method with curdlan, the degradation efficiency of CcGluE for curdlan powder was greatly improved 7.1-fold, which laid a good foundation for the utilisation of curdlan.

Degradation Mode Analysis on Highly Reliable Guardring-Free Planar InAlAs Avalanche Photodiodes

This paper presents the high reliability of the guardring-free planar InAlAs avalanche photodiode (APD) and its degradation mode analysis. We have conducted long-term and high-temperature aging tests under 175degC, 200degC, 225degC, and 250degC. There were three degradation modes as follows: 1) the increase in the dark current; 2) the decrease in the breakdown voltage; and 3) short circuit. Their thermal activation energies were 0.96, 1.30, and 0.93 eV, respectively. Their estimated mean times to failures at 85 are 25, 100, and 22 million hours, respectively. Increased dark current is generated in the upper side of the absorbing layer. The breakdown voltage decreased with aging time because the depletion width in the absorbing layer shrinks from the region side. Any degradation mode on the surfaced p-n junction did not appear in spite of the 10 000-h aging test at a high temperature of 200degC. The guardring-free planar InAlAs APD has the advantage of high reliability because the electric field of a surfaced p-n junction is weakened by the underlying field control layer.