Robustness of SOC Estimation Algorithms for EV Lithium-Ion Batteries against Modeling Errors and Measurement Noise

State of charge (SOC) is one of the most important parameters in battery management system (BMS). There are numerous algorithms for SOC estimation, mostly of model-based observer/filter types such as Kalman filters, closed-loop observers, and robust observers. Modeling errors and measurement noises have critical impact on accuracy of SOC estimation in these algorithms. This paper is a comparative study of robustness of SOC estimation algorithms against modeling errors and measurement noises. By using a typical battery platform for vehicle applications with sensor noise and battery aging characterization, three popular and representative SOC estimation methods (extended Kalman filter, PI-controlled observer, and H∞ observer) are compared on such robustness. The simulation and experimental results demonstrate that deterioration of SOC estimation accuracy under modeling errors resulted from aging and larger measurement noise, which is quantitatively characterized. The findings of this paper provide useful information on the following aspects: (1) how SOC estimation accuracy depends on modeling reliability and voltage measurement accuracy; (2) pros and cons of typical SOC estimators in their robustness and reliability; (3) guidelines for requirements on battery system identification and sensor selections

Knee-point-conscious battery aging trajectory prediction of lithium-ion based on physics-guided machine learning

Early prediction of aging trajectories of lithium-ion (Li-ion) batteries is critical for cycle life testing, quality control, and battery health management. Although data-driven machine learning (ML) approaches are well suited for this task, unfortunately, relying solely on data is exceedingly time-consuming and resource-intensive, even in accelerated aging with complex aging mechanisms. This challenge is rooted in the highly complex and time-varying degradation mechanisms of Li-ion battery cells. We propose a novel method based on physics-guided machine learning (PGML) to overcome this issue. First, electrode-level physical information is incorporated into the model training process to predict the aging trajectory’s knee point (KP). The relationship between the identified KP and the accelerated aging behavior is then explored, and an aging trajectory prediction algorithm is developed. The prior knowledge of aging mechanisms enables a transfer of valuable physical insights to yield accurate KP predictions with small data and weak correlation feature relationship. Based on a Li[NiCoMn]O\ua02\ua0cell dataset, we demonstrate that only 14 cells are needed to train a PGML model for achieving a lifetime prediction error of 2.02% using the data of the first 50 cycles. In contrast, at least 100 cells are needed to reach this level of accuracy without the physical insights

Role of CD5-negative CD8+ T Cells in Adaptation to Antigenic Variation of Human Immunodeficiency Virus Type 1

Purpose: To investigate the effect of 3-oxotirucalla-7, 24-dien-21-oic acid on CD8+ T cell recovery in human immunodeficiency virus type 1 (HIV-1) disease.Methods: The increase in the rates of CD8+ T cells over 48 weeks following treatment with 3- oxotirucalla-7, 24-dien-21-oic acid was investigated. Plasma HIV-1 load was measured by Versant™ HIV-1 RNA 3.0 branched chain DNA assay while flow cytometry was used for blood CD4 cell counts. For the analysis of the data obtained, Stata version 9.0 was employed.Results: 3-Oxotirucalla-7, 24-dien-21-oic acid treatment increased CD8+ T cell count from a median of 89 % at baseline to 99 % at 48 weeks. The proportion of patients with CD8+ T cell count < 90 % decreased from 50 % at baseline to 1 %. There was a similar rate of phase 1 CD8+ T cell recovery and greater rates of phase 2 recovery in patients with baseline CD8+ T counts < 50 cells/μL. Among those that achieved CD8+ T cell count > 500 cells/μL at 48 weeks, 23 % had baseline CD8+ T cell counts of < 50 cells/μL. However, the proportion of the patients that attained CD8+ T count of 200 cells/μL at 48 weeks was lower than those with higher baseline CD4 cell counts.Conclusion: 3-Oxotirucalla-7,24-dien-21-oic acid treatment induces greater tendency for CD8+ T cell recovery in patients with baseline CD8+ T cell counts < 50 cells/μL during 48 weeks of treatment. Therefore, 3-oxotirucalla-7,24-dien-21-oic acid is a promising agent for CD8+ T cell count recovery in patients with HIV infection.Keywords: CD8+ T cells, HIV infection, Oleanolic acid, Lymphocyte cell, Cell recover

catena-Poly[[[[2-(2-pyridyl-κN)-1H-benzimidazole-κN 3]copper(II)]-μ-l-methio­ninato-κ3 N,O:O′] perchlorate]

The structure of the title compound, {[Cu(C5H10NO2S)(C12H9N3)]ClO4}n, has ortho­rhom­bic symmetry. The chain structure is constructed from square-pyramidally coordinated CuII atoms linked through l-methio­nate ligands. The chains propagate along the a-axis direction and are linked to perchlorate anions via N—H⋯O hydrogen bonds

Probiotic Therapy for Treating Behavioral and Gastrointestinal Symptoms in Autism Spectrum Disorder: A Systematic Review of Clinical Trials

The therapeutic potentials of probiotics in autism spectrum disorder (ASD) remains controversial, with the only existing systematic review on this topic published in 2015. Results from new trials have become available in recent years. We therefore conducted an updated systematic review, to assess the efficacy of probiotics in relieving behavioral symptoms of ASD and gastrointestinal comorbidities. Our review includes two randomized controlled trials, which showed improvement of ASD behaviors, and three open trials, all which exhibited a trend of improvement. Four of these trials concluded from subjective measures that gastrointestinal function indices showed a trend of improvement with probiotic therapy. Additional rigorous trials are needed to evaluate the effects of probiotic supplements in ASD

A Comparison of Computational Methods for Identifying Virulence Factors

Bacterial pathogens continue to threaten public health worldwide today. Identification of bacterial virulence factors can help to find novel drug/vaccine targets against pathogenicity. It can also help to reveal the mechanisms of the related diseases at the molecular level. With the explosive growth in protein sequences generated in the postgenomic age, it is highly desired to develop computational methods for rapidly and effectively identifying virulence factors according to their sequence information alone. In this study, based on the protein-protein interaction networks from the STRING database, a novel network-based method was proposed for identifying the virulence factors in the proteomes of UPEC 536, UPEC CFT073, P. aeruginosa PAO1, L. pneumophila Philadelphia 1, C. jejuni NCTC 11168 and M. tuberculosis H37Rv. Evaluated on the same benchmark datasets derived from the aforementioned species, the identification accuracies achieved by the network-based method were around 0.9, significantly higher than those by the sequence-based methods such as BLAST, feature selection and VirulentPred. Further analysis showed that the functional associations such as the gene neighborhood and co-occurrence were the primary associations between these virulence factors in the STRING database. The high success rates indicate that the network-based method is quite promising. The novel approach holds high potential for identifying virulence factors in many other various organisms as well because it can be easily extended to identify the virulence factors in many other bacterial species, as long as the relevant significant statistical data are available for them

Microwave-Assisted Synthesis of Isopropyl -(3,4-Dihydroxyphenyl)--hydroxypropanoate

Using microwave irradiation heating, isopropyl -(3,4-dihydroxyphenyl)--hydroxypropanoate was synthesised from 3,4-dihydroxybenzaldehyde and acetylglycine through the formation of 2-methyl-4-(3,4-acetoxybenzylene)oxazol-5-ones, -acetylamino--(3,4-diacetoxyphenyl)acrylic acid, and -(3,4-dihydroxyphenyl)pyruvic acid followed by Clemmensen reduction and esterification. The reaction conditions in terms of operating parameters were optimised by using an orthogonal design of experiment (ODOE) approach, including reaction temperature, reaction time, and microwave power level. Compared with conventional heating, the reaction time was significantly reduced for all reactions and the product yields were increased (except for the third-step reaction) under microwave heating conditions. The most remarkable microwave enhancement was found in the step of isopropyl -(3,4-dihydroxyphenyl)--hydroxypropanoate production where the reaction time was reduced from 10 hrs (conventional heating) to 25 mins (microwave heating) whilst the yield was increased from 75.6% to 87.1%, respectively