Cost-optimal energy management of hybrid electric vehicles using fuel cell/battery health-aware predictive control

Energy management is an enabling technology for increasing the economy of fuel cell/battery hybrid electric vehicles. Existing efforts mostly focus on optimization of a certain control objective (e.g., hydrogen consumption), without sufficiently considering the implications for on-board power sources degradation. To address this deficiency, this article proposes a cost-optimal, predictive energy management strategy, with an explicit consciousness of degradation of both fuel cell and battery systems. Specifically, we contribute two main points to the relevant literature, with the purpose of distinguishing our study from existing ones. First, a model predictive control framework, for the first time, is established to minimize the total running cost of a fuel cell/battery hybrid electric bus, inclusive of hydrogen cost and costs caused by fuel cell and battery degradation. The efficacy of this framework is evaluated, accounting for various sizes of prediction horizon and prediction uncertainties. Second, the effects of driving and pricing scenarios on the optimized vehicular economy are explored

A review of fractional-order techniques applied to lithium-ion batteries, lead-acid batteries, and supercapacitors

Electrochemical energy storage systems play an important role in diverse applications, such as electrified transportation and integration of renewable energy with the electrical grid. To facilitate model-based management for extracting full system potentials, proper mathematical models are imperative. Due to extra degrees of freedom brought by differentiation derivatives, fractional-order models may be able to better describe the dynamic behaviors of electrochemical systems. This paper provides a critical overview of fractional-order techniques for managing lithium-ion batteries, lead-acid batteries, and supercapacitors. Starting with the basic concepts and technical tools from fractional-order calculus, the modeling principles for these energy systems are presented by identifying disperse dynamic processes and using electrochemical impedance spectroscopy. Available battery/supercapacitor models are comprehensively reviewed, and the advantages of fractional types are discussed. Two case studies demonstrate the accuracy and computational efficiency of fractional-order models. These models offer 15–30% higher accuracy than their integer-order analogues, but have reasonable complexity. Consequently, fractional-order models can be good candidates for the development of advanced b attery/supercapacitor management systems. Finally, the main technical challenges facing electrochemical energy storage system modeling, state estimation, and control in the fractional-order domain, as well as future research directions, are highlighted

Screening, Identification and Genome Annotation of Esterase-Producing Lactococcus garvieae

In this study, a strain, named S5-4, with high esterase activity was obtained from Nongxiangxing Daqu by primary and secondary screening, and subjected to whole genome sequencing, esterase annotation and metabolic pathway analysis. The strain exhibited a maximal ratio of transparent zone diameter to colony diameter (D/d) of 2.27 ± 0.02, and it was identified as Lactococcus garvieae. Its morphological, physiological, and biochemical characteristics were determined, and its esterase activity was measured to be 15.74 U/mL. This strain produced (0.345 0 ± 0.160 0) g/L of ethyl acetate and (0.298 3 ± 0.230 0) g/L of ethyl lactate after anaerobic fermentation at 35 ℃ for 15 days. The genome size of S5-4 was determined to be 2 191 113 bp in length, with a GC content of 37.99%. A total of 1 486 genes, including 4 rRNA genes, 69 tRNA genes, and 34 ncRNA genes were predicted. In total, 11 esterase genes were annotated in the genome of S5-4 including phosphatases (five intracellular ones and two membrane ones), acyl-CoA dehydrogenase (intracellular), triacylglycerol lipase (extracellular), carboxylase (extracellular), and hemicellulose esterase (intracellular). Based on functional genomic annotation, the metabolic pathways to produce ethyl acetate and ethyl lactate from starch and galactose as carbon sources were constructed and found to be consistent with the experimental results. Given its ability to produce esterase, the strain could be used as a biocatalyst for esterification in baijiu fermentation or as a flavor enhancer in Daqu production

Uncovering lupus nephritis-specific genes and the potential of TNFRSF17-targeted immunotherapy: a high-throughput sequencing study

IntroductionLupus nephritis (LN) is a severe manifestation of systemic lupus erythematosus (SLE). This study aimed to identify LN specific-genes and potential therapeutic targets.MethodsWe performed high-throughput transcriptome sequencing on peripheral blood mononuclear cells (PBMCs) from LN patients. Healthy individuals and SLE patients without LN were used as controls. To validate the sequencing results, qRT-PCR was performed for 5 upregulated and 5 downregulated genes. Furthermore, the effect of the TNFRSF17-targeting drug IBI379 on patient plasma cells and B cells was evaluated by flow cytometry.ResultsOur analysis identified 1493 and 205 differential genes in the LN group compared to the control and SLE without LN groups respectively, with 70 genes common to both sets, marking them as LN-specific. These LN-specific genes were significantly enriched in the ‘regulation of biological quality’ GO term and the cell cycle pathway. Notably, several genes including TNFRSF17 were significantly overexpressed in the kidneys of both LN patients and NZB/W mice. TNFRSF17 levels correlated positively with urinary protein levels, and negatively with complement C3 and C4 levels in LN patients. The TNFRSF17-targeting drug IBI379 effectively induced apoptosis in patient plasma cells without significantly affecting B cells.DiscussionOur findings suggest that TNFRSF17 could serve as a potential therapeutic target for LN. Moreover, IBI379 is presented as a promising treatment option for LN

Unraveling the mechanisms of intervertebral disc degeneration: an exploration of the p38 MAPK signaling pathway

Intervertebral disc (IVD) degeneration (IDD) is a worldwide spinal degenerative disease. Low back pain (LBP) is frequently caused by a variety of conditions brought on by IDD, including IVD herniation and spinal stenosis, etc. These conditions bring substantial physical and psychological pressure and economic burden to patients. IDD is closely tied with the structural or functional changes of the IVD tissue and can be caused by various complex factors like senescence, genetics, and trauma. The IVD dysfunction and structural changes can result from extracellular matrix (ECM) degradation, differentiation, inflammation, oxidative stress, mechanical stress, and senescence of IVD cells. At present, the treatment of IDD is basically to alleviate the symptoms, but not from the pathophysiological changes of IVD. Interestingly, the p38 mitogen-activated protein kinase (p38 MAPK) signaling pathway is involved in many processes of IDD, including inflammation, ECM degradation, apoptosis, senescence, proliferation, oxidative stress, and autophagy. These activities in degenerated IVD tissue are closely relevant to the development trend of IDD. Hence, the p38 MAPK signaling pathway may be a fitting curative target for IDD. In order to better understand the pathophysiological alterations of the intervertebral disc tissue during IDD and offer potential paths for targeted treatments for intervertebral disc degeneration, this article reviews the purpose of the p38 MAPK signaling pathway in IDD

Fast character modeling with sketch-based PDE surfaces

© 2020, The Author(s). Virtual characters are 3D geometric models of characters. They have a lot of applications in multimedia. In this paper, we propose a new physics-based deformation method and efficient character modelling framework for creation of detailed 3D virtual character models. Our proposed physics-based deformation method uses PDE surfaces. Here PDE is the abbreviation of Partial Differential Equation, and PDE surfaces are defined as sculpting force-driven shape representations of interpolation surfaces. Interpolation surfaces are obtained by interpolating key cross-section profile curves and the sculpting force-driven shape representation uses an analytical solution to a vector-valued partial differential equation involving sculpting forces to quickly obtain deformed shapes. Our proposed character modelling framework consists of global modeling and local modeling. The global modeling is also called model building, which is a process of creating a whole character model quickly with sketch-guided and template-based modeling techniques. The local modeling produces local details efficiently to improve the realism of the created character model with four shape manipulation techniques. The sketch-guided global modeling generates a character model from three different levels of sketched profile curves called primary, secondary and key cross-section curves in three orthographic views. The template-based global modeling obtains a new character model by deforming a template model to match the three different levels of profile curves. Four shape manipulation techniques for local modeling are investigated and integrated into the new modelling framework. They include: partial differential equation-based shape manipulation, generalized elliptic curve-driven shape manipulation, sketch assisted shape manipulation, and template-based shape manipulation. These new local modeling techniques have both global and local shape control functions and are efficient in local shape manipulation. The final character models are represented with a collection of surfaces, which are modeled with two types of geometric entities: generalized elliptic curves (GECs) and partial differential equation-based surfaces. Our experiments indicate that the proposed modeling approach can build detailed and realistic character models easily and quickly

Correction: Hu, X.; Sun, F. and Zou, Y. Estimation of State of Charge of a Lithium-Ion Battery Pack for Electric Vehicles Using an Adaptive Luenberger Observer. Energies 2010, 3, 1586–1603

The authors would like to make the following corrections to their published paper in Energies [1]. [...

Estimation of State of Charge of a Lithium-Ion Battery Pack for Electric Vehicles Using an Adaptive Luenberger Observer

In order to safely and efficiently use the power as well as to extend the lifetime of the traction battery pack, accurate estimation of State of Charge (SoC) is very important and necessary. This paper presents an adaptive observer-based technique for estimating SoC of a lithium-ion battery pack used in an electric vehicle (EV). The RC equivalent circuit model in ADVISOR is applied to simulate the lithium-ion battery pack. The parameters of the battery model as a function of SoC, are identified and optimized using the numerically nonlinear least squares algorithm, based on an experimental data set. By means of the optimized model, an adaptive Luenberger observer is built to estimate online the SoC of the lithium-ion battery pack. The observer gain is adaptively adjusted using a stochastic gradient approach so as to reduce the error between the estimated battery output voltage and the filtered battery terminal voltage measurement. Validation results show that the proposed technique can accurately estimate SoC of the lithium-ion battery pack without a heavy computational load