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Weaning is a critical transitional point in the life cycle of piglets. Early weaning can lead to post-weaning syndrome, destroy the intestinal barrier function and microbiota homeostasis, cause diarrhea and threaten the health of piglets. The nutritional components of milk and solid foods consumed by newborn animals can affect the diversity and structure of their intestinal microbiota, and regulate post-weaning diarrhea in piglets. Therefore, this paper reviews the effects and mechanisms of different nutrients, including protein, dietary fiber, dietary fatty acids and dietary electrolyte balance, on diarrhea and health of piglets by regulating intestinal function. Protein is an essential nutrient for the growth of piglets; however, excessive intake will cause many harmful effects, such as allergic reactions, intestinal barrier dysfunction and pathogenic growth, eventually aggravating piglet diarrhea. Dietary fiber is a nutrient that alleviates post-weaning diarrhea in piglets, which is related to its promotion of intestinal epithelial integrity, microbial homeostasis and the production of short-chain fatty acids. In addition, dietary fatty acids and dietary electrolyte balance can also facilitate the growth, function and health of piglets by regulating intestinal epithelial function, immune system and microbiota. Thus, a targeted control of dietary components to promote the establishment of a healthy bacterial community is a significant method for preventing nutritional diarrhea in weaned piglets.

Osteosarcoma is a common malignancy that often occurs in children, teenagers and young adults. Although the treatment strategy has improved, the results are still poor for most patients with metastatic or recurrent osteosarcomas. Therefore, it is necessary to identify new and effective prognostic biomarkers and therapeutic targets for diseases. Human genomes contain lncRNAs, transcripts with limited or insufficient capacity to encode proteins. They have been implicated in tumorigenesis, particularly regarding the onset, advancement, resistance to treatment, recurrence and remote dissemination of malignancies. Aberrant lncRNA expression in osteosarcomas has been reported by numerous researchers; lncRNAs have the potential to exhibit either oncogenic or tumor-suppressing behaviors and thus, to govern the advancement of this skeletal cancer. They are suspected to influence osteosarcoma cell growth, replication, invasion, migration, remote dissemination and programmed cell death. Additionally, they have been recognized as clinical markers, and may participate in the development of multidrug resistance. Therefore, the study of lncRNAs in the growth, metastasis, treatment and prognosis of osteosarcoma is very important for the active prevention and treatment of osteosarcoma. Consequently, this work reviews the functions of lncRNAs.

Levulinic acid, a hydrolysis product of lignocellulose, can be metabolized into important compounds in the field of medicine and pesticides by engineered strains of Saccharomyces cerevisiae. Levulinic acid, as an intermediate product widely found in the conversion process of lignocellulosic biomass, has multiple applications. However, its toxicity to Saccharomyces cerevisiae reduces its conversion efficiency, so screening Saccharomyces cerevisiae genes that can tolerate levulinic acid becomes the key. By creating a whole-genome knockout library and bioinformatics analysis, this study used the phenotypic characteristics of cells as the basis for screening and found the HMX1 gene that is highly sensitive to levulinic acid in the oxidative stress pathway. After knocking out HMX1 and treating with levulinic acid, the omics data of the strain revealed that multiple affected pathways, especially the expression of 14 genes related to the cell wall and membrane system, were significantly downregulated. The levels of acetyl-CoA and riboflavin decreased by 1.02-fold and 1.44-fold, respectively, while the content of pantothenic acid increased. These findings indicate that the cell wall-membrane system, as well as the metabolism of acetyl-CoA and riboflavin, are important in improving the resistance of Saccharomyces cerevisiae to levulinic acid. They provide theoretical support for enhancing the tolerance of microorganisms to levulinic acid, which is significant for optimizing the conversion process of lignocellulosic biomass to levulinic acid.

Abstract With the electrochemical performance of batteries approaching the bottleneck gradually, it is increasingly urgent to solve the safety issue. Herein, all‐in‐one strategy is ingeniously developed to design smart, safe, and simple (3S) practical pouch‐type LiNi0.8Co0.1Mn0.1O2||Graphite@SiO (NCM811||Gr@SiO) cell, taking full advantage of liquid and solid‐state electrolytes. Even under the harsh thermal abuse and high voltage condition (100 °C, 3–4.5 V), the pouch‐type 3S NCM811||Gr@SiO cell can present superior capacity retention of 84.6% after 250 cycles (based pouch cell: 47.8% after 250 cycles). More surprisingly, the designed 3S NCM811||Gr@SiO cell can efficiently improve self‐generated heat T1 by 45 °C, increase TR triggering temperature T2 by 40 °C, and decrease the TR highest T3 by 118 °C. These superior electrochemical and safety performances of practical 3S pouch‐type cells are attributed to the robust and stable anion‐induced electrode‐electrolyte interphases and local solid‐state electrolyte protection layer. All the fundamental findings break the conventional battery design guidelines and open up a new direction to develop practical high‐performance batteries.

Engineering Saccharomyces cerevisiae for biodegradation and transformation of industrial toxic substances such as catechol (CA) has received widespread attention, but the low tolerance of S. cerevisiae to CA has limited its development. The exploration and modification of genes or pathways related to CA tolerance in S. cerevisiae is an effective way to further improve the utilization efficiency of CA. This study identified 36 genes associated with CA tolerance in S. cerevisiae through genome-wide identification and bioinformatics analysis and the ERG6 knockout strain (ERG6Δ) is the most sensitive to CA. Based on the omics analysis of ERG6Δ under CA stress, it was found that ERG6 knockout affects pathways such as intrinsic component of membrane and pentose phosphate pathway. In addition, the study revealed that 29 genes related to the cell wall-membrane system were up-regulated by more than twice, NADPH and NADP+ were increased by 2.48 and 4.41 times respectively, and spermidine and spermine were increased by 2.85 and 2.14 times, respectively, in ERG6Δ. Overall, the response of cell wall-membrane system, the accumulation of spermidine and NADPH, as well as the increased levels of metabolites in pentose phosphate pathway are important findings in improving the CA resistance. This study provides a theoretical basis for improving the tolerance of strains to CA and reducing the damage caused by CA to the ecological environment and human health.

IntroductionOsteoporosis, one of the most common non-communicable human diseases worldwide, is one of the most prevalent disease of the adult skeleton. Glucocorticoid-induced osteoporosis(GIOP) is the foremost form of secondary osteoporosis, extensively researched due to its prevalence.Probiotics constitute a primary bioactive component within numerous foods, offering promise as a potential biological intervention for preventing and treating osteoporosis. This study aimed to evaluate the beneficial effects of the probiotic Lactobacillus plantarum on bone health and its underlying mechanisms in a rat model of glucocorticoid dexamethasone-induced osteoporosis, using the osteoporosis treatment drug alendronate as a reference.MethodsWe examined the bone microstructure (Micro-CT and HE staining) and analyzed the gut microbiome and serum metabolome in rats.Results and discussionThe results revealed that L. plantarum treatment significantly restored parameters of bone microstructure, with elevated bone density, increased number and thickness of trabeculae, and decreased Tb.Sp. Gut microbiota sequencing results showed that probiotic treatment increased gut microbial diversity and the ratio of Firmicutes to Bacteroidota decreased. Beneficial bacteria abundance was significantly increased (Lachnospiraceae_NK4A136_group, Ruminococcus, UCG_005, Romboutsia, and Christensenellaceae_R_7_group), and harmful bacteria abundance was significantly decreased (Desulfovibrionaceae). According to the results of serum metabolomics, significant changes in serum metabolites occurred in different groups. These differential metabolites were predominantly enriched within the pathways of Pentose and Glucuronate Interconversions, as well as Propanoate Metabolism. Furthermore, treatment of L. plantarum significantly increased serum levels of Pyrazine and gamma-Glutamylcysteine, which were associated with inhibition of osteoclast formation and promoting osteoblast formation. Lactobacillus plantarum can protect rats from DEX-induced GIOP by mediating the “gut microbial-bone axis” promoting the production of beneficial bacteria and metabolites. Therefore L. plantarum is a potential candidate for the treatment of GIOP.

Abstract Accurate evaluation of Li-ion battery (LiB) safety conditions can reduce unexpected cell failures, facilitate battery deployment, and promote low-carbon economies. Despite the recent progress in artificial intelligence, anomaly detection methods are not customized for or validated in realistic battery settings due to the complex failure mechanisms and the lack of real-world testing frameworks with large-scale datasets. Here, we develop a realistic deep-learning framework for electric vehicle (EV) LiB anomaly detection. It features a dynamical autoencoder tailored for dynamical systems and configured by social and financial factors. We test our detection algorithm on released datasets comprising over 690,000 LiB charging snippets from 347 EVs. Our model overcomes the limitations of state-of-the-art fault detection models, including deep learning ones. Moreover, it reduces the expected direct EV battery fault and inspection costs. Our work highlights the potential of deep learning in improving LiB safety and the significance of social and financial information in designing deep learning models.

The animal gut harbors diverse microbes that play an essential role in the well-being of their host. Specific diets, such as those rich in dietary fiber, are vital in disease prevention and treatment because they affect intestinal flora and have a positive impact on the metabolism, immunity, and intestinal function of the host. Dietary fiber can provide energy to colonic epithelial cells, regulate the structure and metabolism of intestinal flora, promote the production of intestinal mucosa, stimulate intestinal motility, improve glycemic and lipid responses, and regulate the digestion and absorption of nutrients, which is mainly attributed to short-chain fatty acids (SCFA), which is the metabolite of dietary fiber. By binding with G protein-coupled receptors (including GPR41, GPR43 and GPR109A) and inhibiting the activity of histone deacetylases, SCFA regulate appetite and glucolipid metabolism, promote the function of the intestinal barrier, alleviate oxidative stress, suppress inflammation, and maintain immune system homeostasis. This paper reviews the physicochemical properties of dietary fiber, the interaction between dietary fiber and intestinal microorganisms, the role of dietary fiber in maintaining intestinal health, and the function of SCFA, the metabolite of dietary fiber, in inhibiting inflammation. Furthermore, we consider the effects of dietary fiber on the intestinal health of pigs, the reproduction and lactation performance of sows, and the growth performance and meat quality of pigs.

The internal negative electrode potential in lithium-ion batteries (LIBs) is intricately linked to the lithium-ion intercalation and plating reactions occurring within the cell. With the expansion of cell sizes, the internal negative electrode potential distribution gradually becomes inconsistent. However, the existing negative electrode potential estimation models and fast charging strategies have not yet considered the impact of consistency, and the model estimation accuracy will be greatly influenced by different temperatures and charging rates. This study proposes an online lithium-free fast charging equivalent circuit model (OLFEM) for estimating the negative electrode potential terminal voltage and developing fast charging strategies of long-dimensional LIBs in real vehicles. This study employs distributed reference electrodes integrated into long-dimensional LIBs and compares the negative electrode potential measured in the vicinity of both the negative and positive tabs. Subsequently, based on the lowest negative electrode potential point, model parameters were obtained at different temperatures and charging rates. This model is further verified under different operating conditions. Finally, a fast-charging strategy without lithium plating is developed in real-time based on the negative electrode potential estimated by the model. The results demonstrate that long-dimensional cells exhibit a lower negative electrode potential on the positive tab side. Across various temperatures and charging rates, the calibrated model achieves a negative electrode potential estimated error within 25 mV, and the estimation error for terminal voltage is within 5 mV. The proposed fast-charging method prevents lithium plating and charges the cell up to 96.8% within an hour. After 100 cycles, the cell experiences a capacity degradation of less than 2%, and the disassembly results indicate that no lithium precipitation has occurred. The methods outlined in this study provide valuable insights for online fast charging of large-dimensional batteries without lithium plating.

With the rapid development of sequencing technology, research on pigs has focused on intestinal microbes. Accumulating evidence suggests that the metabolites of intestinal microbes are the key medium for interactions between microbes and the host. Amino acid metabolism is involved in the growth and immune processes of pigs. The gut microbes of pigs are heavily involved in the metabolism of amino acids in their hosts. Here, we review the latest relevant literature. Research findings show that microbial metabolites, such as indoles, short-chain fatty acids, and ammonia, play a key role in gut health. Moreover, we summarize the effects of amino acids on the structure of the gut microbial community and the metabolism of amino acids by pig gut microbes. Evidence shows that microbial amino acid metabolites act as signal molecules in the intestine and play an important role in the intestinal health of pigs.

Remaining useful life (RUL) prediction has become one of the key technologies for reducing costs and improving safety of lithium-ion batteries. To our knowledge, it is difficult for existing nonlinear degradation models of the Wiener process to describe the complex degradation process of lithium-ion batteries, and there is a problem with low precision in parameter estimation. Therefore, this paper proposes a method for predicting the RUL of lithium-ion batteries based on a cubic polynomial degradation model and envelope extraction. Firstly, based on the degradation characteristics of lithium-ion batteries, a cubic polynomial function is used to fit the degradation trajectory and compared with other nonlinear degradation models for verification. Secondly, a subjective parameter estimation method based on envelope extraction is proposed that estimates the actual degradation trajectory by using the average of the upper and lower envelope curves of the degradation data of lithium-ion batteries and uses the maximum likelihood estimation (MLE) method to estimate the unknown model parameters in two steps. Finally, for comparison with several typical nonlinear models, experiments are carried out based on the practical degradation data of lithium-ion batteries. The effectiveness of the proposed method to improve the accuracy of RUL prediction for lithium-ion batteries was demonstrated in terms of the mean square error (MSE) of the model and MSE of RUL prediction.

During the manufacturing process of the lithium-ion battery, metal foreign matter is likely to be mixed into the battery, which seriously influences the safety performance of the battery. In order to reduce the outflow of such foreign matter defect cells, the production line universally adopted the K-value test process. In the traditional K-value test, the detection threshold is determined empirically, which has poor dynamic characteristics and probably leads to missing or false detection. Based on comparing the screening effect of different machine learning algorithms for the production data of lithium-ion cells, this paper proposes a K-value dynamic screening algorithm for the cell production line based on the local outlier factor algorithm. The analysis results indicate that the proposed method can adaptively adjust the detection threshold. Furthermore, we validated its effectiveness through the metal foreign matter implantation experiment conducted in the pilot manufacturing line. Experiment results show that the proposed method’s detection rate is improved significantly. The increase in the detection rate of foreign matter defects is beneficial to improving battery quality and safety.

Battery life management is critical for plug-in hybrid electric vehicles (PHEVs) to prevent dangerous situations such as overcharging and over-discharging, which could cause thermal runaway. PHEVs have more complex operating conditions than EVs due to their dual energy sources. Therefore, the SOH estimation for PHEV vehicles needs to consider the specific operating characteristics of the PHEV and make calibrations accordingly. Firstly, we estimated the initial SOH by combining data-driven and empirical models. The data-driven method used was the incremental state of charge (SOC)-capacity method, and the empirical model was the Arrhenius model. This method can obtain the battery degradation trend and predict the SOH well in realistic applications. Then, according to the multiple characteristics of PHEV, we conducted a correlation analysis and selected the UF as the calibration factor because the UF has the highest correlation with SOH. Finally, we calibrated the parameters of the Arrhenius model using the UF in a fuzzy logic way, so that the calibrated fitting degradation trends could be closer to the true SOH. The proposed calibration method was verified by a PHEV dataset that included 11 vehicles. The experiment results show that the root mean square error (RMSE) of the SOH fitting after UF calibration can be decreased by 0.2–14% and that the coefficient of determination (R2) for the calibrated fitting trends can be improved by 0.5–32%. This provides more reliable guidance for the safe management and operation of PHEV batteries.

The lithium iron phosphate (LiFePO4) blade battery is a long, rectangular-shaped cell that can be directly integrated into battery pack systems. It enhances volumetric power density, significantly reduces costs, and is widely utilized in electric vehicles. However, the flat open circuit voltage and significant polarization differences under wide operational temperatures are challenging for accurate voltage modeling of battery management systems (BMSs). In particular, inaccurate state of charge (SOC) estimation may cause overcharging and over-discharging risks. To accurately perceive the SOC of LiFePO4 blade batteries, a SOC estimation method based on the backpropagation neural network-extended Kalman filter (BPNN-EKF) algorithm is proposed. BPNN is a neural network model that utilizes the backpropagation algorithm to update model parameters, while EKF is an optimal estimation algorithm. Firstly, dynamic working condition tests, including the New European Driving Cycle (NEDC) and high-speed working (HSW) condition tests, are conducted under a wide temperature range (−25–43 °C). HSW conditions refer to a simulated operating condition that mimics the driving of an electric vehicle on a highway. The minimum voltage of the battery system is used as the output for training the BPNN model. We derive the Kalman gain by combining the BPNN output voltage. Additionally, the EKF algorithm is employed to correct the SOC value using voltage error information. Concerning long SOC calculation intervals, capacity errors, initial SOC errors, and current and voltage sampling errors, the maximum SOC estimation RMSE is 3.98% at −20 °C NEDC, 3.62% at 10 °C NEDC, and 1.68% at 35 °C HSW. The proposed algorithm can be applied to different temperatures and operations, demonstrating high robustness. This BPNN-EKF algorithm has the potential to be embedded in electric vehicle BMS systems for practical applications.

This paper proposes a framework for generating a battery test profile that accounts for the complex operating conditions of electric vehicles, which is essential for ensuring the durability and safety of the battery system used in these vehicles. Additionally, such a test profile could potentially accelerate the development of electric vehicles. To achieve this objective, the study utilizes a simplified longitudinal dynamics model that incorporates various factors such as the drivetrain efficiency, battery system energy conversion efficiency, and regenerative braking efficiency. The battery test profile is based on the China light-duty vehicle test cycle-passenger car (CLTC-P) and is validated through testing on an electric vehicle with a chassis dynamometer. The results indicate a high degree of consistency between the generated and measured profiles, confirming the efficacy of the simplified longitudinal dynamics model.

Metabolic disorders and intestinal flora imbalance usually accompany obesity. Due to its diverse biological activities, Lactobacillus plantarum is widely used to alleviate various diseases as a probiotic. Here, we show that L. plantarum can reduce the body weight of mice fed high-fat diets, reduce fat accumulation, and enhance mice glucose tolerance. Our results show that L. plantarum can significantly reduce the expression of DGAT1 and DGAT2, increase the expression of Cpt1a, and promote the process of lipid metabolism. Further data show that L. plantarum can increase the SCFA content in the colon and reverse the intestinal flora disorder caused by HFD, increase the abundance of Bacteroides, and Bifidobacteriales, and reduce the abundance of Firmicutes and Clostridiales. Finally, through Pearson correlation analysis, we found that Bacteroides and SCFAs are positively correlated, while Clostridiales are negatively correlated with SCFAs. Therefore, we believe that L. plantarum can regulate the structure of the intestinal microbial community, increase the production of SCFAs and thus regulate lipid metabolism.

The accuracy of capacity estimation is of great importance to the safe, efficient, and reliable operation of battery systems. In recent years, data-driven methods have emerged as promising alternatives to capacity estimation due to higher estimation accuracy. Despite significant progress, data-driven methods are mainly developed by experimental data under well-controlled charge–discharge processes, which are seldom available for practical battery health monitoring under realistic conditions due to uncertainties in environmental and operational conditions. In this paper, a novel method to estimate the capacity of large-format LiFePO4 batteries based on real data from electric vehicles is proposed. A comprehensive dataset consisting of 85 vehicles that has been running for around one year under diverse nominal conditions derived from a cloud platform is generated. A classification and aggregation capacity prediction method is developed, combining a battery aging experiment with big data analysis on cloud data. Based on degradation mechanisms, IC curve features are extracted, and a linear regression model is established to realize high-precision estimation for slow-charging data with constant-current charging. The selected features are highly correlated with capacity (Pearson correlation coefficient < 0.85 for all vehicles), and the MSE of the capacity estimation results is less than 1 Ah. On the basis of protocol analysis and mechanism studies, a feature set including internal resistance, temperature, and statistical characteristics of the voltage curve is constructed, and a neural network (NN) model is established for multi-stage variable-current fast-charging data. Finally, the above two models are integrated to achieve capacity prediction under complex and changeable realistic working conditions, and the relative error of the capacity estimation method is less than 0.8%. An aging experiment using the battery, which is the same as those equipped in the vehicles in the dataset, is carried out to verify the methods. To the best of the authors’ knowledge, our study is the first to verify a capacity estimation model derived from field data using an aging experiment of the same type of battery.

Concentrated electrolytes display superior thermal stability due to their non-flammability nature. Here, the authors show that LiN(SO2F)2-based concentrated electrolytes are incapable of solving the safety issues due to heat release during reaction between the lithiated graphite and electrolyte.

Lithium-ion batteries are widely used in electric vehicles and energy storage systems. Sudden fire accident is one of the most serious issue, which is mainly caused by unpredicted internal short circuit. Metal particle defect is a key factor in internal short circuit it will not show an obvious abnormal change in battery external characteristic just like mechanical and thermal abuse. So, a non-destructive testing of battery internal metal defect is very necessary. This study is first time to scan and analyze different types of defects inside a battery by using ultrasonic technology, and it shows the detection capability boundary of this methodology. A non-contact ultrasonic scanning system with multi-channel was built to scan the battery sample with aluminum foil, copper foil and copper powder defects. The position and shape of those defects were clearly shown by using tomography methodology. It was found that the acoustic properties difference between metal defects and battery active materials has a strong influence on detection sensitivity. Compared with aluminum foil, copper foil and copper powder are easier to be detected and change the ultrasonic signal greatly, they will produce an obvious shadowing artifacts and speed displacement phenomena in tomography images. Ultrasonic tomography technology is an effective method for non-destructive testing of lithium-ion batteries.

Lithium plating, induced by fast charging and low-temperature charging, is one of the reasons for capacity fading and causes safety problems for lithium-ion batteries. Hence, reliable and effective non-destructive detection methods for lithium plating are needed. In this research, electrochemical impedance and internal resistance for batteries are measured during the rest period after charging. The results for lithium plating batteries and normal batteries are compared and analyzed. Lithium plating detection is realized with multiple indicators extracted from electrochemical impedance and internal resistance results. The effectiveness of the proposed detection methods is verified by the experiments conducted with commercial large-capacity batteries. The proposed methods have further potential to be used in battery management systems to realize online detection of lithium plating and improve the safety of battery systems.

In the field of state estimation for the lithium-ion battery (LIB), model-based methods (white box) have been developed to explain battery mechanism and data-driven methods (black box) have been designed to learn battery statistics. Both white box methods and black box methods have drawn much attention recently. As the combination of white box and black box, physics-informed machine learning has been investigated by embedding physic laws. For LIB state estimation, this work proposes a fractional-order recurrent neural network (FORNN) encoded with physics-informed battery knowledge. Three aspects of FORNN can be improved by learning certain physics-informed knowledge. Firstly, the fractional-order state feedback is achieved by introducing a fractional-order derivative in a forward propagation process. Secondly, the fractional-order constraint is constructed by a voltage partial derivative equation (PDE) deduced from the battery fractional-order model (FOM). Thirdly, both the fractional-order gradient descent (FOGD) and fractional-order gradient descent with momentum (FOGDm) methods are proposed by introducing a fractional-order gradient in the backpropagation process. For the proposed FORNN, the sensitivity of the added fractional-order parameters are analyzed by experiments under the federal urban driving schedule (FUDS) operation conditions. The experiment results demonstrate that a certain range of every fractional-order parameter can achieve better convergence speed and higher estimation accuracy. On the basis of the sensitivity analysis, the fractional-order parameter tuning rules have been concluded and listed in the discussion part to provide useful references to the parameter tuning of the proposed algorithm.

The state estimation of lithium-ion battery is the basis of an intelligent battery management system; therefore, both model-based and data-driven methods have been designed and developed for state estimation. Rather than using complex partial differential equations and the complicated parameter tuning of a model-based method, a machine learning algorithm provides a new paradigm and has been increasingly applied to cloud big-data platforms. Although promising, it is now recognized that big data for machine learning may not be consistent in terms of data quality with reliable labels. Moreover, many algorithms are still applied as a black box that may not learn battery inner information well. To enhance the algorithm generalization in realistic situations, this paper presents a fractional-order physics-informed recurrent neural network (PIRNN) for state estimation. The fractional-order characteristics from battery mechanism are embedded into the proposed algorithm by introducing fractional-order gradients in backpropagation process and fractional-order constraints into the convergence loss function. With encoded battery knowledge, the proposed fractional-order PIRNN would accelerate the convergence speed in training process and achieve improved prediction accuracies. Experiments of four cells under federal urban driving schedule operation conditions and different temperatures are conducted to illustrate the estimation effects of the proposed fractional-order PIRNN. Compared to the integer-order gradient descent method, the fractional-order gradient descent method proposed in this work can optimize network convergence and obtains regression coefficient larger than 0.995. Moreover, the experimental results indicate that the proposed algorithm can achieve 2.5% estimation accuracy with the encoding fractional-order knowledge of lithium-ion batteries.

Lithium-ion batteries on electric vehicles have been increasingly deployed for the enhancement of grid reliability and integration of renewable energy, while users are concerned about extra battery degradation caused by vehicle-to-grid (V2G) operations. This paper details a multi-year cycling study of commercial 24 Ah pouch batteries with Li(NiMnCo)O2 (NCM) cathode, varying the average state of charge (SOC), depth of discharge (DOD), and charging rate by 33 groups of experiment matrix. Based on the reduced freedom voltage parameter reconstruction (RF-VPR), a more efficient non-intrusive diagnosis is combined with incremental capacity (IC) analysis to evaluate the aging mechanisms including loss of lithium-ion inventory and loss of active material on the cathode and anode. By analyzing the evolution of indicator parameters and the cumulative degradation function (CDF) of the battery capacity, a non-linear degradation model with calendar and cyclic aging is established to evaluate the battery aging cost under different unmanaged charging (V0G) and V2G scenarios. The result shows that, although the extra energy throughput would cause cyclic degradation, discharging from SOC 90 to 65% by V2G will surprisingly alleviate the battery decaying by 0.95% compared to the EV charged within 90–100% SOC, due to the improvement of calendar life. By optimal charging strategies, the connection to the smart grid can potentially extend the EV battery life beyond the scenarios without V2G.

The LiFePO4 (LFP) battery tends to underperform in low temperature: the available energy drops, while the state of charge (SOC) and residual available energy (RAE) estimation error increase dramatically compared to the result under room temperature, which causes mileage anxiety for drivers. This paper introduces an artificial intelligence-based electrical–thermal coupling battery model, presents an application-oriented procedure to estimate SOC and RAE for a reliable and effective battery management system, and puts forward a model-based strategy to control the battery thermal state in low temperature. Firstly, an LFP battery electrical model based on artificial intelligence is proposed to estimate the terminal voltage, and a thermal resistance model with an EKF estimation algorithm is established to assess the temperature distribution in the battery pack. Then, the electrical and thermal models are coupled, a closed-loop EKF algorithm is employed to estimate the battery SOC, and a fusion method is discussed. The coupled model is simulated under a given protocol and RAE can be obtained. Finally, based on the electrical–thermal coupling model and RAE calculation algorithm, a preheating method and constant power condition-based RAE estimation are discussed, and the thermal management strategy of the battery system under low temperature is formed. Results show that the estimation error of SOC can be controlled within 2% and RAE can be controlled within 4%, respectively. The preheating strategy at low temperature and low SOC can significantly improve the energy output of the battery pack system.

Macrophages, which are functional plasticity cells, have the ability to phagocytize and digest foreign substances and acquire pro-(M1-like) or anti-inflammatory (M2-like) phenotypes according to their microenvironment. The large number of macrophages in the intestinal tract, play a significant role in maintaining the homeostasis of microorganisms on the surface of the intestinal mucosa and in the continuous renewal of intestinal epithelial cells. They are not only responsible for innate immunity, but also participate in the development of intestinal inflammation. A clear understanding of the function of macrophages, as well as their role in pathogens and inflammatory response, will delineate the next steps in the treatment of intestinal inflammatory diseases. In this review, we discuss the origin and development of macrophages and their role in the intestinal inflammatory response or infection. In addition, the effects of macrophages in the occurrence and development of inflammatory bowel disease (IBD), and their role in inducing fibrosis, activating T cells, reducing colitis, and treating intestinal inflammation were also reviewed in this paper.

Summary: Heating battery at low temperatures is fundamental to avoiding the range anxiety and the time-consuming charging associated with electric vehicles (EVs). One method for achieving fast and uniform battery heating is to polarize the cell under pulse currents. However, the on-board implementation of this method leads to an increase in the cost and size. Therefore, in this study, an adapted EV circuitry compatible with the existing one and an optimized operating condition are proposed to enable rapid battery heating. With this circuit, electricity transfer between the cells can be realized through a motor, leading to remarkably higher battery currents than those of the conventional circuit. The increase in the maximum heating currents (from 1.41C to 4C) resulted in a battery temperature rise of 8.6°C/min at low temperatures. This heating method exhibits low cost, high efficiency, and negligible effects on battery degradation, practical and promising on battery heating of EVs.

Phenol is a ubiquitous pollutant and can contaminate natural water resources. Hence, the removal of phenol from wastewater is of significant importance. A series of biological methods were used to remove phenol based on the natural ability of microorganisms to degrade phenol, but the tolerance mechanism of phenol-degraded strains to phenol are not very clear. Morphological observation on Candida tropicalis showed that phenol caused the reactive oxygen species (ROS) accumulation, damaging the mitochondrial and the endoplasmic reticulum. On the basis of transcriptome data and cell wall susceptibility analysis, it was found that C. tropicalis prevented phenol-caused cell damage through improvement of cell wall resistance, maintenance of high-fidelity DNA replication, intracellular protein homeostasis, organelle integrity, and kept the intracellular phenol concentration at a low level through cell-wall remodeling and removal of excess phenol via MDR/MXR transporters. The knowledge obtained will promote the genetic modification of yeast strains in general to tolerate the high concentrations of phenol and improve their efficiency of phenol degradation.

A great many EVs in cold areas suffer from range attenuation in winter, which causes driver anxiety concerning the driving range, representing a hot topic. Many researchers have analyzed the reasons for range attenuation but the coupling mechanism of the battery as well as the vehicle and driving conditions have not been clearly estimated. To quantitatively investigate the driving range attenuation of electric vehicles (EVs) during winter, an EV model mainly integrated with a passenger-cabin thermal model, battery model, and vehicle dynamic model was constructed and simulated based on the mass-produced Wuling HongGuang Mini EV. Real vehicle dynamic driving data was used to validate the model. Based on NEDC driving conditions, the driving range calculation formula and energy flow diagram analysis method were used. The reason for attenuation was evaluated quantitatively. Results show that battery energy loss and breaking recovery energy loss contribute nearly half of the range attenuation, which may be alleviated by battery preheating. Suggestions for extending driving range are proposed based on the research.

The health assessment for energy storage batteries matters in the context of carbon neutrality. Dual closed-loops capacity framework integrated with a reduced-order electrochemical model including triple side reactions is put forward, realizing parameter correction for health evaluation. Simplified microgrid aging experiment is formulated to test the closed-loop matching between the aging mechanism and electrochemical model relying on incremental capacity analysis. In addition, taking into account the future degradation prediction for energy storage system, the reliable capacity output afterwards acts as references for closed-loop parameter updating in empirical model to predict degradation evolution. The framework proposed implements the closed-loop dynamic updating for aging parameters with ideal error within 2%, making up for the lack of aging mechanism interpretation of accustomed empirical or data-driven black box model in the field of energy storage batteries.

Electrochemical impedance spectroscopy (EIS) is a powerful tool for investigating electrochemical systems, such as lithium-ion batteries or fuel cells, given its high frequency resolution. The distribution of relaxation times (DRT) method offers a model-free approach for a deeper understanding of EIS data. However, in lithium-ion batteries, the differential capacity caused by diffusion processes is non-negligible and cannot be decomposed by the DRT method, which limits the applicability of the DRT method to lithium-ion batteries. In this study, a joint estimation method with Tikhonov regularization is proposed to estimate the differential capacity and the DRT simultaneously. Moreover, the equivalence of the differential capacity and the incremental capacity is proven. Different types of commercial lithium-ion batteries are tested to validate the joint estimation method and to verify the equivalence. The differential capacity is shown to be a promising approach to the evaluation of the state-of-health (SOH) of lithium-ion batteries based on its equivalence with the incremental capacity.

The lithium titanium oxide (LTO) anode is widely accepted as one of the best anodes for the future lithium ion batteries in electric vehicles (EVs), especially since its cycle life is very long. In this paper, three different commercial LTO cells from different manufacturers were studied in accelerated cycle life tests and their capacity fades were compared. The result indicates that under 55 °C, the LTO battery still shows a high capacity fade rate. The battery aging processes of all the commercial LTO cells clearly include two stages. Using the incremental capacity (IC) analysis, it could be judged that in the first stage, the battery capacity decreases mainly due to the loss of anode material and the degradation rate is lower. In the second stage, the battery capacity decreases much faster, mainly due to the degradation of the cathode material. The result is important for the state of health (SOH) estimation and remaining useful life (RUL) prediction of battery management system (BMS) for LTO batteries in EVs.

Incremental capacity analysis (ICA) has been used pervasively to characterize the degradation mechanisms of the lithium-ion batteries, and several online state-of-health estimation algorithms are built based on ICA. However, the stairs and the noises in the discrete sampled voltage data obstruct the calculation of the capacity differentiation over voltage (dQ/dV), therefore we need methods to fit the sampled voltage first. In this paper, the support vector regression (SVR) algorithm is used to smooth the sampled voltage curve using Gaussian kernels. A parametric study has been conducted to show how to enhance the performances of the SVR algorithm, including (1) speeding up the algorithm by downsampling; (2) avoiding overfitting and under-fitting using proper standard deviation σ in the Gaussian kernel; (3) making precise capture of the characteristic peaks. A novel method using linear approximation has been proposed to help judge the accuracy of the SVR algorithm in tracking the ICA peaks. And advanced SVR algorithms using double σ and using cost function that directly regulates the differentiation result have been proposed. The advanced SVR algorithm can make accurate curve fitting for ICA with overall error less than 1% (maximum 3%) throughout cycle lives, for four kinds of commercial lithium-ion batteries with LiFePO4 and LiNixCoyMnzO2 cathodes, making it promising to be further applied in online SOH estimation algorithms.