A novel adaptive back propagation neural network-unscented Kalman filtering algorithm for accurate lithium-ion battery state of charge estimation.

Accurate State of Charge (SOC) estimation for lithium-ion batteries has great significance with respect to the correct decision-making and safety control. In this research, an improved second-order-polarization equivalent circuit (SO-PEC) modelling method is proposed. In the process of estimating the SOC, a joint estimation algorithm, the Adaptive Back Propagation Neural Network and Unscented Kalman Filtering algorithm (ABP-UKF), is proposed. It combines the advantages of the robust learning rate in the Back Propagation (BP) neural network and the linearization error reduction in the Unscented Kalman Filtering (UKF) algorithm. In the BP neural network part, the self-adjustment of the learning factor accompanies the whole estimation process, and the improvement of the self-adjustment algorithm corrects the shortcomings of the UKF algorithm. In the verification part, the model is validated using a segmented double-exponential fit. Using the Ampere-hour integration method as the reference value, the estimation results of the UKF algorithm and the Back Propagation Neural Network and Unscented Kalman Filtering (BP-UKF) algorithm are compared, and the estimation accuracy of the proposed method is improved by 1.29% under the Hybrid Pulse Power Characterization (HPPC) working conditions, 1.28% under the Beijing Bus Dynamic Stress Test (BBDST) working conditions, and 2.24% under the Dynamic Stress Test (DST) working conditions. The proposed ABP-UKF algorithm has good results in estimating the SOC of lithium-ion batteries and will play an important role in the high-precision energy management process

Joint state of charge and state of health estimation of lithium-ion battery using improved adaptive dual extended Kalman filter based on piecewise forgetting factor recursive least squares.

This work aims to improve the accuracy of state of charge estimation for lithium-ion battery, as well as to accurately estimate state of health. This study presents a piecewise forgetting factor recursive least squares method based on integral separation with a second-order resistor-capacitor model and uses a novel adaptive filter based on error covariance correction on the conventional dual extended Kalman filter. The experiments show that the error of SOC estimation is less than 0.61% and the error of SOH is less than 0.09% under different complex conditions, the proposed method can effectively improve the estimation accuracy and robustness

Corrosion Behavior of Alloy C-276 in Supercritical Water

The corrosion behavior of Alloy C-276 in high-temperature and high-pressure water at 500°C and 25 MPa, respectively, was investigated by means of mass gain, scanning electron microscopic observation, and X-ray diffraction. The results indicated that the mass gain rate of Alloy C-276 in supercritical water obeys the parabolic law. The oxide scale was formed on Alloy C-276 with a dual-layer structure, mainly consisting of an outer Ni-rich layer and an inner Cr2O3/NiCr2O4 mixed layer. Tiny microcracks can also be found in the oxide scale if exposed for longer time. Meanwhile, higher temperature promotes oxide rate and results in thermal stress in the oxide film

A novel high-fidelity unscented particle filtering method for the accurate state of charge estimation of lithium-ion batteries.

Power Li-ion batteries are one of the core "three powers" systems of new energy vehicles, and its accurate batteries modeling and state prediction have become the core technology of the scientific and technological progress in the industry. This paper takes the ternary Li-ion batteries as the research subject. Aiming at the mathematical expressions of different structural features, innovatively construct a second-order Thevenin equivalent circuit model with autoregressive effect. This model can characterize the internal reaction mechanism of Li-ion batteries and fit the complex electrochemical reactions inside the battery. An improved particle filter model, namely a new high-fidelity unscented particle filter method, is designed and established. By introducing a suitable suggested density function, the model can accurately calculate the mean and variance, solve the particle degradation problem, and find out the Li-ion batteries state of charge, which is suitable for complex charging and discharging conditions. By further improving the theoretical analysis and combining with experiments under different working conditions, this method studies the Li-ion batteries state of charge. The test results show that the average absolute error of the improved equivalent circuit model is reduced by 0.00457 V, and the error rate is stably kept within 1%, which has the ability to describe Li-ion batteries well. When using the high-fidelity unscented particle filter algorithm to estimate the state of charge of the lithium battery, the robustness of the system is improved, the following effect is better, and the estimation error is controlled within 1.5%, which brings good practical value to the power Li-ion batteries

Realization of strong coupling between deterministic single-atom arrays and a high-finesse miniature optical cavity

We experimentally demonstrate strong coupling between a one-dimensional (1D) single-atom array and a high-finesse miniature cavity. The atom array is obtained by loading single atoms into a 1D optical tweezer array with dimensions of 1$\times$11. Therefore, a deterministic number of atoms is obtained, and the atom number is determined by imaging the atom array on a CCD camera in real time. By precisely controlling the position and spacing of the atom array in the high finesse Fabry--Perot cavity, all the atoms in the array are strongly coupled to the cavity simultaneously. The vacuum Rabi splitting spectra are discriminated for deterministic atom numbers from 1 to 8, and the $\sqrt{N}$ dependence of the collective enhancement of the coupling strength on atom number $N$ is validated at the single-atom level.Comment: Main text: 7 pages, 5 figures; Supplementary material: 5 pages, 4 figure

Classification and transfer learning of sleep spindles based on convolutional neural networks

BackgroundSleep plays a critical role in human physiological and psychological health, and electroencephalography (EEG), an effective sleep-monitoring method, is of great importance in revealing sleep characteristics and aiding the diagnosis of sleep disorders. Sleep spindles, which are a typical phenomenon in EEG, hold importance in sleep science.MethodsThis paper proposes a novel convolutional neural network (CNN) model to classify sleep spindles. Transfer learning is employed to apply the model trained on the sleep spindles of healthy subjects to those of subjects with insomnia for classification. To analyze the effect of transfer learning, we discuss the classification results of both partially and fully transferred convolutional layers.ResultsThe classification accuracy for the healthy and insomnia subjects’ spindles were 93.68% and 92.77%, respectively. During transfer learning, when transferring all convolutional layers, the classification accuracy for the insomnia subjects’ spindles was 91.41% and transferring only the first four convolutional layers achieved a classification result of 92.80%. The experimental results demonstrate that the proposed CNN model can effectively classify sleep spindles. Furthermore, the features learned from the data of the normal subjects can be effectively applied to the data for subjects with insomnia, yielding desirable outcomes.DiscussionThese outcomes underscore the efficacy of both the collected dataset and the proposed CNN model. The proposed model exhibits potential as a rapid and effective means to diagnose and treat sleep disorders, thereby improving the speed and quality of patient care

Evaluation of protein extraction methodologies on bacterial proteomic profiling: a comparative analysis

Bacterial proteomics is a pivotal tool for elucidating microbial physiology and pathogenicity. The efficiency and reliability of proteomic analyses are highly dependent on the protein extraction methodology, which directly influences the detectable proteome. In this study, we systematically compared four protein extraction protocols—SDT lysis buffer with boiling (SDT-B), SDT lysis buffer with ultrasonication (SDT-U/S), a combination of boiling and ultrasonication (SDT-B-U/S), and SDT lysis buffer with liquid nitrogen grinding followed by ultrasonication (SDT-LNG-U/S)—to evaluate their effects on peptide and protein identification, distribution, and reproducibility in Escherichia coli and Staphylococcus aureus. Both data-dependent acquisition (DDA) and data-independent acquisition (DIA) strategies were employed for comprehensive proteomic profiling. DDA analysis identified 23,912 unique peptides corresponding to 2,141 proteins in E. coli and 13,150 unique peptides corresponding to 1,511 proteins in S. aureus. DIA analysis yielded slightly fewer peptides (21,027 for E. coli and 7,707 for S. aureus) but demonstrated superior reproducibility. Among the tested protocols, SDT-B-U/S outperformed the others, identifying 16,560 peptides for E. coli and 10,575 peptides for S. aureus in DDA mode. It also exhibited the highest technical replicate correlation in DIA analysis (R2 = 0.92). This method enhanced the extraction of proteins within key molecular weight ranges (20–30 kDa for E. coli; 10–40 kDa for S. aureus) and was particularly effective for recovering membrane proteins (e.g., OmpC). Additionally, ultrasonication-based protocols outperformed the liquid nitrogen grinding approach in extracting the S. aureus proteome. These findings underscore the significant impact of protein extraction methods on bacterial proteomics. The SDT-B-U/S protocol—thermal denaturation followed by ultrasonication—proved most effective, enhancing protein recovery and reproducibility across both Gram-negative and Gram-positive bacteria. This work offers key guidance for optimizing microbial proteomic workflows

Characterization of an Artificial Swine-Origin Influenza Virus with the Same Gene Combination as H1N1/2009 Virus: A Genesis Clue of Pandemic Strain

Pandemic H1N1/2009 influenza virus, derived from a reassortment of avian, human, and swine influenza viruses, possesses a unique gene segment combination that had not been detected previously in animal and human populations. Whether such a gene combination could result in the pathogenicity and transmission as H1N1/2009 virus remains unclear. In the present study, we used reverse genetics to construct a reassortant virus (rH1N1) with the same gene combination as H1N1/2009 virus (NA and M genes from a Eurasian avian-like H1N1 swine virus and another six genes from a North American triple-reassortant H1N2 swine virus). Characterization of rH1N1 in mice showed that this virus had higher replicability and pathogenicity than those of the seasonal human H1N1 and Eurasian avian-like swine H1N1 viruses, but was similar to the H1N1/2009 and triple-reassortant H1N2 viruses. Experiments performed on guinea pigs showed that rH1N1 was not transmissible, whereas pandemic H1N1/2009 displayed efficient transmissibility. To further determine which gene segment played a key role in transmissibility, we constructed a series of reassortants derived from rH1N1 and H1N1/2009 viruses. Direct contact transmission studies demonstrated that the HA and NS genes contributed to the transmission of H1N1/2009 virus. Second, the HA gene of H1N1/2009 virus, when combined with the H1N1/2009 NA gene, conferred efficient contact transmission among guinea pigs. The present results reveal that not only gene segment reassortment but also amino acid mutation were needed for the generation of the pandemic influenza virus