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•Battery pack equalization strategy based on UCCVC hypothesis is proposed.•The convergence of equalization is obtained in different inconsistent conditions.•The equalization strategy ...is simulated in fresh and aged scenarios.•The equalization strategy is embedded in a real BMS for practical application analysis.
Lithium-ion battery pack capacity directly determines the driving range and dynamic ability of electric vehicles (EVs). However, inconsistency issues occur and decrease the pack capacity due to internal and external reasons. In this paper, an equalization strategy is proposed to solve the inconsistency issues. The difference of inconsistency for lithium-ion battery pack equalization is determined based on the uniform charging cell voltage curves hypothesis. Stability of the sampling voltage interval and convergence of equalization are analyzed experimentally. Finally, the results of simulation and experiment both show that the equalization strategy not only maximizes pack capacity, but also adapts to different consistency scenarios. Pack capacity and consistency in the fresh or aged state are significantly improved after battery equalization. In the real battery module experiment, the maximum absolute errors of open circuit voltage (OCV) and state of charge (SOC) are 21.9 mV and 1.86%, and the capacity is improved by 13.03%. Importantly, the equalization strategy has high precision and competitive simplicity with low computation, making it suitable for on-line equalization in EVs.
•An intelligent SOH estimation framework for EVs big data platform is presented.•The capacity estimation model is established based on neural network algorithm.•The dataset is generated from ...real-word data of EVs under actual operation.•Big data analysis is used for extraction of health feature parameters and selection of training data.•Accurate SOH evaluation can be obtained by using the historical operating data.
State of health (SOH) of in-vehicle lithium-ion batteries not only directly determines the acceleration performance and driving range of electric vehicles (EVs), but also reflects the residual value of the batteries. Especially, with the development of data acquisition and analysis technologies, using big data to realize on-line evaluation of battery SOH shows vital significance. In this paper, we propose an intelligent SOH estimation framework based on the real-world data of EVs collected by the big data platform. Defined by the more accessible detection, the health features are extracted from historical operating data. Then, the deep learning process is implemented in feedforward neural network driven by the degradation index. The estimation method is validated by the one-year monitoring dataset from 700 vehicles with different driving mode. The result shows that the proposed framework can effectively estimate SOH with the maximum relative error of 4.5% and describe the aging trend of battery pack based on big data platform.
Hydrogen produced from CH4–CO2 reforming by an optimized rotating gliding arc discharge plasma reactor is investigated in this study. The effect of CH4/CO2 ratio (mole ratio), total input flow rate, ...discharge gap, voltage, and discharge frequency are analyzed. The results show that H2 yield increases with the increase of CH4/CO2 ratio. Arc can be stretched effectively by increasing total input flow rate, then the discharge region is enlarged. Increasing discharge gap can enlarge the discharge region, but the reaction of the gas mixture would be suppressed if the discharge region was excessively large. The discharge region decreases with the increased discharge frequency to a certain degree. Based on the experimental results, the optimal experimental condition is concluded as applied voltage 60 V, discharge frequency 20 kHz, and minimum discharge gap 3 mm. It is anticipated that the results would serve as a good guideline to the application of hydrogen production from hydrocarbon fuels by plasma reforming onboard.
•An optimized rotating GAD plasma reactor is designed for CH4–CO2 reforming.•Performance comparisons of different GAD plasma reactors are made.•Experimental investigations of hydrogen production are carried out.•The effects of different characteristics on the reaction are discussed.
Mn4+-activated fluoride phosphors are promising luminescent materials for use in phosphor-converted white-light-emitting diodes (wLEDs). However, poor water resistance in high humidity environment, ...one of critical drawbacks of this type of phosphors, has limited their practical applications. To address this issue, we developed a new facile strategy to passivate the surface of phosphors by in situ constructing a dual Mn4+-rare protective layer using well-known aminocarboxyl complexants with mild reducibility and strong complexation property. Ethylenediamine tetraacetic acid (EDTA) was taken as a representative for the research in detail, and a typical Mn4+-doped fluorosilicate phosphor K2SiF6:Mn4+ (KSFM) was selected to demonstrate the reliability of the proposed strategy. The surface passivation was achieved by simply dispersing KSFM in a saturated EDTA aqueous solution (KSFM-sEDTA) or a hydrofluoric acid solution containing a certain amount of EDTA (KSFM-EDTA), and then stirring for a short time at room temperature. The analysis results indicate that the dual Mn4+-rare surface layer is composed of a K2SiF6 matrix shell and an organic coating of residual EDTA and/or its related compounds, and the layer constructed by Method 2 is more significant than that by Method 1. Both the luminescence performance and the water resistance of the passivated KSFM were greatly enhanced while the luminescence characteristics remained unchanged. When the water-eroded KSFM was re-treated with EDTA, the luminescence was dramatically restored due to removal of the manganese hydrolysates and repair of the crystal surface defects. The wLEDs assembled with KSFM-EDTA and KSFM-sEDTA phosphor exhibited intense white light with high luminous efficiency (LE, 170.61 lm/W and 162.51 lm/W), high color rendering index (CRI, 88.3 and 85.9), and low correlated color temperature (CCT, 3680 K and 3805 K), respectively. More importantly, the fabricated wLEDs retained high LE (90.6% and 88.6%) and no remarkable changes of CRI and CCT values after being aged at high temperature and humidity (85 °C, 85%) for 550 h, which are more superior to the wLED assembled with the pristine KSFM.
Mn4+-activated fluorosilicate phosphors were passivated by simply dispersing the phosphors in a saturated complexone aqueous solution or a hydrofluoric acid solution containing a complexone. A dual Mn4+-rare protective layer composed of a matrix K2SiF6 shell and an organic coating of the residual complexone and its related compounds was formed in situ, which may collaboratively protect the internal MnF62- groups of the phosphors from hydrolysis. Display omitted
•A dual Mn4+-rare surface protective layer composed of a K2SiF6 matrix shell and an organic coating of the residual complexant and/or its related compounds was formed in situ on the surface of KSFM.•Both the luminescence performance and the water resistance of the passivated KSFM were significantly enhanced while the characteristic luminescence properties remained unchanged.•The manganese hydrolysates of the water-eroded KSFM can be removed and the crystal defects be repaired by the complexants to some extent, thereby dramatically restoring their luminescence.•wLEDs assembled with the passivated KSFM exhibited high photostability and excellent luminous efficiency with high CRI and low CCT.
This appendix provides all necessary materials for the paper 'Linear Dynamics-embedded Neural Network for Long-Sequence Modeling', including model details, experimental configurations, and PyTorch ...implementation.
Although deep learning-based methods have shown great success in spatiotemporal predictive learning, the framework of those models is designed mainly by intuition. How to make spatiotemporal ...forecasting with theoretical guarantees is still a challenging issue. In this work, we tackle this problem by applying domain knowledge from the dynamical system to the framework design of deep learning models. An observer theory-guided deep learning architecture, called Spatiotemporal Observer, is designed for predictive learning of high dimensional data. The characteristics of the proposed framework are twofold: firstly, it provides the generalization error bound and convergence guarantee for spatiotemporal prediction; secondly, dynamical regularization is introduced to enable the model to learn system dynamics better during training. Further experimental results show that this framework could capture the spatiotemporal dynamics and make accurate predictions in both one-step-ahead and multi-step-ahead forecasting scenarios.
This article proposes a new objective function based on sparse regularization for damage identification on functionally graded materials using an improved experience-based learning algorithm. The new ...objective function is developed to improve the accuracy and robustness of optimization analysis and thus to obtain better performance for damage identification. Functionally graded beam specimens of poly(methyl methacrylate) polymers with different damage situations are fabricated to reveal the effectiveness and robustness of the proposed method. Moreover, the influence of artificial noise on the identification performance is investigated. The damage identification results from both numerical and experimental validation on poly(methyl methacrylate) polymer specimens demonstrate the superiority of the new objective function for damage identification of functionally graded materials compared with the traditional objective function.
Delamination in composite laminates reduces the structural stiffness and thus causes changes in the vibration responses of the laminates. Therefore, it is feasible to employ dynamic characteristics ...(such as natural frequencies and mode shapes) for delamination detection by using an optimization method. In the present study, a two-step method is proposed for the delamination detection in composite laminates using an experience-based learning algorithm. In the first step, one-dimensional equivalent through-thickness beam elements are employed to model the composite laminated beam and potential delamination locations are identified. In the second step, a typical three-dimensional finite mesh is utilized for the beam’s modeling and the detailed delamination information (including the delamination location, size, and interface layer) is detected. This two-step method combines the advantages of the two different modeling techniques and is able to significantly reduce the computational cost without reducing detection accuracy. The proposed method is applied for an eight-layer quasi-isotropic symmetric (0/-45/45/90)s composited beam with different delamination situations to verify its effectiveness and robustness. The performance of the two-step method is demonstrated by comparing with the one-step method and other three state-of-the-art algorithms (CMFOA, PSO, and SSA). Moreover, the influence of artificial noise on the accuracy of the detection performance is also investigated. Both numerical and experimental results confirm the superiority of the proposed method for delamination detection in composite laminates especially for the prediction of delamination interface.
Programmed cell death protein 1 (PD-1) inhibitor plus apatinib is reported to be a promising strategy for advanced cancers. Moreover, a PD-1 inhibitor or apatinib exerts a certain efficacy in ...advanced colorectal cancer (CRC), whereas their synergistic effect is unclear. This study aimed to evaluate the treatment efficacy and safety of a PD-1 inhibitor plus apatinib in advanced CRC patients.
In total, 45 advanced CRC patients who received a PD-1 inhibitor plus apatinib (PD-1 inhibitor plus apatinib group, N=20) or apatinib monotherapy (apatinib group, N=25) as third-line therapies were enrolled in the current study.
The objective response rate (20.0% vs. 8.0%) (
=0.383) and disease control rate (70.0% vs. 52.0%) (
=0.221) were numerically increased in the PD-1 inhibitor plus apatinib group, respectively, compared with the apatinib group, but no statistical significance was observed. The median progression-free survival (PFS) was 7.5 versus 4.8 months; the 1-year PFS rate was 32.5% versus 9.9%; the median overall survival (OS) was 12.3 versus 8.7 months; and the 1-year OS rate was 50.7% versus 27.0% in the PD-1 inhibitor plus apatinib group versus the apatinib group, respectively. PFS (
=0.038) and OS (
=0.048) were prolonged in the PD-1 inhibitor plus apatinib group compared with the apatinib group. PD-1 inhibitor plus apatinib (versus apatinib) was independently associated with longer PFS (
=0.012) and OS (
=0.009). The majority of the adverse events were of grade 1-2, wherein the incidence was similar between groups, except for the fact that the incidence of capillary proliferation was elevated in the PD-1 inhibitor plus apatinib group compared with the apatinib group (25.5% versus 0.0%) (
=0.013).
PD-1 inhibitor plus apatinib presents a potential improvement in efficacy and survival benefit compared with apatinib monotherapy, with tolerable safety in advanced CRC patients.
The time-correlated single-photon-counting (TCSPC) three-dimensional (3D) imaging lidar system has broad application prospects in the field of low-light 3D imaging because of its single-photon ...detection sensitivity and picoseconds temporal resolution. However, conventional TCSPC systems always limit the echo photon flux to an ultra-low level to obtain high-accuracy depth images, thus needing to spend amounts of acquisition time to accumulate sufficient photon detection events to form a reliable histogram. When the echo photon flux is increased to medium or even high, the data acquisition time can be shortened, but the photon pile-up effect can seriously distort the photon histogram and cause depth errors. To realize high accuracy TCSPC depth imaging with a shorter acquisition time, we propose a high-flux fast photon-counting 3D imaging method based on empirical depth error correction. First, we derive the photon flux estimation formula and calculate the depth error of our photon-counting lidar under different photon fluxes with experimental data. Then, a function correction model between the depth errors and the number of echo photons is established by numerical fitting. Finally, the function correction model is used to correct depth images at high photon flux with different acquisition times. Experimental results show that the empirical error correction method can shorten the image acquisition time by about one order of magnitude while ensuring a moderate accuracy of the depth image.
