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With the development of extreme fast charging technology, charging stations need to use energy storage stations to reduce the rising peak to average power ratio (PAPR). Lithium-ion capacitor (LIC) is a chemical power source that uses both Faraday process and non-Faraday process to store energy. Because of its attractive performance in terms of rate characteristics and chemical stability, it is suitable for some energy storage stations that consider both power density and energy density. It is important to describe the current-voltage characteristics of LIC to predict the charge and discharge efficiency in the early design of energy storage power stations. During the test, however, a full discharge or charge results in a high temperature rise, and the electrical model parameters near a specific temperature point cannot be accurately obtained. The short current pulses cannot stabilize the polarization. In this paper, a high-accuracy parameters identification method based on an improved Butler-Volmer-Equation-Based electrical model is used to summarize the phenomena caused by the rate of change of high-energy LIC. The accuracy of the method is tested under the dynamic stress condition test. The maximum voltage error is less than 2%. Energy efficiency calculation based on the used model is simulated by the design condition from the energy storage station of the Haizhu line in Guangzhou. The maximum error is less than 0.2%.

Hyperspectral images with very high resolution (VHR-HSI) have become considerably valuable due to their abundant spectral and spatial details. Classification of hyperspectral images (HSIs) is a basic and important procedure for diverse applications. However, low interclass spectral variability and high intraclass spectral variability in VHR-HSI, shadows, pedestrians, and low signal-to-noise ratio increase the fuzziness of different categories. To address the known challenges of VHR-HSI classification, an effective classification method based on encoder–decoder architecture is proposed. The proposed algorithm is an object-level contextual convolution neural network based on an improved residual network backbone with 3-D convolution, which fully considers the spatial–spectral and contextual features of HSIs. Two different spatial resolution aerial HSIs are used as experimental data. The results show that the overall accuracy of the proposed method is improved by 7.42% and 18.82%, respectively, compared to the pixelwise convolution neural network and DeepLabv3 algorithm, which is extraordinarily suitable for HSI classification with very high spatial resolution.

The perspective distortion of oblique images has a substantial impact on dense matching, i.e., it reduces the matching precision. In this article, a strategy of dense matching in which the object plane direction is considered is proposed. According to many regular planes in urban areas, epipolar rectification with minimum distortions relative to the selected reference planes can be generated. The matching results of epipolar images relative to various reference planes are weighted and fused into a single depth map, which is a better matching result. The experimental results demonstrate that the perspective distortion has a substantial influence on the dense matching performance. The root-mean-square error (RMSE) of the flatness for horizontal objects is increased by approximately 30%, and the RMSE of the flatness for facades is increased by approximately 40%. Hence, the proposed matching strategy, in which the object plane is considered, can effectively improve the matching results.

Common depth image fusion methods use each original image as a reference plane and fuse the depth images using mutual projection. These methods can eliminate inconsistency between the depth images, but they cannot alleviate the point cloud redundancy and computational complexity. This article proposes a virtual view method for depth image fusion, defines a limited number of virtual views by means of view clustering, reduces the redundant calculations, and covers all scenes as much as possible. The depth image is merged ray by ray, and a reliable depth value is obtained via the F-test. Compared with the modified semiglobal matching (TSGM) stereo dense matching algorithm, the accuracy is improved by approximately 50% and the roughness is improved by approximately 50%. Compared with the classic surface reconstruction (SURE) fusion algorithm, there is more fusion depth value in each ray, and the accuracy and roughness are slightly improved. In addition, the algorithm of this article greatly reduces the number of reference planes.

The existence of the consistency degradation of the battery pack hinders the accurate estimation of pack capacity and cell capacity in the battery pack. The paper focuses on the capacity estimation of cells in the serial battery pack. The shape invariance of the charging voltage curve is discussed and used as the theoretical foundation of cell capacity difference identification. The matching relationship between two voltage curves is obtained based on the dynamic time warping algorithm. Then the capacity difference identification algorithm to calculate the capacity difference between the two cells is proposed. Based on the algorithm, a three-step capacity estimation method is established. The proposed method can only use the previous charging curve of one cell in the pack and the current charging data of the battery pack to rapidly estimate the capacity of each cell in the battery pack. A 16 serial LiFePO4 battery pack is employed to verify the method. The result shows the estimation error of cell capacities is less than 3% rated capacity. With this method, the cell capacities in the pack can be rapidly and accurately estimated, providing a foundation for the consistency analysis and equalization of the battery pack.

Timely, spatially complete, and reliable forest above-ground biomass (AGB) data are a prerequisite to support forest management and policy formulation. Traditionally, forest AGB is spatially estimated by integrating satellite images, in particular, optical data, with field plots from forest inventory programs. However, field data are limited in remote and unmanaged areas. In addition, optical reflectance usually saturates at high-density biomass level and is subject to cloud contaminations. Thus, this study aimed to develop a deep learning based workflow for mapping forest AGB by integrating Landsat 8 and Sentinel-1A images with airborne light detection and ranging (LiDAR) data. A reference AGB map was derived from the wall-to-wall LiDAR data and field measurements. The LiDAR plots—stratified random samples of forest biomass extracted from the LiDAR simulated strips in the reference map—were adopted as a surrogate for traditional field plots. In addition to the deep learning model, i.e., stacked sparse autoencoder network (SSAE), five different prediction techniques including multiple stepwise linear regressions, K-nearest neighbor, support vector machine, back propagation neural networks, and random forest were individually used to establish the relationship between LiDAR-derived forest biomass and the satellite predictors. Optical variables (Landsat 8 OLI), SAR variables (Sentinel-1A), and their combined variables were individually input to the six prediction models. Results showed that the SSAE model had the best performance for the forest biomass estimation. The combined optical and microwave dataset as explanatory variables improved the modeling performance compared to either the optical-only or microwave-only data, regardless of prediction algorithms. The best mapping accuracy was obtained by the SSAE model with inputs of optical and microwave integrated metrics that yielded $R^{2}$ of 0.812, root mean squared error (RMSE) of 21.753 Mg/ha, and relative RMSE (RMSE r ) of 14.457%. Overall, the SSAE model with inputs of combined Landsat 8 OLI and Sentinel-1A information could result in accurate estimation of forest biomass by using the stratification-sampled and LiDAR-derived AGB as ground reference data. The modeling workflow has the potential to promote future forest growth monitoring and carbon stock assessment across large areas.