Accurate estimations of State-of-Charge (SOC) and State-of-Health (SOH) are crucial for ensuring the safe and efficient operation of lithium-ion batteries in Battery Management Systems (BMSs). This paper proposes a novel joint estimation method integrating an Autoregressive Equivalent Circuit Model (AR-ECM) with a data-driven model to address the strong coupling between SOC and SOH. First, a multi-strategy improved Ivy algorithm (MSIVY) is utilized to optimize the hyperparameters of a Hybrid Kernel Extreme Learning Machine (HKELM). Key voltage interval features, including split voltage, differential capacity, and current–voltage product, are extracted and filtered using a sliding window approach to enhance SOH prediction accuracy. The estimated SOH is subsequently incorporated into the AR-ECM state-space equations, where an enhanced particle swarm optimization algorithm optimizes the model parameters. Finally, the Extended Kalman Filter (EKF) is applied to achieve collaborative SOC–SOH estimation. Experimental results demonstrate that the proposed method achieves SOH errors below 1% and SOC errors under 2% on public datasets, showcasing its robust generalization capability and real-time performance.

With the widespread use of lithium-ion batteries (LIBs) in electric vehicles, it is essential for battery management systems (BMS) to accurately estimate the state of health (SOH) of the battery. This study constructs a regression estimation model applicable to the SOH of LIBs by combining multi-feature signal analysis with a data-driven approach. First, the collected data were smoothed using advanced filtering methods to extract input features characterising capacity degradation properties from differential thermal voltammetry (DTV) curves, incremental capacity analysis (ICA) curves, meantime decay (MTD) curves, and terminal thermal analysis (TTA) curves. Then, the extracted features are subjected to feature fusion using principal component analysis (PCA) to obtain strongly correlated features with a cumulative contribution of 98% or more as inputs to the data-driven model and the hyperparameters of the group method of data handling (GMDH) network are optimised using simulated annealing genetic algorithm (SAGA). Finally, model construction, validation, and comparison are carried out using NASA and Oxford battery datasets. The RMSE between the actual measured value and the estimated value is obtained to be less than 0.3%, and the R ² value is as high as 0.99. The results demonstrate that the approach can efficiently catch the phenomenon of capacity regeneration. It is valid and robust for SOH estimation for different types of LIBs.

In this paper, we propose a novel color-texture image segmentation method based on local histograms. Starting with clustering-based color quantization, we extract a sufficient number of representative colors. For each pixel, through counting the number of pixels with each representative color within a circular neighborhood, a local histogram is obtained. After the circular neighborhood is extended to several scales, a local histogram with an appropriate scale is adopted as a color-texture descriptor at the corresponding pixel for image segmentation. Further, we correct the color-texture features near boundaries and obtain a initial segmentation by a clustering method with the color-texture descriptors. Finally, in order to obtain a better segmentation result, we merge the over segmented regions guided by the obtained boundaries. Experiments are performed on both synthetic and natural color-texture images, and the results show that our proposed method performs much better compared with state-of-the-art methods on image segmentation, particularly in textured areas.

Smoothing the multi-scale textures with strong gradient while maintaining the weak structures is still a challenging work for the existing texture filtering methods. In this paper, we propose a variant of the bilateral filter to address this issue. Unlike the classical bilateral filter, which usually uses a fixed range kernel for texture filtering, the variant uses an adaptive range kernel, that is, if a pixel is located near a structure, the range kernel assigned to the pixel is small, otherwise, the range kernel is large. This mechanism ensures the preservation of sharp edges. In order to perform structure measurement, we propose a local histogram operator, which can identify structures from textures by comparing the difference in color distribution between the left and right half neighboring windows centered on the pixel of interest. In addition, after once bilateral filtering, instead of multiple iterations, a novel anisotropic filter is designed to remove residual textures, which can avoid edge blurring to some extent. Experimental results demonstrate the effectiveness of our method by comparing it with the state-of-the-art methods in preserving weak structures as well as suppressing textures with strong gradient or varying scales.

In close-range photogrammetry, circular-coded targets (CCTs) are a reliable method to solve the issue of image correspondence. Currently, the identification methods for CCTs are very mature, but complex illumination conditions are still a key factor restricting identification. This article proposes an adaptive homomorphic filtering (AHF) algorithm to solve this issue, utilizing homomorphic filtering (HF) to eliminate the influence of uneven illumination. However, HF parameters vary with different lighting types. We use a genetic algorithm (GA) to carry out global optimization and take the identification result as the objective function to realize automatic parameter adjustment. This is different from the optimization strategy of traditional adaptive image enhancement methods, so the most significant advantage of the proposed algorithm lies in its automation and universality, i.e., users only need to input photos without considering the type of lighting conditions. As a preprocessing algorithm, we conducted experiments combining advanced commercial photogrammetric software and traditional identification methods, respectively. We cast stripe- and lattice-structured light to create complex lighting conditions, including uneven lighting, dense shadow areas, and elliptical light spots. Experiments showed that our algorithm significantly improves the robustness and accuracy of CCT identification methods under complex lighting conditions. Given the perfect performance under stripe-structured light, this algorithm can provide a new idea for the fusion of close-range photogrammetry and structured light. This algorithm helps to improve the quality and accuracy of photogrammetry and even helps to improve the decision making and planning process of photogrammetry.