The large-scale grouping of the battery system leads to the inconsistency of the battery pack. Aiming at tacking this issue, an inconsistency evaluation method is deployed for the battery pack based on an improved Gaussian mixture model (GMM) and feature fusion approach. Specifically, the proposed adaptive forgetting factor recursive least squares (AFFRLS) algorithm allows the open-circuit voltage and other parameters to be jointly identified without the open circuit voltage-state of charge (OCV-SOC) test. An online capacity estimation approach with the extended Kalman particle filter (EPF) is put forward for capacity estimation. Further, an improved GMM is proposed to visualize battery pack inconsistency, using the K-means++ algorithm to initialize category centers. The standard deviation coefficient approach quantifies the inconsistency. Finally, the real-life vehicle data are performed to validate the effectiveness of the proposed method. The experimental results show that the proposed method can evaluate the battery parameters accurately. With the increase in service time, the inconsistency of the battery pack is gradually deteriorating.

In view of the shortcomings of Probabilistic Roadmaps (PRM) in the case of narrow passages, an improved method based on optimal sampling strategy is proposed. By sampling the dense area of the obstacles, the sampling points distributed inside the obstacle are selected and uniformly sampled by the distanced, so that the sampling point is generated around the obstacle in the free area, thereby increasing the number of sampling points in the narrow passages. The simulation results show that the improved PRM has more sampling points in the narrow passages than the standard PRM. And the time of path planning, the success rate and the path length are also significantly improved.

Because of uncertain random noise and unknown disturbance, the observer‐based inverter fault diagnosis meets the problems of large deviation of current observation value and high false alarm rate. An inverter open‐circuit fault diagnosis method is proposed via residual performance evaluation. Firstly, the interval sliding mode observer is constructed by combining the interval observer with the sliding mode observer. Three phase current estimates are obtained and current residuals are calculated. Secondly, the residual performance indicators are calculated using the evaluation function. The fault detection is derived by comparing residual performance with the threshold. Thirdly, a residual information table is established based on the fault information contained in the residual for fault isolation. Finally, the proposed algorithm is validated on simulation and physical platforms. Compared with traditional observer‐based diagnostic methods, the introduction of residual performance evaluation improves the diagnostic speed by more than 50%. The interval sliding mode observer has higher estimation accuracy and anti‐interference ability in the presence of noise and external disturbances.

T-type three-level inverters have been extensively utilized in renewable energy generation, motor drive systems, and other power conversion applications. However, failures in semiconductor devices critically reduce the operational reliability of power conversion systems. While significant progress has been made in the diagnosis of single-switch open-circuit (OC) faults, the precise location and detection of simultaneous double-switch OC faults remain challenging. Therefore, this paper proposes a fault diagnosis method, integrating an improved adaptive sliding mode observer (IASMO) and dynamic current threshold detection. First, the IASMO is constructed through the hybrid logic dynamic model, achieving accurate and rapid estimation of phase currents. Then, integrating estimated with actual currents accomplishes the design of detection variables and adaptive thresholds. Subsequently, fault location variables are formulated to achieve accurate localization of both single-switch and double-switch faults. Finally, Simulation and experimental results demonstrate that the proposed method effectively identifies 18 types of OC faults within 75% of the current cycle, with high efficiency and robustness.

To deal with the problem of multitarget tracking with measurement origin uncertainty, the paper presents a multitarget tracking algorithm based on Adaptive Network Graph Segmentation (ANGS). The multitarget tracking is firstly formulated as an Integer Programming problem for finding the maximum a posterior probability in a cost flow network. Then, a network structure is partitioned using an Adaptive Spectral Clustering algorithm based on the Nyström Method. In order to obtain the global optimal solution, the parallel A* search algorithm is used to process each sub-network. Moreover, the trajectory set is extracted by the Track Mosaic technique and Rauch–Tung–Striebel (RTS) smoother. Finally, the simulation results achieved for different clutter intensity indicate that the proposed algorithm has better tracking accuracy and robustness compared with the A* search algorithm, the successive shortest-path (SSP) algorithm and the shortest path faster (SPFA) algorithm.