Fast-charging technology for lithium-ion batteries is of great significance in reducing charging time and enhancing user experience. However, during fast charging, the imbalance among battery cells can affect the overall performance and available capacity of the battery pack. Moreover, the charging efficiency not only is limited by the battery technology itself but is also closely related to the optimization of the charging strategy. To address the optimization of fast charging for lithium-ion batteries, this paper proposes a method based on deep reinforcement learning. First, a deep reinforcement learning charging optimization model is constructed, aiming to minimize charging time and SOC balancing cost, with constraints on battery voltage, temperature, SOC, and SOH. The model employs the deep deterministic policy gradient (DDPG) algorithm integrated with reward centralization and entropy regularization mechanisms, aiming to dynamically adjust the charging current to achieve an optimal balance between fast charging and battery health. Experimental results indicate that the proposed method enhances charging efficiency, contributes to extending battery life, and supports the safety of the charging process. Compared to the traditional constant-current constant-voltage (CCCV) strategy, the improved DDPG strategy reduces the total charging time by 60 s and the balancing time from 540 s to 470 s. Furthermore, compared to the basic DDPG method, the proposed algorithm shows a clear advantage in charging efficiency.

In classification tasks, the algorithms heavily depend on big data, yet data category imbalance hinders the model’s ability to adequately learn from scarce class samples, impacting its overall learning capacity. This paper addresses the challenge of data imbalance in data-driven classification problems, which introduce an Improved Finite Elements-Synthetic Minority Oversampling Technique (IFE-SMOTE) for data balancing. This method triangulates the sample space leveraging FE-SMOTE, designates three strategic points within each split element based on defined rules, and synthetically generates samples within the linear vicinity of these points. The mathematical expectation of generated samples aligns with the triangle center of the original minority samples, while their variance closely mirrors that of the triangle center, ensuring statistical consistency with the original data. The corresponding theorem is provided and its validity is proved. Numerical experiments confirm the effectiveness of the proposed IFE-SMOTE method. The IFE-SMOTE algorithm classified eight datasets and compared them with other oversampling algorithms using G-mean, F-measure, and AUC. IFE-SMOTE’s average scores were 0.9273, 0.9754, and 0.9309, respectively, outperforming FE-SMOTE by 0.0268, 0.0059, and 0.0253, and other algorithms’ averages by 0.0493, 0.0169, and 0.0402.

This paper addresses real-time task allocation and path conflict resolution for heterogeneous agents in large-scale automated guided vehicle (AGV) systems within smart manufacturing. To overcome limitations of existing methods in dynamic and high-density environments, we propose a distributed market-based task allocation algorithm integrated with a cooperative multi-AGV obstacle avoidance strategy to ensure coordinated motion planning. A dynamic task buffer pool suppress system oscillations caused by frequent task arrivals. In addition, an incremental path caching mechanism is developed to reduce redundant computations during the 2-opt optimization process. The proposed Two-level priority mechanism effectively resolves multi-AGV collaborative obstacle avoidance and path conflict issues. Compared with existing approaches, the proposed method maintains robust conflict resolution among multiple AGVs while reducing task replanning frequency and computational cost. A standardized task allocation comparison framework is constructed to evaluate system performance under varying task scales. Simulation results based on real manufacturing layouts validate the feasibility and effectiveness of the proposed hybrid algorithm. The results provide practical guidance for efficient task scheduling and collaborative obstacle avoidance in industrial AGV systems.