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.

This article discusses the process for creating a dataset to train and test the core and the corrector AI classifiers of the Arch-I-Scan Project. This dataset includes the photographs of real sherds that were compiled by the project from the collections of our project partners as well as synthetic sherds that were generated to supplement the low numbers of real sherd training data and to account for the imbalances among the numbers of real sherds per class (i.e., per vessel form). This article presents the simulation process used to create the synthetic sherds, as well as the process we used to decrease the differences between images of real and simulated sherds so that they were useable together for training the machine. Finally, the compilation and size of the resulting overall dataset are outlined.