Accurate estimating the state-of-charge (SOC) of Li-ion battery contributes significantly to electric vehicle safety. Existing methods typically focus on the traditional recurrent neural networks to encode time series features for SOC estimation. However, these methods rely solely on their own structure to extract time series correlated features, ignoring a significant amount of information on temporal dimension. To address this issue, this paper proposes a temporal transformer-based sequence network (TTSNet) that can make full use of temporal dimensional information to model the relationship between the input and SOC. Specifically, the proposed network splits the raw data into three branches including voltage, current, and temperature, as well as extracts the corresponding primary semantic features. It then uses a temporal transformer to effectively encode the features of temporal dimensional information. The resulting features are further fed into an attention-guided feature fusion module to interact information among voltage, current, and temperature branches for subsequent SOC estimation. To enhance the network's resilience for long time sequences, a sliding time window technique is introduced to pre-process the raw data. Besides, a Kalman filter is incorporated as post-processing to smooth the output to guide a more accurate SOC estimation. Comprehensive experiments are conducted on battery open datasets and vehicle operation datasets to verify the proposed method. The results demonstrate that the proposed method achieves high accuracy and strong robustness in both datasets, with average MAE, RMSE, and $\mathrm{R^{2}}$ values of 0.506%, 0.694%, and 99.791%, respectively. The code is available at https://github.com/haooozi/TTSNet .

Active battery equalization and passive battery equalization are two important methods which can solve the inconsistency of battery cells in lithium battery groups. In this paper, a new hybrid battery equalization strategy combinfigureing the active equalizing method with a passive equalizing method is proposed. Among them, the implementation of the active equalizing method uses the bidirectional Flyback converter and Forward converter. This hybrid equalizing strategy adopts the concept of hierarchical equilibrium: it can be divided into two layers, the top layer is the equalization between groups, and the bottom layer is the equalization of group. There are three active equilibrium strategies and one passive equilibrium strategy. For verification purposes, a series of experiments were conducted in MATLAB 2018b/Simulink platform. The simulation and experiment results show that this hybrid battery equalizing method is efficient and feasible.

A battery management system (BMS) is an important link between on-board power battery and electric vehicles. The BMS is used to collect, process, and store important information during the operation of a battery pack in real time. Due to the wide application of lithium-ion batteries in electric vehicles, the correct estimation of the state of charge (SOC) of lithium-ion batteries (LIBS) is of great importance in the battery management system. The SOC is used to reflect the remaining capacity of the battery, which is directly related to the efficiency of the power output and management of energy. In this paper, a new long short-term memory network with attention mechanism combined with Kalman filter is proposed to estimate the SOC of the Li-ion battery in the BMS. Several different dynamic driving plans are used for training and testing under different temperatures and initial errors, and the results show that the method is highly reliable for estimating the SOC of the Li-ion battery. The average root mean square error (RMSE) reaches 0.01492 for the US06 condition, 0.01205 for the federal urban driving scheme (FUDS) condition, and 0.00806 for the dynamic stress test (DST) condition. It is demonstrated that the proposed method is more reliable and robust, in terms of SOC estimation accuracy, compared with the traditional long short-term memory (LSTM) neural network, LSTM combined with attention mechanism, or LSTM combined with the Kalman filtering method.

Accurately predicting the remaining useful life (RUL) of lithium-ion batteries (LIBs) is crucial for ensuring the safety and health maintenance of electric vehicles (EVs). Although conventional Transformers have demonstrated superior performance in various scenarios, they encounter significant challenges when applied to RUL estimation. Specifically, these models often lack the ability to decompose and interpret raw temporal data effectively, leading to underutilization of critical information. In addition, the prolonged prediction times associated with these models can increase the risks associated with safe battery operation. To address these issues while preserving the strengths of Transformer-based temporal modeling, this article introduces a novel exponential Transformer designed to learn interpretable temporal features. Given the declining trend in battery capacity data and the presence of fluctuating information, this article first extracts the level component and the fluctuation component from the original data, thereby decomposing the capacity information into interpretable time-series features. Furthermore, we propose an exponential attention mechanism to replace the self-attention mechanism, which enhances the RUL prediction accuracy and efficiency by focusing on similar data features through exponential decay. Extensive experiments conducted on several aging datasets demonstrate that the proposed network achieves effective results in both single-step and multistep prediction tasks. Compared with other well-established methods, our method reduces the mean absolute error (MAE) and root mean square error (RMSE) by an average of 81.61% and 77.76%, respectively. The code will be made publicly available at: https://github.com/wch1121/RUL-Prediction.

A memristor is a nanoscale electronic element that displays a threshold property, non-volatility, and variable conductivity. Its composite circuits are promising for the implementation of intelligence computation, especially for logic operations. In this paper, a flexible logic circuit composed of a spintronic memristor and complementary metal-oxide-semiconductor (CMOS) switches is proposed for the implementation of the basic unbalanced ternary logic gates, including the NAND, NOR, AND, and OR gates. Meanwhile, due to the participation of the memristor and CMOS, the proposed circuit has advantages in terms of non-volatility and load capacity. Furthermore, the input and output of the proposed logic are both constant voltages without signal degradation. All these three merits make the proposed circuit capable of realizing the cascaded logic functions. In order to demonstrate the validity and effectiveness of the entire work, series circuit simulations were carried out. The experimental results indicated that the proposed logic circuit has the potential to realize almost all basic ternary logic gates, and even some more complicated cascaded logic functions with a compact circuit construction, high efficiency, and good robustness.

In this paper, we propose a robotic arm grasping system suitable for complex environments. For a robotic arm, in order to achieve its accurate grasp of the target object, not only the vision but also a certain tracking ability should be provided. To improve the grasp quality, we propose a robotic arm grasping system using YOLOv4 combined with a particle filter (PF) algorithm, which can be applied in a nonlinear and non-Gaussian environment. Firstly, the coordinates of the bounding box in the image can be obtained through the YOLOv4 object detection algorithm. Secondly, the coordinates in the world system can be obtained through the eye-to-hand calibration system. Thirdly, a PF model can be established based on the coordinate changes of the target object. Finally, according to the predicted output of the PF, the robotic arm and the target object can reach the specific position at the same time and complete the grab. As the target object, the bowl is applied to experiments for the sake of achieving a more convincing demonstration. The experimental results show that the robotic arm grasping system proposed in this paper can accomplish the successful grasp at a rate of nearly 88%, even at a higher movement speed, which is of great significance to robot applications in various fields.