Accuracy and speed of Lithium-ion battery state of charge (SOC) estimation determine the reliability and stability of electric vehicle (EV), as well as promoting the development of smart home energy management system. Existing SOC estimation approaches embedded in commercial EVs still suffer from limitations of low precision, effectiveness, and relatively low robustness. To address these issues, we propose an internal cascaded neuromorphic computing system (ICNCS) via memristor circuits for EV SOC estimation. Specifically, we use three circuit modules to facilitate the design of the proposed ICNCS. Firstly, a bidirectional gated recurrent unit (Bi-GRU) circuit module is designed, enabling adequate feature extraction from the time-contextual battery information. Secondly, an attention circuit module is proposed to distinguish the useful and unimportant information related to SOC estimation. Thirdly, the Kalman filter module is constructed to eliminate the random noise caused by data transmission and processing. Finally, a series of experiments and analysis demonstrate that the proposed ICNCS has good performances in terms of accuracy (the lowest achievable RMSE and MAE are 0.853 and 0.711, respectively), time efficiency (approximately 16 20 times), and robustness (anti-noise capacity), indicating an advancement in consumer electronics applications.

Traffic sign detection is of great significance to the development of the Intelligent Transportation System (ITS) as a database for environmental awareness. The main challenges of existing traffic sign detection method are inaccurate small object detection, difficult mobile deployment, and complex working environment. Based on these, a vehicle-mounted adaptive traffic sign detector (VATSD) for small-sized signs in multiple working conditions is proposed in this paper. First, the Backbone of the detector is optimized. A feature tight fusion structure is designed to constitute a new feature extraction module, DCSP, which improves the feature extraction capability and the detection accuracy of small objects with negligible additional parameters. Second, an image enhancement network IENet with an adaptive joint filtering strategy is proposed. The IENet enables the dynamic selection of filters and thus adaptively optimizes low-quality images under multiple conditions to improve the accuracy of subsequent detection tasks. The proposed method has experimented on three traffic sign datasets and the detection accuracy increased by up to 7.6% compared to the original. The proposed detector demonstrates superiority over other state-of-the-art (SOTA) methods in terms of small object detection accuracy, detection speed, and environmental adaptability. Further, we deployed VATSD to Jetson Xavier NX and achieved a detection speed of 21.6 FPS, meeting real-time requirements.

With the rapid adoption of Internet of Things (IoT) and artificial intelligence (AI), lithium-ion battery state-of-health (SOH) estimation plays an important role in guaranteeing the secure and stable functioning of various domains. However, the majority of the existing methods are constrained by factors, such as transmission latency, computational energy, and computing speed. To address these challenges, we develop a time-frequency hybrid neuromorphic computing architecture for battery SOH estimation. Specifically, an eco-friendly, biodegradable memristor crossbar array is designed, enabling high-energy efficiency and high-performance density in the proposed system. To improve the understanding of the designed time-frequency hybrid neuromorphic computing system, a local information extraction module, a time-frequency feature fusion module, and a global information perception module are proposed. Furthermore, the proposed system is validated on two publicly available battery ageing data sets (i.e., the CALCE-CS2 data set and the National Aeronautics and Space Administration data set). The experimental results show that the system exhibits superior performance to that of the state-of-the-art (SOTA) methods in terms of estimation accuracy (highest estimation accuracy), time consumption (approximately 8–12 times faster), and transmission latency (approximately 10 times faster). This study is expected to promote the advancement and evolution of next-generation computing systems, enabling the realization of low-power consumption and high-density information processing in IoT scenarios.

Remaining useful life (RUL) early prediction of lithium-ion battery is crucial to develop advanced battery health management and complete security assessment. However, most of existing methods still suffer from two limitations, i.e., the inadaptability to the different data distribution and the inability to capture the relationship between input series and RUL, which always make the RUL early prediction difficult and challengeable. To address these issues, this paper proposes a parallel feature fusion network (PFFN) for RUL early prediction of lithium-ion battery. Firstly, a feature selection strategy is designed to filter the optimal feature sets (containing cycle statistical features and domain knowledge-based features) that are most related to RUL of lithium-ion battery. Secondly, two specific Transformer encoders connected in parallel configuration are developed to integrate the cycle statistical features and domain knowledge-based features, respectively, achieving original RUL early prediction results. Furthermore, the Bayesian optimization is applied for global iterative optimization, aiming to enhance the prediction accuracy and generalization capability. A series of experiments are conducted with different data distributions. Experimental results demonstrate that the proposed PFFN outperforms the state-of-the-art (SOTA) methods, achieving 6.00%~27.61%, 0.58%~6.49%, and 5.95%~7.03% reduction in Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Score respectively.

The combination of the Metaverse and intelligent transportation systems (ITS) holds significant developmental promise, especially for visual perception tasks. However, the acquisition of high-quality scene data poses a challenging and expensive endeavor. Meanwhile, the visual disparity between the Metaverse and the physical world poses an impact on the practical applicability of the visual perception tasks. In this paper, a Metaverse Intelligent Traffic Visual Framework, MITVF, is developed to guide the implementation of visual perception tasks in the physical world. Firstly, a two-stage metadata optimization strategy is proposed that can efficiently provide diverse and high-quality scene data for traffic perception models. Specifically, an element reconfigurability strategy is proposed to flexibly combine dynamic and static traffic elements to enrich the data with a low cost. A diffusion model-based metadata optimization acceleration strategy is proposed to achieve efficient improvement of image resolution. Secondly, a Meta-Physical adaptive learning method is proposed, and further applied to visual perception tasks to compensate for the visual disparity between the Metaverse and the physical world. Experimental results show that MITVF achieves a 10 $\times$ acceleration in optimization speed, ensuring the image quality and reconstructing diverse. Further, MITVF is applied to the traffic object detection task to verify the effectiveness and validity. The performance of the model trained with 5k real data exceeded that of the model trained with 200k real data, with AP $_{50}$ reaching 67.7%.