Accurate state estimation is necessary for battery management systems in electric vehicles (EVs) to deploy appropriate control policy, thus the safety of the battery pack can be ensured and lifespan can be prolonged. Current state estimation methods cannot fully exploit the battery multi-sensor data from a multi-timescale perspective, which results in deteriorating estimation performance in laboratory testing data, let alone real-world application scenarios. To overcome the above drawbacks, this paper proposes a deep neural network-based state-of-charge (SoC) and state-of-health (SoH) co-estimation framework which could realize accurate estimation in both laboratorial and realistic scenes. To realize multi-sensor data fusion, the original data is rearranged into a two-dimensional matrix with one dimension representing the time domain and the other representing the feature domain. To exploit the multi-timescale changing properties related to SoC and SoH, convolutional filters with different sizes are used to extract features in different timescales. Furthermore, the swish activation function and long short-term memory layer are introduced to enhance the network convergence and estimation accuracy. The global average pooling layer is adopted to substitute the traditional fully-connected layer for network lightweight. Oxford public battery dataset and real-world EV battery operational data are used to verify the applicability of the proposed method. Results show that the SoC and SoH estimation errors are 1.43% and 1.59% respectively for the Oxford dataset, which is superior to many existing advanced machine learning models. In addition, the SoC and pseudo-SoH estimation errors in real-world EV driving scenarios are 0.79% and 2.59% respectively, further verifying the accuracy and generalization capability of the proposed method.

Time-sensitive networking (TSN) has revolutionized Ethernet with real-time and deterministic transmission capabilities, making it one of the most potential solutions for future vehicular and industrial networks. Compared to time-aware shaper (TAS), the cyclic queuing and forwarding (CQF) protocol simplifies the gate control list (GCL) configuration process, reducing the deployment difficulty of TSN in large-scale networks. Much research has proposed incremental scheduling approaches for the CQF. However, existing methods often inadequately consider and insufficiently integrate network and flow characteristics, limiting scheduling performance. This paper introduces a novel CQF scheduling method, IRFS, which integrates queue resources and flow features for efficient searching of scheduling priority, routing path, and start offset. A priority sorting function is proposed that deeply combines network and flow characteristics while considering both spatial and temporal resource allocation. IRFS achieves efficient scheduling and load balancing by constructing combinations of (flow, path, offset), where the elements respectively represent flow features, the spatial distribution, and the temporal distribution of resources. The IRFS is validated in different network scenarios, including simple, complex, and In-Vehicle Networking (IVN) settings. It is compared against other state-of-the-art CQF scheduling algorithm. The IRFS demonstrates superior performance in scheduling success rate, load balancing, and computation time across these scenarios.

Autonomous driving technology and platooning driving technology are important directions for the development of intelligent and connected vehicles. Aiming at the motion control problem of autonomous vehicle platoon, this paper proposes a multilayer predictive control framework (MPCF) based on heuristic learning agent and improved distributed model. Firstly, the leading autonomous vehicle and following heterogeneous vehicles are modeled respectively, and the motion control problem of autonomous platoon is described. Then, the multilayer motion control framework is designed, which contains highly automated tracking control optimization for the leading vehicle (LV) and high-precision formation keeping optimization for the following vehicles (FVs). In the upper layer, the heuristic Dyna algorithm based predictive control (HDY-PC) method is proposed to improve the path tracking performance of the leading vehicle. In the lower layer, the improved distributed model based predictive control (IDM-PC) method is developed to guarantee the motion effectiveness and stability of the vehicle platoon. Besides, the multilayer control framework can handle various communication topologies and dynamic cut-in/cut-out maneuvers. The virtual environment simulation shows that the proposed motion control framework for heterogeneous autonomous vehicle platoon achieves better performance in path tracking and platoon keeping. The adaptability of the framework is also verified using another real-world scene.