In the initial period of automobile development, the vast majority of researchers would never have guessed network communications would be applied to vehicles one day. After the advent of CAN, a tremendous amount of intra-vehicle network communication technologies have sprung up throughout the automobile industry. With the introduction of intelligent driving, inter-vehicle network communication technologies have been subsequently developed to complement intra-vehicle network communication technologies and achieve higher-level functions. However, communication and control seem to be advanced independently for a long time and are less connected in actual development. Thus, to promote the fusion of communication and control, this paper provides a comprehensive survey on vehicle real-time motion control considering the adverse influence of network communications (such as network-induced delay, packet dropout, and network congestion) from three aspects: intravehicle, inter-vehicle, and integration of these two. To benefit analysis and study, mainstream intra-vehicle and inter-vehicle network communication technologies are first summarized, also with their network architectures. As research is still emerging, several open issues and future challenges of Control over Communications (CoC) are emphasized for further development to enable intelligent driving in the true sense.

Accurate prediction of the remaining useful life of a lithium–ion battery (LiB) is of paramount importance for ensuring its durable operation. To achieve more accurate prediction with limited data, this paper proposes an RVM-GM algorithm based on dynamic window size. The method combines the advantages of the relevance vector machine (RVM) algorithm and grey predictive model (GM). The RVM is applied to provide the relevance vectors of fitting function and output probability prediction, and the GM is used to obtain the trend prediction with limited data information. The algorithm is further verified by the NASA PCoE lithium–ion battery data repository. The experimental prediction results of different batteries data show that the proposed algorithm has less error while applying a dynamic window size compared with a fixed window size, while it has higher prediction accuracy than particle filter algorithm (PF) and convolutional neural network (CNN), which has verified the effectiveness of the proposed algorithm.

Considering the battery-failure-induced catastrophic events reported frequently, the early fault warning of batteries is essential to the safety of electric vehicles (EVs). Motivated by this, a novel data-driven method for early-stage battery-fault warning is proposed in this paper by the fusion of the short-text mining and the grey correlation. In particular, the short-text mining approach is exploited to identify the fault information recorded in the maintenance and service documents and further to analyze the categories of battery faults in EVs statistically. The grey correlation algorithm is employed to build the relevance between the vehicle states and typical battery faults, which contributes to extracting the key features of corresponding failures. A key fault-prediction model of electric buses based on big data is then established on the key feature variables. Different selections of kernel functions and hyperparameters are scrutinized to optimize the performance of warning. The proposed method is validated with real-world data acquired from electric buses in operation. Results suggest that the constructed prediction model can effectively predict the faults and carry out the desired early fault warning.

This paper aims to address uncertainty and instability in automated lane change (ALC) systems for electric vehicles with a central-zonal electronic and electrical (E/E) architecture, where mixed-service package transmission competitions are inevitable. While the central-zonal E/E architecture provides significant advantages, including enhanced service integration, reduced wiring complexity, and seamless software updating, it also induces transmission competitions between time-sensitive services like steering and non-time-sensitive services such as information and entertainment, referred to as mixed-service package transmission competitions. These competitions can result in queuing delays within the control loop, termed mixed-service-induced loop delays, negatively impacting the stability and safety of ALC systems. To this end, a service-identification and active-scheduling (SIDAS) mechanism is proposed with a triple-plane framework consisting of strategy, plane and data planes. Further, a collaborative design scheme of control and communication is presented to ensure precise trajectory tracking and maintain the robustness and stability of ALC motion control in the presence of MSI loop delays. Ultimately, hardware-in-the-loop tests demonstrate the effectiveness of the proposed mechanism and methodology.

Connected cruise control (CCC) is an advanced system that extends traditional cruise control by incorporating vehicle-to-everything (V2X) communications. However, as such technologies continue to iterate, conventional intra-vehicle communication systems struggle to meet escalating bandwidth demands, while cross-vehicle communication delays degrade CCC’s closed-loop performance and increase collision risks. Motivated by these issues, a zone-centralized architecture integrating automotive passive optical networks and cellular V2X is proposed to reduce the upper bounds of loop delays compared to those in Ethernet-based domain-centralized systems. A novel cross-vehicle loop delay analysis framework is introduced to characterize communication system uncertainties with provable upper bounds. Building on this, a scheduling and control coordination scheme is developed to mitigate the cross-vehicle loop delays in intra-vehicle and inter-vehicle communications, while ensuring precise tracking platoon motion. For scheduling, a fraction-type basic period methodology and an earliest-deadline-priority strategy are employed to manage deterministic communications and alleviate the cross-vehicle loop delays. In terms of control, a delay-robust model predictive control approach is applied for decision-making and an H $\infty $ -based linear quadratic regulator technique is used for vehicle longitudinal acceleration tracking while combating vehicle communication delays. Finally, the proposed scheduling and control coordination scheme undergoes validation across diverse driving scenarios, demonstrating its effectiveness and robustness through hardware-in-the-loop verification.