Liang Dai

Integrated photovoltaic-storage-charging station at highway service area is an important scene for transportation-energy integration. Multiple charging stations along highway experience pronounced spatiotemporal supply-demand mismatch due to heterogeneous service quality, spatially varying solar resources, time-varying electricity prices, and stochastic electric vehicle (EV) charging demand. A distributed online energy management strategy for highway charging stations is proposed, incorporating considerations of spatiotemporal demand and user preferences. Firstly, a Logit-based charging station selection model is developed by constructing a utility function that integrates travel time, charging queuing time, range anxiety, and charging price to characterize EV users’ preferences. Subsequently, virtual queues are introduced for the energy storage system and EV charging demand to convert the time-coupled constraints into a queue stability problem. Finally, Lyapunov optimization is employed to reformulate the long-term operational cost minimization into a per-slot drift-plus-penalty problem. Simulation results demonstrate that the proposed strategy effectively balances system operating costs and queue stability. Compared with greedy strategy and model predictive control-based strategy, the proposed strategy reduces the average operating cost by 25.35% and 13.8%.

The vehicular cloudlet (VC), capable of leveraging synergies to support emerging cooperative services and task offloading among neighboring vehicles, will be adopted in the Internet of Vehicles (IoV). Compared with traditional clustering methods based on link connectivity, VCs require higher transmission capacity and stability. Three major challenges must be addressed when optimally establishing the VC structure. First, the transmission capacity is affected by inherent stochastic characteristics, including channel fading and interference. Second, the mobility of vehicles introduces instability. Third, a comprehensive optimization model is required to jointly improve the stability and transmission capacity of the established VC. Therefore, this paper proposes a dual cyber-physical network (DCP) model to represent the dynamic physical network and the coupled transmission network. Generalized analytical expressions for channel quality are derived using moment-generating functions based on a Nakagami-m small-scale fading model to handle stochastic characteristics. Furthermore, a DCP association density optimization model is proposed that considers the stability of the physical topology and the transmission capacity of the channel. Symmetric non-negative matrix factorization is used to solve the optimization problem with low complexity. Simulation results confirm that our proposed method achieves higher transmission capacity and stability compared existing link connectivity-based clustering methods.

The separation of battery charging and swapping processes enables highway operators to more flexibly manage the recharging of depleted batteries at battery swapping stations (BSSs), while also leveraging renewable energy (RE) resources to lower electricity costs. However, previous studies have not addressed the connection between BSS recharging strategies and the demand for fully charged batteries, which is determined by EV swapping schemes. To address this gap, the paper proposes a joint optimization approach for scheduling EVs and managing BSSs, considering RE generation along the highway. Specifically, a spatial-temporal network model is developed to represent the transportation-energy characteristics of EVs, incorporating BSS selection and battery swapping processes. Additionally, a BSS management model is formulated to plan the recharging of depleted batteries and ensure the availability of fully charged batteries to meet the swapping demands of EVs. The Lagrange relaxation algorithm is employed to handle the interaction between EV scheduling and BSS operations. A case study shows that this method effectively coordinates EV swapping schemes with BSS recharging strategies, resulting in a 17.3% improvement in RE generation utilization and a reduction in grid electricity consumption by RMB 1753.8.

The highway transportation system exhibits immense potential for adaptive functioning due to its distinctive operational attributes. This capability permits to harness more renewable energy, leading to a reduction in energy consumption costs. In this article, an integrative energy system optimization model is introduced from the standpoint of highway service zone operators. To simulate the behavior of electric vehicles on highways, an expanded network flow model is formulated considering driving, queueing, and charging actions. This model comprehensively integrates dimensions of time, space, and the state of charge of the vehicles. Consequently, the proposed model is proficient in representing the spatiotemporal and flexible operational traits inherent to electric vehicles. Through a series of numerical case studies executed on a real-world highway system, the effectiveness of the proposed model is demonstrated. These findings substantiate the potential utility and efficacy of our model in enhancing the operation optimization of energy systems within highway transportation systems.

Speed limit is an important link in traffic safety. For example, schools, hospitals, villages and other special sections, it is necessary to remind/control the reasonable speed in the subsequent sections ahead and prevent the danger of overspeed. In this paper, we implement the vehicle speed control system of Cooperative Vehicle Infrastructure System and its simulation verification. In the process of simulation and verification, combined with the micro traffic simulation software SUMO and network simulation software NS3, taking the map of Weishui campus of Chang’an University (OSM) as the design background, roadside units are set at multiple intersections to design the Cooperative Vehicle Infrastructure System. When the vehicle passes the intersection with roadside units deployed, the roadside unit sends the restricted speed of the section to passing vehicles, So as to control the driving speed of the vehicle on this road section. In the networked vehicle simulation system designed in this paper, first generate the road network and simulated vehicles through SUMO, then deploy RSU in the road network and deploy OBU on the simulated vehicles by NS-3, finally realize the communication between RSU and OBU by means of network simulation, and conduct animation demonstration through SUMO, finally realize the speed control of vehicles within the specified range, and analyze the effect of this control, The feasibility of the scheme is proved, and the impact of the networked vehicle speed control system on the road network is discussed by analyzing the change of vehicle emission in the networked vehicle speed control system.

In Cooperative Vehicle Infrastructure System (CVIS), the roadside unit (RSU) obtains many kinds of monitoring data through observation equipment carried by the RSU. The monitoring data from RSUs are transmitted to an RSU that is connected to the backbone network using the “store–carry–forward” scheme through the mobile vehicle. The monitoring data obtained by RSUs are timely, and different types of monitoring data have corresponding timelines. Reducing end-to-end delays to ensure more packets can be transmitted before deadlines is challenging. In this paper, we propose a Distributed Packet Scheduling Scheme for Delay-Packets Queue Length Tradeoff System (DDPS) in CVIS to solve the multi-RSU-distributed packet transmission problem. We also establish the vehicle speed state, vehicle communication quantity prediction, data arrival, and end-to-end delay minimization models. After Lyapunov’s optimization theory transformed the optimization model, a knapsack problem was described. The simulation results verified that DDPS reduced the end-to-end average delay and ensured the data queue’s stability under packet deadline conditions.