There is an increasing need for charging station recommendation to minimize the overall charging time for electric vehicles and balance load for the charging stations. To grant this need, we model the recommendation problem as a Markov Decision Process (MDP) problem. However, the traditional MDP model has the issue of ‘curse of dimensionality’. To address this issue, we propose an extension of MDP: multiple-phase MDP, in which the state transition of MDP is decomposing into several phases, so as to reduce the state space and state transition complexities. This is done by introducing two states other than the normal state defined in MDP: post decision state and intermediate decision state. Then, we propose an online learning based algorithm to solve the formulated multiple-phase MDP model. Thanks to the reduced complexities of the state space and state transition, the proposed online algorithm can converge fast. By comparing to other recommendation mechanisms, such as game theory based recommendation and Q-learning based recommendation, our simulation evaluation demonstrates that our proposition can bring good performance.

The service management based on battery heterogeneity has become an increasingly important research problem in battery swapping technology. In this paper, with the method of bipartite matching, we first theoretically analyse the offline optimization problem of battery swapping service under battery heterogeneity. Nevertheless, the information of global view used in offline optimization solution cannot be known in advance during real-time operation. To address the disadvantage, an online framework comprising several sub-procedures is proposed for heterogeneous battery implementation. Firstly, by incorporating battery swapping station (BSS) local status such as charging and waiting queue of heterogeneous batteries, a charging slot allocation mechanism is designed. Utilizing the proposed allocation method, the charging priority is determined by the proportion of heterogeneous batteries demand, so as to guarantee charging fairness. Secondly, with the help of reservation information, the proposed allocation method can further be improved by predicting the future arrival distribution of heterogeneous types of electric vehicles. Thirdly, according to the service demand prediction based on long short-term memory neural network, joint optimization of BSS-selection and charging cost can be achieved by charging power adjustment. Simulation results indicate the desirable performance of proposed scheme in balancing the demands of multi-party participators.

In disaster scenarios, secure and reliable data collection in Vehicular Ad Hoc Network (VANET) is crucial, yet the network often suffers from issues such as infrastructure damage, network partitioning, and vulnerabilities to attacks (e.g., False Data Injection and Black Hole Attack). Trust management is a promising solution to prevent these attacks. However, in infrastructure-less and partitioned disaster areas, existing trust schemes face problems of trust evidence sparsity and evaluation inconsistency, leading to inaccurate detection. To address these limitations, we propose an Unmanned Aerial Vehicle (UAV)-Assisted Traceable and Hierarchical Trust Management scheme (UATHTM). The UATHTM includes a Vehicle-to-Vehicle (V2V) local trust model and a UAV-to-Vehicle (U2V) global trust model. The former facilitates rapid detection of false data, while the latter is designed for accurately tracing malicious vehicles. Specifically, the V2V model constructs a mutual adjustment between entity-centric and data-centric assessments, continuously refining trust for accurate local detection. The U2V model incorporates a new trust metric based on disaster trajectory similarity to enhance the accuracy of global tracing, through leveraging the comprehensive view of UAVs. Extensive simulations demonstrate that the UATHTM scheme outperforms existing trust management schemes, showing higher precision, recall, and F1-score in detecting false data and malicious vehicles in VANET under challenging conditions.

With the advent of consumer electronics, Electric Vehicles (EVs) are gradually becoming representative carriers for the next generation of consumer electronics. EVs are gaining market share compared to traditional fuel vehicles. This paper works on charging station (CS) recommendation to improve the charging experience. However, there is uncertainty that EV users follow exactly the CS recommendation from operation system, and this thereby degrades the performance of CS recommendation system. To address this issue, we extend the Markov Decision Process (MDP) model by integrating user preference (e∙, prefer the nearest CS). Then, we propose a neural network-based framework to solve the established model, in which we use a Long Short-Term Memory (LSTM) module to capture user preference features and an attention module to establish the relationship between the preference features and system state features. Based on these modules, a user Preference-based Deep Q Network (PDQN) is designed to solve the established CS recommendation model. Our simulation results demonstrate that our proposition can bring better performance in terms of average charging time and rewards, by comparing it with the nearest mechanism, the minimum queuing mechanism and the DQN mechanism without preference.

With the increasing intelligence and convenience of modern vehicles, Internet-of-Vehicle (IoV) technology plays a pivotal role in driving these advancements. While IoV enhances user services, it also introduces security threats, particularly intrusions into the In-Vehicle Network (IVN). Attackers can exploit onboard external network interfaces, posing risks to vehicle safety and operation. Besides, deploying an Intrusion Detection System (IDS) on vehicles with limited computational resources exacerbates the computational burden. To address these challenges, this paper proposes a Cloud Collaborative-based Intrusion Detection and Prevention System (CC-IDPS). Firstly, this study analyzes existing in-vehicle intrusion detection datasets and, combined with real in-vehicle/simulation data, constructs the In-vehicle Attack Dataset (IVAD). Secondly, the CC-IDPS employs a sliding window approach for feature extraction from multiple Controller Area Network (CAN) IDs, and leverages Bidirectional Encoder Representation from Transformers (BERT) models for in-vehicle traffic classification. Based on classification results, the CC-IDPS applies treatments to in-vehicle traffic. Subsequently, the proposed Encode2ID algorithm encodes and stores malicious traffic in the database of cloud, facilitating subsequent model training. Experimental results demonstrate that, when applied to the IVAD dataset, the model exhibits lower detection accuracy and F1-score compared to other datasets. Notably, the CC-IDPS outperforms other machine learning and deep learning models in detecting performance on the IVAD dataset.

Multi-station integration is motivated by the requirements of distributed energies interconnection and improvements in the efficiency of energy systems. Due to the diversity of communication services and the complexity of data exchanges between in-of-station and out-of-station, multi-station integrated systems have high security requirements. However, issues related to cyber security for multi-station integrated systems are seldom explored. Hence, this paper designs the secondary system architecture and proposes cyber security protection solutions for smart energy stations (SESt) that integrate the substation, photovoltaic station, energy storage station, electric vehicle charging station, and data center station. Firstly, the composition of SESt and functions of each substation are presented, a layered architecture of SESt is designed, and data exchanges of SESt are analyzed. Then, the cyber security threats and requirements of SESt are illustrated. Moreover, the cyber security protection principle and a cyber security protection system for SESt are proposed. On this basis, a security zoning and isolation scheme for SESt is designed. Finally, a traffic isolation scheme based on virtual local area networks (VLANs), a real-time guarantee scheme for communications based on service priority, and an enhancing cyber security scheme based on improved IEC 62351 are proposed for SESt.