Huimiao Chen

Environment perception plays a crucial role in autonomous driving technology. However, various factors such as adverse weather conditions and limitations in sensing equipment contribute to low perception accuracy and a restricted field of view. As a result, intelligent connected vehicles (ICVs) are currently only capable of achieving autonomous driving in specific scenarios. This paper conducts an analysis of the current studies on image or point cloud processing and cooperative perception, and summarizes three key aspects: data pre-processing methods, multi-sensor data fusion methods, and vehicle–infrastructure cooperative perception methods. Data pre-processing methods summarize the processing of point cloud data and image data in snow, rain and fog. Multi-sensor data fusion methods analyze the studies on image fusion, point cloud fusion and image-point cloud fusion. Because communication channel resources are limited, the vehicle–infrastructure cooperative perception methods discuss the fusion and sharing strategies for cooperative perception information to expand the range of perception for ICVs and achieve an optimal distribution of perception information. Finally, according to the analysis of the existing studies, the paper proposes future research directions for cooperative perception in adverse weather conditions.

As a foreseeable future mode of transport with lower emissions and higher efficiencies, electric vehicles have received worldwide attention. For convenient centralized management, taxis are considered as the fleet with electrification priority. In this work, we focus on the study on electric taxis dispatching, with consideration of picking up customers and recharging, based on real world traffic data of a large number of taxis in Beijing. First, the assumed electric taxi charging stations are located using the K mean method. Second, based on the station locations and the order demands, which are in form of origin-destination pairs and extracted from the trajectory data, a dispatching strategy as well as the simulation framework is developed with consideration of reducing customer waiting time, mitigating electric taxi charging congestion, and balancing order number distribution among electric taxis. The proposed method models the electric taxi charging behaviors temporally discretely from the aspects of charging demands and availability of chargers, and further incorporates a centralized and intelligent fleet dispatching platform, which is capable of handling taxi service requests and arranging electric taxis' recharging in real time. The methodology in this paper is readily applicable to dispatching of different types of electric vehicle fleet with similar dataset available. Among the method, we use queueing theory to model the electric vehicle charging station waiting phenomena and include this factor into dispatching platform. Carbon emission is also surveyed and analyzed.

In this study, an analytical model for roadside unit (RSU) location is proposed. The objective of the model is to promote information propagation on two parallel roadways in vehicular ad hoc networks. The information propagation is achieved through multi‐hop vehicle‐to‐vehicle and vehicle‐to‐infrastructure communication. The relationship between the information travel time from one RSU to another and the distance between the two RSUs are derived. The RSU location problem is formulated as a shortest path problem and then as an integer linear programming. The proposed model is applicable for traffic flow with a general vehicle headway distribution. A detailed comparison between the proposed model and an existing RSU location model is conducted through Monto Carlo simulation experiments under different vehicle headway distributions, traffic densities, budget sizes, and road separation distances. The results indicate that the proposed model outperforms the existing model in all cases. Moreover, it is shown that it is necessary to consider the general vehicle headway distribution and road separation in RSU locations that are focused on information propagation promotion.

The large scale integration of wind energy generation adds a new burden to frequency regulation because of the volatility of wind power. Incorporating wind turbines (WTs) into automatic generation control (AGC) is a new approach for maintaining a satisfactory frequency regulation performance, which requires the active power control (APC) of WTs. Conventionally, the APC of WTs is achieved by pitch angle control (PAC) or rotor speed control (RSC), which respectively show a slow response and a narrow regulating range. To improve the regulation performance of variable speed WTs, a coordinated APC strategy that includes the simultaneous activation of PAC and RSC and takes full advantage of rotor kinetic energy to absorb or release extra mechanical energy is proposed in this paper. The simulations focusing on both the WT and power system levels have shown the effectiveness of proposed control strategy.

Transmission network expansion planning (TNEP) is expected to cope with the power transmission inadequacy problem resulting from rapid growth of wind generation in power systems. However, due to significant uncertainties on the generation side, TNEP based solely on one snapshot or a limited number of scenarios cannot capture the time-variant characteristics of wind power and may be suboptimal. In this paper, a new heuristic methodology for TNEP based on chronological evaluation is presented. First, to assess the transmission adequacy, the multiperiod DC optimal power flow considering the generators' ramping limits is employed for a system production simulation covering an entire year. Second, two sensitivity factors derived from Lagrange multipliers, i.e., the sensitivity factors of the branch capacity and the branch admittance, are applied to determine the initial set of the transmission network expansion plan (I-STNEP). Finally, the cost-benefit incremental relationship index and a degressive method are used to identify the best subset of the transmission network expansion plan within the I-STNEP. Simulation results based on a 3000-bus real-world network in northern China validate the effectiveness of the proposed TNEP method.

Plug-in electric vehicles (PEVs) can contribute to the improvement of energy and environmental issues. Among different types of PEVs, plug-in hybrid electric taxis (PHETs) go in advance. In this study, we provide a spatial and temporal PHET charging demand forecasting method based on one-month global positioning system (GPS)-based taxi travel data in Beijing. Then, using the charging demand forecasting results, a mixed integer linear programming (MILP) model is formulated to plan PHET charging stations in the central area of Beijing. The model minimizes both investment and operation costs of all the PHET charging stations and takes into account the service radius of charging stations, charging demand satisfaction and rational occupation rates of chargers. At last, the test of the planning method is carried out numerically through simulations and the analysis is complemented according to the results.

Investment of charging facilities is facing deficit problems in many countries at the initial development stage of plug-in electric vehicles (PEVs). In this paper, we study the charging facility planning problem faced by a PEV charging station investor who aims to serve PEV customers with random behaviors and demands (but follow a series of predicted distributions) with lower economic costs of both charging facilities and practical operation. First, we design a multiple-charger multiple-port charging (MCMP) system which provides the capability of scheduling a limited quantity of chargers to serve more PEVs. Second, we further develop a general two-stage stochastic programming model to plan the MCMP system inside a PEV parking lot with consideration of coordinated charging. Third, we classify MCMP stations according to three different connecting patterns between chargers and parking spaces. Fourth, the general planning model is simplified (as a reference) based on the classification. Finally, we complement our analysis through case studies.