The accurate modelling of the charging characteristics of electric vehicles (EVs) is the basis for the load forecasting, infrastructure planning, and orderly charging management. While, research based on the measured charging data of EVs is seldom carried out, and the concrete modelling of the correlations of various parameters is a gap in the knowledge. Aiming at this, we carried out an investigation based on operational data, from August 2016 to August 2017, of an EV charging service company in Nanjing, China. The time-energy characteristics of EV charging behaviour can be described using the probability distributions and correlations of three charging parameters, i.e. charging start time, charging duration, and charged capacity. In the research, we fitted the probability densities of these charging parameters using the kernel estimation method and verified the correlations of time parameters of the charging behaviour. Multiple copula functions were used to model the correlation between the time and energy parameters of different types of charging behaviours. On this basis, we also carried out stochastic simulation for the load curve of disordered charging and analysed the potential of the EV charging load participating in the orderly management and its coordination with the output of power generation using renewable energy.

Electric vehicles (EVs) have attracted growing attention in recent years. However, most existing research has not utilized actual traffic data and has not considered real psychological decision-making of owners in analyzing the charging demand. On this basis, an urban EV fast-charging demand forecasting model based on a data-driven approach and human decision-making behavior is presented in this paper. In this methodology, Didi ride-hailing order trajectory data are firstly taken as the original dataset. Through data mining and fusion technology, the regenerated data and rules of traffic operation are obtained. Then, the single EV model with driving and charging behavior parameters is established. Furthermore, a human behavior decision-making model based on Regret Theory is introduced, which comprises the utility of time consumption and charging cost to plan driving paths and recommend fast-charging stations for vehicles. The rules obtained from data mining together with established models are combined to construct the ‘Electric Vehicles–Power Grid–Traffic Network’ fusion architecture. At last, the actual urban traffic network in Nanjing is selected as an example to design the fast-charging demand load experiments in different scenarios. The results demonstrate that this proposed model is able to effectively predict the spatio-temporal distribution characteristics of urban fast-charging demands, and it more realistically simulates the decision-making psychology of owners’ charging behavior.

Bidirectional wireless power transmission (BD-WPT) systems are applicable to occasions that need bidirectional energy flows, such as vehicle-to-grid (V2G) technology. The traditional BD-WPT systems work at a fixed frequency and the optimal transmission efficiency of the systems is fixed. Combining the frequency characteristics of AC resistance, the mathematical model of variable frequency BD-WPT system is built in this paper. Then, the characteristics of transmission efficiency and the characteristics of maximum active power are analyzed based on the model. In order to make the BD-WPT system have higher transmission efficiency, a BD-WPT system with variable structural parameters and the corresponding segmented power-efficiency coordinated control strategy are proposed in this paper. According to different reference power on the secondary side, the BD-WPT system can work in different resonant frequency segments by controlling the switching of corresponding relays in the capacitor arrays on the primary and secondary sides and changing working frequency of the H-bridge converters on the primary and secondary sides. By using the proposed control strategy, the transmission power, and direction, can be tunable segmentally while maintaining high transmission efficiency. The proposed concept is verified to be effective through experiment.

This work introduces the Nudge theory of behavioral economics in electric vehicle (EV) charging schedules. Nudges emphasize guiding user choice through non-coercive means that go beyond economics. First, we conduct surveys of EV users to verify the effectiveness of Nudges in guiding charging behavior and establish user response models for day-ahead simulation. Then, we build a charging agent that can realize the rapid optimization of a single EV to reduce the electricity cost and improve the flexible regulation capacity (FRC). This agent is modeled and trained based on Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. Next, we perform Monte Carlo (MC) simulations with this agent to calculate the day-ahead variability range of power consumption and FRCs involving various EVs. Finally, we set the price difference of the charging options with Nudges as the control variable and utilize Discrete Particle Swarm Optimization (DPSO) to obtain its optimal value with the objective of the revenue of the charging station operator (CSO). The case study is based on over 1,500 questionnaires. The results highlight that the Nudges can improve the percentage of users participating in scheduled charging, thereby helping CSO achieve more revenue and offer reliable FRCs to the grid.

While providing charging service for electric vehicles, charging stations are also aggregation centers which supply adjustment capabilities to the power grid. It is necessary for charging stations to take care of all parties' interests with respect to users' independent choice. This article focuses on multi-element charging stations which contain conventional load, energy storage and distributed renewable energy generation besides charging piles. Firstly, charging price mechanism that improves participation motivation of users in providing charging flexibility were designed. Next, rational model based on expected utility and bounded rational model based on prospect theory were established respectively. Model predictive control(MPC) has been selected to handle uncertainties caused by users' behavior and output of distributed renewable energy generation. Both operating economy and adjustment flexibility of charging stations have been taken into account in proposed comprehensive scheduling strategy. Based on the setting of dummy variables, double-layer optimization has been converted into single-layer optimization which can reduce the calculation time significantly, making the strategy suitable for online application. Advantage of the bounded rational model in predicting users' behavior was verified by experiments towards real people. The selection of time scale for MPC was discussed through simulation experiments in different scenarios. In the simulation experiment, the proposed scheduling model integrating social-physical methods has shown good performance in power grid requirement following and economic optimization.