A vehicular energy network (VEN) enables energy transfer by leveraging electric vehicles as mobile carriers through wireless exchange across large geographic areas. This article presents a scalable and robust framework for energy routing in stochastic VENs with the objective of minimizing transmission loss. The problem is formulated as a graph generalized flow optimization, solvable to global optimality via linear programming. To ensure scalability, a flow-guided graph reduction method is proposed, which preserves critical supply-demand connectivity by prioritizing high-impact routes based on vehicular flow patterns. Building upon this, a route-guided time-expanded graph construction strategy is developed to avoid exhaustive temporal replication by generating only time-relevant nodes and arcs along active routes. To address long-horizon stochasticity, a long short-term memory-based model predictive control framework is designed, which captures both randomness and uncertainty via data-driven forecasting and residual-aware robust correction under a rolling-horizon decomposition. The proposed methods are validated on 100 real-world U.S. datasets, demonstrating significant gains in computational efficiency, scalability, and solution robustness across both time-invariant and time-varying VENs.

This paper introduces a novel multi-frequency cascade disturbance observer (MF-CDOB) designed for permanent magnet synchronous motor (PMSM) drives. Unlike conventional disturbance observer (DOB) techniques, the proposed MF-CDOB employs a cascaded arrangement of multiple DOBs. This unique structure effectively decouples the frequency responses, enabling independent tuning for the rejection of both aperiodic and harmonic disturbances. Developed in the discrete-time domain, the method offers enhanced real-time disturbance rejection without compromising transient performance. Preliminary experimental results validate that the MF-CDOB significantly reduces harmonic content and improves current regulation compared to traditional control schemes.

Variations in coupling coefficient and load impedance pose significant challenges to achieving a stable constant voltage (CV) output in wireless power transfer (WPT) systems, particularly for multi-frequency multi-pickup applications. This article presents an improved LCC topology designed for multi-frequency multi-channel WPT to ensure CV characteristics under such conditions. The proposed network facilitates simultaneous power transfer on multiple frequency channels with key contributions including: 1) enabling load-independent constant current (CC) output per channel at the primary side; 2) realizing coupling coefficient-independent CV output per channel at the pickup side by adopting a closed-loop control strategy. Both merits ensure stable power delivery regardless of positional misalignment or load fluctuations. Theoretical analysis addresses the design principles and parameter selection criteria. Verification results validate the effectiveness of the proposed topology in maintaining CV output across multiple channels under varying coupling and load conditions, demonstrating its suitability and effectiveness.

The dynamic wireless charging (DWC) system based on wireless charging lanes (WCLs) is an important component of smart cities, allowing electric vehicles (EVs) to charge while moving. It is necessary to establish a user-oriented real-time DWC navigation system to achieve the joint optimization of EV routing and charging. However, the modeling characteristics of DWC and the risk preferences of EV owners towards congested WCLs are completely different from those in traditional wired charging. Furthermore, optimal EV charging navigation is always challenging without prior knowledge of uncertainty in electricity prices and traffic conditions. This paper first proposes a novel dynamic charging routing model for individual EVs to minimize travel and charging costs, and reformulates it as a twostep optimization problem to facilitate feature extraction. Then, mirror-symmetrical Dijkstras algorithm (MSDA) is proposed to solve the reformulated model in linear time and extract advanced features from the stochastic information. By feeding the system state containing extracted features into the deep Q network (DQN) in an event-triggered manner, the near-optimal charging navigation strategy is finally obtained. The proposed MSDADQN approach not only efficiently extracts low-dimensional interpretable input features, but also adaptively learns the unknown dynamics of system uncertainty. Numerical results based on simulated and real-world data validate the proposed approach.

Electric vehicle energy network (EVEN) enables the transmission of renewable energy from rural to urban area by the flexibility of EVs via energy exchange. In this paper, (dis)charging behavior modelling and bidirectional charging station (BCS) deployment optimization are addressed, since they are crucial in EVEN for EV accommodation, renewable energy utilization, drivers’ profitability estimation, operators’ cost assessment, and financial policy establishment. A novel stochastic Markov (dis)charging behavior model is proposed to calculate the spatiotemporal load pattern considering the realistic factors such as personal features, state of charge (SoC), electricity price, and BCS locations. Unlike most works ignoring energy trading, six scenarios are explored: (S1) no trade; (S2) trade in main battery. (S3) trade in extra battery. (S4) trade in extra ultracapacitor; (S5) trade in both main and extra battery; (S6) trade in both main battery and ultracapacitor. Also, a multi-objective BCS deployment strategy is newly designed, aiming at minimizing installation cost and driver’s electricity bill, while quality of service (QoS) and voltage stability are ensured. An improved hybrid algorithm is developed, which combines hill climbing for enhanced exploitation and particle swarm optimization for better evolvement based on genetic algorithm framework. The simulation validates the fitting ability of charging model, the effectiveness of parameter selection algorithm and the deployment approach. Comparing 6 scenarios, benefits of energy trading in EVEN is confirmed and the superiority of ultracapacitor for trading is demonstrated. The feasibility of financial policies is also studied, and certain guidance is provided for drivers to improve their cost.

The high-temperature superconducting permanent magnet synchronous machine (HTS-PMSM) has attracted great attention from industries and researchers because of its higher torque density, higher power density, and higher system efficiency. Considering the stator current of HTS-PMSM is supposed to be as clean as possible, the multiple-level inverter is more suitable for the HTS-PMSM. In addition, similar to the conventional PMSMs, the unknown load disturbance can obviously lead to a speed drop. To solve the issues above, this paper proposed a fast finite-time speed control of a three-level T-type inverter for the HTS-PMSM, combined with model predictive control of the current loop. The proposed control method can ensure that the HTS-PMSM can have the ability to resist load disturbances or changes. Finally, the simulation results verify the effectiveness of the proposed control method.

Traffic energy routers (TERs) are wireless power infrastructures that support the trading of electric vehicles (EVs) in the energy market without physical cables. This paper proposes a TER deployment model for wireless energy trading. The objective is to decide which road junction should be electrified into a TER and how long it is. In this paper, the ultracapacitor is utilized due to the advantages of long-life cycles, high power handling capacity and charging/discharging efficiency. Firstly, the number of EVs stopped at each road junction due to red traffic lights is estimated. Secondly, referring to the current electricity price, the charging and discharging demands are predicted. Thirdly, the predicted demand is revised according to whether the EVs are on the electrified paths and the charging state of ultracapacitors. Hence, the trading profiles in the spatial and temporal domains are obtained. Finally, a multi-objective deployment optimization model is designed, aiming to minimize the installation cost and maximize the drivers' profits. Simulations are carried out to study the energy trading behaviors and the optimal deployment strategy. Compared with the full deployment case, the optimal deployment in our paper uses 2.3% cost while earning 38.0% profit.

This paper proposes and implements a stepless frequency regulation method for wireless power transfer (WPT) with a simple LC compensation network to provide load-independent transmitter current. It should be mentioned that the capacitor in LC compensation network is controlled by a switch. Different from previous switched capacitor compensation networks, which can only select a limited number of resonant frequency points, this model can choose the resonant frequency arbitrarily in a wide frequency range. In theory, the frequency range can be controlled from a few kilohertz to hundreds of kilohertz. The key is controlling the turn-on time of the switch and adjusting the activation time length of the controlled capacitor in every WPT cycle. Therefore, the proposed system is very suitable for multi-frequency WPT, as the system can work at full resonance with practically arbitrary frequency. A Simulink simulation serves for verification, and the results prove the system can change the resonant frequency from 100 kHz to 300 kHz continuously while keeping the transmitter current load-independent.

A vehicular energy network (VEN) uses electric vehicles (EVs) to transport energy across a wide geographical area. EVs can charge and discharge wirelessly at road junctions while moving and, thus, transfer energy between junctions. In this article, we propose a method to optimally route energy from multiple energy-supplying junctions to multiple energy-demanding junctions while minimizing energy loss in four different VEN scenarios: 1) a time-invariant VEN; 2) a VEN with time-varying vehicular flows and energy demands; 3) a VEN with time-varying vehicular flows and time-averaged demands; and 4) a VEN with significant storage capacities at junctions. The method employed to solve these scenarios is to model the VEN as a graph specific to each scenario, and then solve the generalized flow problem on the graph. Simulations are performed on all four scenarios and show that transmission efficiency improves when a small number of long vehicular routes are introduced, and when storage capacities at junctions increase. The error in approximating a time-varying VEN as a static VEN is also investigated.

In this paper, a novel kind of wireless motors, namely, the LCC-compensated wireless switched reluctance motor, is proposed and implemented. The definite advantages are that there is no power converter at the motor side and particularly no switched-capacitor array at the transmitter side to realize the multiple-frequency operation. The key is to develop an LCC compensation involving an inductor and two capacitors (the so-called LCC) for a multiple-frequency wireless power transfer system. As a result, only one transmitter is needed to targetedly feed three receivers, which directly energize the three phase windings of the motor. Particularly, there is no need to control switched capacitors to change the resonant frequency of the transmitter. Meanwhile, the burst firing control method is employed to realize the speed control. Both calculation and experimental results are presented to validate the feasibility of the proposed system. In the system prototype, the transmission distance can reach up to 150 mm and the transmission efficiency can be achieved up to 80%.