Electric vehicles are beneficial to the environment owing to its nonproduction of emissions and excessive noise; however, they have their own limitations with respect to charging difficulties and mileage anxiety. In order to address these problems, dynamic wireless power transfer (DWPT) technology has been developed. In this article, we replace a conventional open-loop method without dc-dc converter with the proposed dc-dc converter in the energy receiver in order to improve output power. Moreover, to make sure that the DWPT system is reliable when the coupling coefficient changes rapidly over a wide range of values, we propose model predictive control (MPC) to ensure optimal dynamic-tracking performance. Considering the fact that sampling delay will cause an error in the controller output, the system is modeled accurately in order to improve the MPC performance. In addition, the MPC and double closed-loop proportional-integral-differential control are compared with simulations and experiments, the results of which demonstrate the effectiveness of MPC-based DWPT.

Electric vehicles (EVs) can behave as distributed energy storage devices for providing on-demand smart grid support service, that is an emerging Vehicle-to-Grid (V2G) technology. A high-performance and easy-implementation current control strategy for V2G Three-phase four-leg inverter with LCL filter is proposed. It consists of a deadbeat (DB) controller and a paralleled repetitive controller (RC). The DB controller is based on weighted average inductor current (WAIC) scheme, which simplifies the third-order LCL filter to be an equivalent 1st order L filter. The stability of the DB controlled inverter with the unmodelled system time delay is analyzed. DB controller is of very fast response and easy implementation, but is not immune to system time delay and various uncertainties. To overcome the disadvantages, a plug-in RC is added to reinforce the DB controller to remove harmonic distortion from the feed-in current in the presence of parameter uncertainties. A lab prototype of 10kW grid-connected three-phase four-leg inverter has been built up to validate the proposed current control strategy. The simulations and experiments are provided to demonstrate the validity of the proposed control strategy.

State of health (SOH) is one of the most important performance indicators of lithium-ion batteries (LIBs). Accurate estimation of SOH is a prerequisite for the safe and reliable operation of LIBs. Traditional SOH estimation methods predominantly rely on complete charging cycle data acquired through laboratory testing. However, in practical application, the charging behaviors of electric vehicle users are random and unpredictable, making the partial charging curves difficult to utilize the traditional methods. This work introduces a novel data-driven approach to estimating a battery's SOH for partial charging cases. Firstly, a curve fitting method is proposed to extract health indicators (HIs) from partial charging voltage data, where novel HIs based on the energy-voltage curve are extracted. A composite Gaussian process regression-based data-driven method is proposed to achieve highly accurate SOH estimation. The method's adaptability to real-world partial charging habits is evaluated through three representative scenarios derived from extensive charging behavior reports of EV users. The impact of partial charging on HI extraction is analyzed based on the three identified scenarios. The proposed method is verified using a combination of our laboratory testing data and the Oxford open dataset. The results show that the proposed framework demonstrates the ability to estimate SOH accurately and strong robustness to various partial charging behaviors.

Durability is considered as one of the main technical obstacles to the large-scale commercialization of proton exchange membrane fuel cells (PEMFCs), which can be effectively improved through degradation prediction techniques. This article proposes a stacked echo state network (ESN) based on the genetic algorithm (GA) to predict the future degradation trend of PEMFCs. By alternately using the projection layer and the encoding layer, the proposed method can make full use of the temporal kernel property of the ESN to encode the multiscale and multilevel dynamics of the stack voltage, thereby obtaining more robust generalization performance and higher accuracy than the existing methods. Specifically, a stack voltage time series of PEMFCs is projected into the high-dimensional echo state space of the reservoir. Then, an autoencoder projects the echo state representation into the low-dimensional feature space. After that, the GA is utilized to optimize the hyperparameters of the developed model. Based on two open-source datasets of PEMFCs with different accelerated test conditions, this article systematically tested the proposed degradation prediction methods based on different model structures. Test results demonstrate that the proposed method is superior to traditional prediction methods in terms of accuracy and generalization performance.

A finite-time disturbance-observer (DO)-based fuzzy sliding mode cooperative backstepping (FDFSM-CB) scheme is proposed, to improve the synchronization performance and reduce its error under disturbance between multiple permanent magnet synchronous traction motors in low-speed urban rail transit (URT). First, each motor is supposed to an agent of multi-agent system (MAS), and the information between multiple motors can be transmitted through a communication topology network, which ensures the consistency of the response of each agent. Then, considering that the load torque of the motor is disturbed during operation, a finite-time DO is proposed to estimate the unknown load disturbance, thus guaranteeing the anti-disturbance ability of the system. Besides, the nonlinear parts of the dynamic model are approximated by fuzzy logic systems (FLSs), and a second-order sliding mode differentiator is designed to avoid the direct derivation of virtual control law and the problem of differential explosion. Finally, the system is proved to be finite-time-stable. The feasibility and effectiveness of the proposed control scheme are verified by the hardware-in-the-loop (HIL) platform.

Three-phase four-leg inverters with an LCL filter are employed as onboard vehicle to grid (V2G) inverters due to their advanced features—high utilization of the dc bus voltage, counteraction of unbalanced voltages, and so on. Repetitive control (RC) can exactly track periodic signals with known frequency, which offers a simple high-accuracy current tracking and harmonic suppression solution for three-phase four-leg inverters. However, the conventional RC (CRC) offers slow dynamic responses due to the embedded delay element, and grid frequency fluctuation would cause significant performance degradation of CRC controllers in V2G applications. Moreover, the three-order LCL filter often causes complexity in developing good performance feedback control schemes for the inverters, especially for complex three-phase four-leg ones. To address these problems, an easy-for-implementation hybrid control scheme that combines a frequency-adaptive RC (FARC) controller with a deadbeat (DB) controller is proposed to obtain fast and accurate control of feed-in grid current for the onboard three-phase four-leg V2G inverters with an LCL filter in the presence of grid frequency variation and load changes. The fast-response DB control scheme is based on a simplified first-order model of an LCL filter. The FARC controller is immune to grid frequency fluctuation. Simulations and experiments have been done to prove that the DB plus FARC control strategy can provide an excellent current control solution for the three-phase four-leg V2G inverters.