Due to the decrease in coupling in the transition area of the primary segment, an undesired fluctuation in output power is expected for most existing solutions. This article, therefore, proposes a method based on the vector summing to reduce the output power fluctuations in electric vehicle dynamic wireless power transfer (EV-DWPT) systems. A simple but well-designed passive LC network is employed between the primary segments. This additional passive LC network in adjacent segments can sum an induced voltage vector provided by the primary segment. The amplitude of the induced voltage vector at the secondary coil is maintained quasi-constant as the vehicle charges dynamically. This ultimately reduces the fluctuation of output power and allows for quick transitions between primary segments. Based on the mathematical model built and vector-summing analysis, the design method for the passive LC network is provided, including the configuration of the passive coil and its compensation capacitor. Taking the entire dynamic process into account, an 8.3 kW prototype has been built where the output power fluctuation is within +/- 3.7% of the rated power, and the system efficiency (dc/dc) is higher than 88.5% with a 220 mm air gap.

In this article, the problem of modeling and control of an LCC-S wireless power transfer (WPT) system in the presence of rectifier discontinuous conduction and load variation is considered. The diode bridge in an LCC-S WPT system often enters the discontinuous conduction mode (DCM), due to its unidirectional conductivity property. To better handle this type of nonlinearity, a linear parameter varying (LPV) model, which allows the model parameters to change with respect to a set of chosen dependence variables, is suggested to describe the dynamic behavior of the system. Based on this LPV model, a modified two degrees of freedom internal model control structure is proposed. The proposed control structure can switch between open loop and closed loop: when the system is in DCM, the system is configured as open loop because the load voltage or current is uncontrollable from the primary side, where any control action may impair the control system performance; otherwise, the system is operated in closed loop. Experimental results are provided to validate the effectiveness of the proposed modeling and control method.

The low-order Hammerstein model plus time delay has been a popular option to describe the dynamic behavior of a wireless power transfer (WPT) system. The controller designed based on this model can achieve better set point tracking performance in a computationally efficient way. Furthermore, load and mutual inductance variations are quite common in WPT systems and they will impair the modeling accuracy and control performance if one has not been taken into account. To address this problem, this article proposes a combined internal model control (IMC) and Luenberger disturbance observer (LDO) method to achieve enhanced disturbance rejection performance against load and mutual inductance variations. Specifically, a data-driven method is employed to build the system model, then the IMC-based method is proposed for controller design, in which LDO is combined to observe and eliminate the disturbances. To ensure the stability of the closed-loop system, a pole placement method is suggested to properly design the observer gain. Thanks to the LDO that can accurately estimate the disturbances resulting from load and mutual inductance variations, the IMC controller can rapidly track the set point irrespective the disturbances. Finally, simulation and experimental results are provided to validate the effectiveness of the proposed method.

Wireless power transfer (WPT)systems have received more and more attention in undersea applications in recent years. The seawater between the transmitter and the receiver is a highly-conductive medium, which can be regarded as an unknown topology that will introduce eddy current loss and unknown dynamic behavior during the power transfer process. Hence, the dynamic modeling and control design methods for WPT systems in air may not apply. In this article, we propose an observer-free model predictive control (MPC) strategy for WPT systems in seawater environment. Considering the unknown topology introduced by the seawater medium, the control system is designed using a data-driven dynamic model obtained by simplified refined instrumental variable (SRIV) method. In a further step, the SRIV-based dynamic model is converted to a special state-space model by choosing a set of state variables corresponding to the input and output variables. Thus, the state observer design is avoided. Besides, operational constraints are imposed into the MPC algorithm to guarantee that the control input is implemented in an appropriate range. Experiments are performed to demonstrate that the proposed MPC system has superior performance in both reference tracking and parametric robustness in comparison to a proportional-integral control system.

Class Phi(2) (or Class EF2) reduces the voltage stress of a Class E converter by adding an additional LC branch, which increases the complexity of the circuit. This letter proposes an integrated Class Phi(2) converter that uses the bifurcation phenomenon of the impedance matching network of the isolation transformer to eliminate the additional LC of a typical Class Phi(2) (or Class EF2) converter. With bifurcation between the isolation transformer, the resonant circuit can be tuned simultaneously at the first, second, and third harmonics, hence achieving a similar quasi-square voltage waveform. Since fewer components are employed, the additional LC can be removed, reducing complexity in comparison to the typical Class Phi(2) (or Class EF2) converter. A mathematical model has been developed to illustrate the frequency response under the bifurcation status. A 6.78 MHz prototype system has been built to verify the correctness of the mathematical analysis. The experiment demonstrates a lower switch voltage stress similar to the typical Class Phi(2) converter but without using the additional LC. The system has a high efficiency of 90.5% for a megahertz low-power system and exhibits zero-voltage switching independency over the power output range from 3 W to rated 30 W.

Recent studies have focused on developing underwater wireless power transfer (UWPT) anti-misalignment methods to address the vulnerability of UWPT systems to large water flow fluctuations and unstable power transference. Autonomous underwater vehicles (AUVs) use irregular coupling mechanisms to ensure stable power transmission through constant mutual inductance, but these require complex structures and present docking difficulties. A prediction algorithm based on the mutual inductance surrogate model is proposed to achieve optimal prediction for coupling mechanisms with constant power transmission at misaligned positions. The relative position and posture parameters of a coupling mechanism simulation model were subjected to Latin hypercube sampling to construct a dataset. Subsequently, a back propagation (BP) neural network was applied to develop an omnidirectional surrogate coupling mechanism model to predict the mutual inductance value. The surrogate model and a genetic algorithm were used to optimize the coil posture for maintaining constant power transfer. Experimental validation reveals that at a 0.6 aspect ratio, the system can ensure constant mutual inductance and power within a 25% omnidirectional misalignment range with an average error in mutual inductance of only 0.43%. At a 10% acceptable mutual inductance drop threshold, the anti-misalignment ranges of the system increase to 2.11 times the pre-optimization range.

Modeling of Hammerstein–Wiener nonlinear systems has received a lot of attention in the signal processing community. However, all existing model identification methods may fail to provide a consistent parameter estimate for Errors-In-Variables (EIV) Hammerstein–Wiener systems, where both input–output data are contaminated by measurement white noises. In this paper, a bias-correction Least-Squares (LS) algorithm for consistent identification of EIV Hammerstein–Wiener systems with polynomial nonlinearities using noisy measurements is proposed. Firstly, the analytic expression for the estimated bias of the LS algorithm using noisy measurements for EIV Hammerstein–Wiener systems with polynomial nonlinearities is derived, which is caused by the correlation between the input–output signals and measurement noises. Secondly, a consistent estimation method for the bias-correcting term, including a recursive step and a cross-validation step based on the available noisy measurements only, is then proposed to estimate the unknown terms of noises variances and noise-free measurements in the estimated bias. The effectiveness of the proposed algorithm is demonstrated through a simulated example and a robot arm system.

This paper characterizes the performance of an autonomous WPT system under wide-multiple-parameter variations. The autonomous converter can maintain soft switching over the entire operating region, leading the system to present either constant current (CC) or constant voltage (CV) characteristics independent of wide changes in magnetic coupling, self-inductance, and loading conditions. The system has no other operating modes except CC and CV, and these two modes can transition adaptively. The transitional point between CC to CV is determined by theoretical analyzed. A prototype system is built to verify the system performance, and the experimental results show the output voltage is maintained between 9.2 to 12.7V as the coupling coefficient varies from 0.21 to 0.93, with the simultaneously load varies from 10Ω to 1000Ω. By contrast, the output current is able to maintained approximately constant when the coupling coefficient and the load changes from 0.05 to 0.21, with the simultaneously load from 0.5Ω to 10Ω, respectively. The experiment verifies the CC-CV transitional at a typical coupling 0.45. Apart from the coupling and load variations, the system shows excellent component tolerances as demonstrated by allowance of over 83% changes in the self-inductance and more than 100% of the tuning parameters.

Some nonlinear systems can be represented through linear parameter varying models. In this work, we address the estimation of continuous-time linear parameter varying models in output error form, using a refined instrumental variable method. A distinguished feature of a linear parameter varying model is that it has parameters that depend on an external signal called the scheduling variable. In this paper, we assume that the scheduling variable is noisy, a condition which is often met in practice, but not frequently considered in the literature. On the other hand, there are applications in which the noise-free version of the scheduling variable is smooth. Under such scenario we can simply filter the scheduling variable before estimating the linear parameter model. Nonetheless, there are cases where special smoothing techniques are required. In this study, we consider one of these special cases, and we use the well-known local regression method as smoothing technique. A numerical example based on a Monte Carlo simulation shows the benefits of the proposed approach.

Due to the capacity and costs of single-power electronic devices, the modularized inductive power transfer systems (MIPT) are widely used in high-power electronic applications. To achieve safe and efficient operation of high-power IPT technology, this article proposes a modularized topology design method with an inherent impedance decoupling feature. When the module number changes, the current and voltage stresses of the devices in the proposed system remain unchanged inside modules at the rated power. Moreover, the impedance of any module will not be affected by neighboring modules, especially in cases where the module number may change abruptly. Such a desired feature significantly improves the reliability of high power IPT systems. A 15 kW MIPT prototype with three modules connected in parallel is implemented to verify the validity of the proposed method. The result clearly shows that both the impedance and device stress are decoupled from the module number. The system exhibits a peak efficiency of 95.1% and over 92.4% when the power is between 2 and 15 kW.

This article proposes the design and implementation of a 60-kW electric vehicle dynamic wireless power transfer (EV-DWPT) system. The system utilizes a dual transmitter and dual receiver (DTDR) structure, and one inverter to active two transmitter coils, which enables it to achieve high output power with low power electronic modules and reduce the number of inverters. In the proposed scheme, a unipolar inductor is integrated into the receiver coil, which has the advantage of higher power density. In addition, a protection circuit is added to protect the charging circuit and the battery, improving the reliability of the system. In the EV-DWPT system with DTDR structure, based on the system modeling and analysis, the coupler design method is given while considering both power and magnetic field exposure time. Moreover, a 54-m-long dynamic charge path was constructed and a 60-kW EV-DWPT system was tested to evaluate the system performance while presenting the practical implementation challenges encountered during the construction of the demonstration. The system operates at 85 kHz with a 20-cm air gap from the transmitter coil to the electric bus chassis. The dc/dc efficiency reaches 87.5% when the transfer power is 62 kW.

The autonomous wireless power transfer (WPT) system with series–series compensation has the advantage of the output power being approximately independent of variations in the coupling coefficient and load within the bifurcation region. However, the system encounters the issue of nonuniqueness in steady-state oscillation frequency, influenced by factors, such as varying system parameters, unpredictable external disturbances, and initial conditions. To overcome this issue, this letter proposes a relay-switching technique for manipulating the oscillation frequencies of autonomous WPT systems. This technique utilizes an adjustable relay threshold to dynamically modify the phase relationship between the input voltage and current, guiding the system to operate at the desired oscillation frequency. Complementary to the relay-switching technique, a gain-shaping strategy is presented to enhance the current gain at the target frequency, thereby increasing the possibility of convergence to that frequency. Finally, an experimental setup is built to verify the effectiveness of the proposed method. A video demonstrating the switching of different steady-state oscillation frequencies is attached to this letter.

In magnetic coupling wireless power transfer systems, air gap and load changes are very common. Due to the presence of ferrite cores, changes in the air gap can lead to variations in coil parameters (mutual inductance and self-inductances), particularly in small air gap applications. This article proposes a constant current (CC) output method for LCC-LCC compensated WPT system with variable parameters. The proposed method utilizes an integrated magnetic coupler to provide a variable compensation inductance to offset output fluctuations caused by mutual inductance changes. In addition, compensation parameters for maintaining a CC output versus air gap and load changes are obtained through a particle swarm optimization (PSO) algorithm. An 800 W experimental setup is constructed to validate the effectiveness of the proposed method. The experimental results show that, when the output power varies from 400 to 800 W, within the air gap range of 15 to 50 mm, with a maximum self-inductance variation range of 23% and a maximum coupling coefficient variation range of 0.71 to 0.32, the system has a CC output characteristic with a maximum current fluctuation of only 4% and a maximum efficiency of 90.8%. A video demonstrating the variations in system key waveforms during air gap changes is attached to this article.

Wireless power transfer (WPT) systems are a kind of high-order, highly nonlinear, time-delay systems. The conventional circuit theory-based methods for modeling the system result in high-order models, so it may not be efficient in digital implementation, especially on cost-sensitive microcontrollers. Besides, the time delay will impair the feedback performance of the system, and even lead to closed-loop instability under incorrectly compensated. To solve the abovementioned problems, this article proposes to infer a low-order model for the system based on sampled data and then use this model to design the control system. More precisely, the proposed methodology consists of two steps. In the first step, a parsimonious modeling method is proposed to yield a low-order model of Hammerstein type plus time delay, which makes it possible to simulate the model response in a cost-sensitive microcontroller. Then, based on the model obtained in the previous step, the internal model control (IMC) is adopted to design the closed-loop control system. Benefiting from the accurate prediction provided by the model, the closed-loop controller can mitigate the effect of the time delay and track the set value quickly. Finally, experimental and comparative results are given to verify the effectiveness of the proposed method.

This article develops a method of input design for the identification of wireless power transfer (WPT) systems based on receding horizon D-optimization. The dynamic behavior of the system is characterized by a discrete-time, single-input, single-output (SISO) model, and the associated model parameters are estimated by a recursive least-squares (RLS) method. To improve the quality of the parameter estimates, the excitation signal of the system is designed based on receding horizon D-optimization. To improve the convergence and efficiency of the input design process, the sequential quadratic programming (SQP) method is employed to solve the receding horizon D-optimization. Numerical and experimental results are presented to demonstrate the effectiveness of the proposed method.

This paper concerns the parameter identification problem for Errors-In-Variables(EIV) Hammerstein-Wiener systems using available noisy measurements. Based on the least squares method and consistency, a Bias-Correction Least Squares(BCLS) algorithm is proposed to estimate EIV Hammerstein-Wiener systems. The major contribution of this study is to derive the estimation bias of the least squares method and to recursively represent the monomial of noiseless measurements as available measurements. The effectiveness of the algorithm is demonstrated through the identification of simulation example.

The single capacitance coupled wireless power transfer (SCC-WPT) technology facilitates improving the spatial freedom of the system to offer charging/supply services in 2-D planar. This article proposes a double-receiver SCC-WPT system with three-plate compact coupler to achieve free-position charging and superior system performance. The system consists of two independent receivers: one receiver adopts the LCLC-S topology to achieve constant-voltage (CV) output, while the other adopts the LCLC-M topology to achieve constant-current (CC) output and did not affect each other. By circuit analysis, a simplified equivalent circuit of the three-plate coupler is established and, on this basis, the CV and CC characteristics are theoretically analyzed. The effectiveness of the proposed system is verified by building a prototype system, where the coupler and parameters are designed and optimized. The experimental results show that the two receivers can realize CV output and CC output, respectively. The output characteristic of each receiver is almost unchanged when another receiver is removed or moved in. The receiver can obtain stable output power at any position within the transmitting plate.

In this article, a consistent subspace identification method (SIM) is proposed for block-oriented errors-in-variables Hammerstein systems. Due to that the existing SIMs using parity subspace based on noisy measurements may result in biased parameter estimates, we propose a scheme for the consistent system parameter estimation, which estimates the noise-free Hankel matrix using available noisy measurements and noise variances. A 2-D search method is proposed to estimate the unknown noise variances from available noisy measurements. After that consistent estimations of the Hammerstein system parameters can be then retrieved from the estimated noise-free Hankel matrix following the same algorithm framework of the existing SIMs using parity subspace. Two simulation examples are included to support the effectiveness and merits of the proposed method.

It is known that the wireless power transfer systems are characterized by high order and high nonlinearity. Traditional modeling methods require circuit element parameters to be known a priori or can be accurately measured, but in practical industrial applications, the measured value of the component parameters may be inaccurate, owing to the aging effect of circuit components, changes in coil position, or changes in load impedance. In view of the above problems, based on the input and output data of the system, this paper uses Gauss-Newton method to identify the equivalent dynamic model of the WPT system, which reduces the computational load and burden of model simulation by simplifying the system model, which is more suitable for low-cost applications.

Accurate modeling and state of charge (SOC) estimation of lithium-ion battery against the model uncertainty and data uncertainty are difficult tasks nowadays. In this paper, a model and data uncertainties-robust method is proposed simultaneous estimation of the model parameters and the SOC using an enhanced adaptive unscented Kalman filter (AUKF). An extended state observer is established to integrate all unknown variables including parameters and SOC into a vector. An covariance matching technique with adaptive forgetting factor is proposed to obtain uncertain model and data statistics, in combination with a singular value decomposition based unscented transform to guarantee the positive definiteness of the error covariance matrix. Furthermore, establishing new protocols to handle missing input and missing output separately, the battery SOC and parameters can be estimated from missing measurements. Benefits from above procedures, the proposed method is more robust to model uncertainties and the data uncertainties compared to the conventional SOC estimation method. The robustness of the proposed method is verified at different operation temperatures and dynamic load profiles. The results shows that the proposed method possesses high accuracy and excellent robustness.