In this paper, a bias-correction least-squares (LS) algorithm is proposed for identifying block- oriented errors-in-variables nonlinear Hammerstein (EIV- Hammerstein) systems. Because both the input and output of the EIV-Hammerstein system are observed with additive white noises, the estimation bias of traditional LS algorithm is introduced. The estimation bias is derived from a consistency point of view, which is a function about noise variances and monomial of noiseless system input–output measurements. A bias-estimation scheme based only on the available noisy measurements is then proposed for consistent identification of the monomial of noiseless system input–output measurements in a recursive form. In particular, a specific algorithm based on minimizing the output prediction error is given to find out the unknown noise variances for practical applications, such that the noise effect can be eliminated and the consistent estimated parameters are obtained. The effectiveness of the proposed method is demonstrated by a simulation example and an experimental prototype of wireless power transfer system.

A gray-box parsimonious subspace identification method using sampled dynamical and steady-state data is proposed for block-oriented Hammerstein-type systems. Compared with the conventional subspace identification methods based on over-parameterized models, the proposed method assumes parsimonious models, where the number of parameters is as minimal as possible to ensure the model accuracy, especially for highly nonlinear models. Generally, the available dynamical data do not contain adequate information on the low-frequency characteristics of the system. To improve the accuracy of mode identification, the steady-state information of the system is taken into account, and a multi-regularization method is developed, where the whole model parameters are estimated in a hierarchical, iterative manner from two sets of data: the dynamical input-output data and the steady state input-output data. The use of parsimonious models and hierarchical estimation can significantly reduce the size the associated parameter estimation error covariance matrix, thus improving the model accuracy and decreasing the variance of the estimated parameters, compared with the over-parametrization methods only using dynamica data. The effectiveness and merits are demonstrated by a simulation example and a real-word example.

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.

This paper proposes the design and implementation of a 60kW electric vehicle dynamic wireless power transfer (EV-DWPT) system. The system utilizes a dual transmitter 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 numbers of inverters. In the proposed scheme, a unipolar inductor is integrated into the receiver coil, which has the advantage of higher power density. Additionally, 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 modelling and analysis, the coupler design method is given, while taking into account both power and magnetic field exposure time. Moreover, a 54m long dynamic charge path was constructed and a 60kW EV-DWPT system was tested to evaluate system performance while presenting the practical implementation challenges encountered during the construction of the demonstration. The system operates at 85kHz with a 20cm air gap from the transmitter coil to the electric bus chassis. The DC/DC efficiency reaches 87.5% when the transfer power is 62kW.

This paper investigates control-oriented modeling of a class of wireless power transfer systems with a phase-shift controlled inverter. Two methods are studied: the conventional analytical modeling method which is based on physical laws, and the refined instrumental variable method which is based on sampled data. It shows that the former provides good physical insights into the system, and that the resulting models are accurate when the true circuit component parameters are known. However, poor results may be obtained if this condition is not satisfied. By contrast, the later provides a cheap solution for accurate modeling, which requires a set of sampled input-output data only and the resulting model can capture very well the dynamic behavior of the system within its operating range. To have virtues of both methods, it is therefore suggested to use them as a combination, that is, using the analytical method to determine the model order while using the data-driven method to estimate the model parameters. All results derived in this paper are verified by means of both numerical simulation and experimental validation.