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To compete with battery electric vehicles, fuel cell (FC) hybrid electric vehicles (FCHEVs) are required to offer better performance in fuel economy and FC durability. To this end, this article proposes a novel real-time adaptive model predictive control (AMPC)-based energy management strategy (EMS) for FCHEVs to improve their fuel efficiency and mitigate the degradation of their onboard FC hybrid systems. First, a linear parameter-varying (LPV) prediction model of the FC hybrid system that considers the system parameter variation is developed. The model offers sufficient accuracy while enabling the real-time implementation capability of the AMPC. Then, an AMPC strategy is proposed to optimally distribute the load current of the FCHEV between the FC and the battery in real time. In each control interval of the AMPC, the LPV prediction model is updated online to adapt to the variations of the battery state of charge. The constrained optimization problem of the AMPC is then formulated to achieve a desired tradeoff among four performance metrics and is further transformed into a quadratic programming problem, which can be solved in real time. Hardware-in-the-loop tests are performed on a downscaled FC hybrid system with the proposed AMPC-based EMS, a commonly used rule-based EMS, an equivalent consumption minimization strategy, and an improved MPC-based EMS, respectively. Results show that among the four real-time EMSs, the AMPC-based EMS achieves the best performance in reducing hydrogen consumption and FC current fluctuation and the smallest optimality gap with respect to an offline dynamic programming-based optimal EMS. [ABSTRACT FROM AUTHOR]

Permanent-magnet synchronous generators (PMSGs) are commonly used for small variable-speed wind turbines to produce high-efficiency, high-reliability, and low-cost wind power generation. This paper proposes a novel control scheme for an interior PMSG (IPMSG) driven by a wind turbine, in which the d-axis and q-axis stator-current components are optimally controlled to achieve the maximum wind power generation and loss minimization of the IPMSG. The effect of magnetic saturation, which causes the highly nonlinear characteristics of the IPMSG, is considered in the control-scheme design. The optimal d-axis stator-current command is obtained as a function of the IPMSG rotor speed by solving a constrained nonlinear-optimization problem that minimizes the copper and core losses of the IPMSG. At any wind speed within the operating range, the IPMSG rotor speed is optimally controlled to extract maximum wind power. The optimal q-axis stator-current command is then obtained from the optimal IPMSG rotor speed and d-axis current. To eliminate the effects of nonlinearity caused by magnetic saturation, an input--output feedback linearization technique is applied to design the high-performance nonlinear current controllers. The proposed control scheme provides the wind generation system with the maximum efficiency and high dynamic performance. Index Terms--Input--ouput feedback linearization (IOL), loss minimization, magnetic saturation, maximum wind power generation, permanent-magnet synchronous generator (PMSG).

A method to extract low-order dynamic models of nonlinear magnetic devices from a high-order physics-based model is presented. The model includes saturation, skin effect, and eddy current. The physics-based model is derived from finite-element analysis, using only geometry and materials data. A nonlinear model order reduction method based on the Krylov subspace projection is used to extract the low-order model. A linear system is set up at each interpolation point along a training trajectory. Each linear system is reduced by the corresponding projection matrix. The resulting reduced linear systems are used to approximate the original system within each region, yielding accurate local simulation results. Good global properties are achieved by the use of piecewise simulation and weighting. Simulation results demonstrate that the original, nonlinear high-order system can be represented by a set of connected low-order, piecewise-linear systems with satisfactory agreement. This method can be automated and should provide a foundation for more complicated magnetic devices, such as multiphase coupled inductors, actuators, and rotary machines. Index Terms--Finite element analysis (FEA), inductor, Kryiov subspace, model order reduction (MOR).