27 research outputs found
Plug-in, Direct Flux Vector Control of PM Synchronous Machine Drives
A general-purpose control algorithm is proposed for permanent-magnet (PM) synchronous machine drives based on the principle of direct-flux vector control. The algorithm does not require regulator tuning, and it is tailored to different machines automatically via identification of the stator resistance and flux linkage tables. The model parameters are identified via a preliminary self-commissioning procedure that can be integrated into the standard drive firmware with no need for extra hardware or offline manipulation. The combination of the control and self-commissioning algorithms forms a “plug-in” controller, which pertains to a controller that is capable of exploiting the full drive capabilities with no prior knowledge of the PM machine in use. Experimental results are reported for two prototype concentrated-winding PM machines designed for traction applications, i.e., one with a surface-mounted PM rotor and another with an interior PM rotor
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FINAL REPORT ON CONTROL ALGORITHM TO IMPROVE THE PARTIAL-LOAD EFFICIENCY OFSURFACE PM MACHINES WITH FRACTIONAL-SLOT CONCENTRATED WINDINGS
Surface permanent magnet (SPM) synchronous machines using fractional-slot concentrated windings are being investigated as candidates for high-performance traction machines for automotive electric propulsion systems. It has been shown analytically and experimentally that such designs can achieve very wide constant-power speed ratios (CPSR) [1,2]. This work has shown that machines of this type are capable of achieving very low cogging torque amplitudes as well as significantly increasing the machine power density [3-5] compared to SPM machines using conventional distributed windings. High efficiency can be achieved in this class of SPM machine by making special efforts to suppress the eddy-current losses in the magnets [6-8], accompanied by efforts to minimize the iron losses in the rotor and stator cores. Considerable attention has traditionally been devoted to maximizing the full-load efficiency of traction machines at their rated operating points and along their maximum-power vs. speed envelopes for higher speeds [9,10]. For example, on-line control approaches have been presented for maximizing the full-load efficiency of PM synchronous machines, including the use of negative d-axis stator current to reduce the core losses [11,12]. However, another important performance specification for electric traction applications is the machine's efficiency at partial loads. Partial-load efficiency is particularly important if the target traction application requires long periods of cruising operation at light loads that are significantly lower than the maximum drive capabilities. While the design of the machine itself is clearly important, investigation has shown that this is a case where the choice of the control algorithm plays a critical role in determining the maximum partial-load efficiency that the machine actually achieves in the traction drive system. There is no evidence that this important topic has been addressed for this type of SPM machine by any other authors. This topic takes on even greater significance for fractional-slot concentrated-winding SPM machine designs. In particular, maximizing the torque/power density of this class of SPM machines typically leads to machine designs with high numbers of poles. The resulting high electrical frequencies can easily result in high stator core losses unless special care is taken during the machine design process. The purpose of this report is to discuss a modified vector control algorithm for a fractional-slot concentrated winding SPM machine that has been developed to maximize the machine's partial-load efficiency over a wide range of operating conditions. For purposes of this discussion, a 55 kW (peak) SPM machine designed to meet requirements established in the US FreedomCar program [13] is used as the basis for demonstrating the proposed technique. A combination of closed-form analysis [14] and finite element analysis (FEA) is used during this investigation