Dynamic Current Control to Compensate for Magnetic Mutual Coupling in Electrically Excited Synchronous Machines

Electrically excited synchronous machines have become an attractive solution to electric vehicles. Equipped with a field winding in the rotor, the excitation of the machine is controllable. However, due to the magnetic mutual coupling between the stator and rotor windings, a voltage will be induced in the field winding in case of a current rise in the stator winding and vice versa. In this study, a dynamic current control algorithm with compensation for magnetic mutual coupling is proposed. A first-order response of current rise is expected. To achieve this, the controller consists of three parts. The first part is the feed forward of cross-coupling terms due to Park transform. The second part takes care of the resistances and selfinductances. The third part takes care of the mutual inductances. Finally, the outputs from the three parts are summed up to be the total output from the controller

Design of Electrically Excited Synchronous Machines to Achieve Unity Power Factor in Field Weakening for Long-Haul Electric Trucks

Electrically excited synchronous machines are a promising candidate for long-haul electric trucks due to excellent capability in field weakening. This study aims at concluding a structural design process of the machine for long-haul electric trucks. A criterion of machine design to achieve unity power factor in field-weakening is derived. With this criterion, a minimum level of field current is decided in the design process. Parametric sweeps are applied to decide the optimum slot geometries for stator and rotor. The optimization of slot geometries is multi-objective. In this study, it is to maximize the peak torque while minimize iron-core losses simultaneously. Pareto frontier is used to identify the optimum solutions. The performance of the finalized design is then evaluated. The high efficiency area is located at high-speed low-torque region which is preferable for long-haul electric trucks. Balance is achieved between copper and iron-core losses during steady-speed intervals of the test cycles which leads to minimum losses in total

Convective Heat Transfer Coefficients and Mechanical Loss Evaluation of Oil Splashing in Direct Cooled Electrically Excited Hairpin Motors

There in an increasing trend in the use of the direct oil cooling in electric motors for automotive because of the increasing demand of high power/torque density as well as overload capability. One of the most immediate solution is to fill the housing with some oil level and benefit of the heat transfer from the oil splashing. The mechanical losses coming from the rotor rotation are well known and they represent a significant challenge, especially at high speed and high oil level. Therefore, the derivation and prediction of these losses have not been properly investigated leading to a lack in the current literature. Moving Particles Simulation (MPS) method is used in Particleworks to calculate the mechanical losses caused by the oil viscosity and convective heat transfer coefficients (HTC) are extracted for a 250 kW Electrically Excited Synchronous Machine at different speeds and oil levels

Observations of Field Current and Field Winding Temperature in Electrically Excited Synchronous Machines with Brushless Excitation

Electrically excited synchronous machines have become an alternative in electrification of transportations and renewable power generations. To reduce the extra effort in the maintenance of sliprings and brushes for field excitation, brushless excitation has been developed. However, when brushless excitation is adopted, the field winding becomes physically inaccessible when the machine is rotating. To solve this problem, an algorithm is proposed in this study to observe the field current and field winding temperature of an EESM with brushless excitation. The stator currents are measured and then used to correct the machine state predictor. The correction of the state prediction is interpreted to adjust the field winding resistance and temperature value. The algorithm is evaluated in simulations. The estimations of field current and field winding temperature track the measurements successfully

Learning Adaptive Display Exposure for Real-Time Advertising

In E-commerce advertising, where product recommendations and product ads are presented to users simultaneously, the traditional setting is to display ads at fixed positions. However, under such a setting, the advertising system loses the flexibility to control the number and positions of ads, resulting in sub-optimal platform revenue and user experience. Consequently, major e-commerce platforms (e.g., Taobao.com) have begun to consider more flexible ways to display ads. In this paper, we investigate the problem of advertising with adaptive exposure: can we dynamically determine the number and positions of ads for each user visit under certain business constraints so that the platform revenue can be increased? More specifically, we consider two types of constraints: request-level constraint ensures user experience for each user visit, and platform-level constraint controls the overall platform monetization rate. We model this problem as a Constrained Markov Decision Process with per-state constraint (psCMDP) and propose a constrained two-level reinforcement learning approach to decompose the original problem into two relatively independent sub-problems. To accelerate policy learning, we also devise a constrained hindsight experience replay mechanism. Experimental evaluations on industry-scale real-world datasets demonstrate the merits of our approach in both obtaining higher revenue under the constraints and the effectiveness of the constrained hindsight experience replay mechanism.Comment: accepted by CIKM201