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In order to develop continuously variable transmission (CVT) suited for electric vehicle (EV), a comparative analysis of the transmission used on EV is introduced in this paper. To solve the problems of traditional CVT used for EV, a newly designed electric CVT (ECVT) is proposed. The final drive ratio, speed ratio range of variator and hydraulic system of ECVT are redesigned for improving the transmission efficiency. Considering that the permanent magnet synchronization traction motor (PMSTM) of EV has an ideal driving characteristic (Constant torque at low speed, constant power at high speed) and wider high efficiency area compared with gasoline engine, the speed ratio control strategy of ECVT is studied again for improving the vehicle driving range and better meeting the requirements of driver. Finally, a simulation test system is fabricated for evaluating the battery energy consumption of an EV equipped with ECVT and single-speed transmission (SST) under constant speed and road drive cycle. The simulation results show that the ECVT proposed can extend the endurance mileage of EV at the speed of 20 to 90km/h, and improve the acceleration performance from 0 to 50km/h. The EV equipped with ECVT showed more advantages compared with SST in urban conditions.

Modern neural networks [e.g., Deep Neural Networks (DNNs)] have recently gained increasing attention for visible image classification tasks. Their success mainly results from capabilities in learning a complex feature mapping of inputs (i.e., feature representation) that carries images manifold structure relevant to the task. Despite the current popularity of these techniques, they are training-costly with Back-propagation (BP) based iteration rules. Here, we advocate a lightweight feature representation framework termed as Guided Random Projection (GRP), which is closely related to the classical random neural networks and randomization-based kernel machines. Specifically, we present an efficient optimization method that explicitly learns the distribution of random hidden weights instead of time-consuming fine-tuning or task-independent randomization configurations. Further, we also report the detailed mechanisms of the GRP with subspace theories. Experiments were conducted on visible image classification benchmarks to evaluate our claims. It shows that the proposed method achieves reasonable accuracy improvement (more than 2%) with moderate training cost (seconds level) compared with other randomization methods.

Nonideal switching characteristics of voltage source inverter (VSI) will result in distortions in both phase currents and reference voltages of a permanent magnet synchronous machine (PMSM) drive system, which will also consequently introduce ripples into the output speed and shaft torque, and result in a performance deterioration in the state observation control. To solve this issue, a method of directly extracting and compensating the nonlinear voltage disturbance due to VSI nonlinearity employed in PMSM drives is proposed in this paper. Firstly, the distorted voltage due to VSI nonlinearity is directly extracted via a harmonic separation scheme, which can be easily compensated by a proportional integral controller afterwards. Since both 6th harmonic and DC terms of dq-axis distorted voltages due to VSI nonlinearity are well compensated, the proposed method shows sufficiently good performance under both $i_{d}=0$ and maximum torque per ampere controls ( $i_{d} < 0$ ), as verified on two prototype PMSMs being salient pole and non-salient pole, respectively, both developed for a low-speed rotary actuator. In addition, the application of proposed method to state observation controls such as the model-based sensorless control and the deadbeat predictive current control, is finally investigated. Test results manifest that the proposed method can effectively improve the accuracy of observed rotor position/speed, and the ability of current tracking.