Yafu Zhou

Research of Ant Colony Optimized Adaptive Control Strategy for Hybrid Electric Vehicle

Energy management control strategy of hybrid electric vehicle has a great influence on the vehicle fuel consumption with electric motors adding to the traditional vehicle power system. As vehicle real driving cycles seem to be uncertain, the dynamic driving cycles will have an impact on control strategy’s energy-saving effect. In order to better adapt the dynamic driving cycles, control strategy should have the ability to recognize the real-time driving cycle and adaptively adjust to the corresponding off-line optimal control parameters. In this paper, four types of representative driving cycles are constructed based on the actual vehicle operating data, and a fuzzy driving cycle recognition algorithm is proposed for online recognizing the type of actual driving cycle. Then, based on the equivalent fuel consumption minimization strategy, an ant colony optimization algorithm is utilized to search the optimal control parameters “charge and discharge equivalent factors” for each type of representative driving cycle. At last, the simulation experiments are conducted to verify the accuracy of the proposed fuzzy recognition algorithm and the validity of the designed control strategy optimization method.

Design of doubly salient permanent magnet motor for small displacement ISG mild hybrid

The performance of hybrid motor directly affects hybrid vehicle performance and fuel economy. In this paper, doubly salient permanent magnet motor is selected as vehicle motor of small displacement ISG mild hybrid. Magnet field superposition method is proposed to analyze the static characteristics of doubly salient permanent magnet motor. Namely, the “partition method” is adopted to analyze magnet field static characteristics when excitation windings operate alone. Then two-dimensional finite element is adopted to analyze magnet field static characteristics when permanent magnet operates alone. Doubly salient permanent magnet motor static characteristics are obtained by superposition, and motor structural parameters are designed through analogy. Finally, through physical prototype test, objectively test the performance of the prototype design to achieve the research and design of hybrid doubly salient permanent magnet motor.

Traffic Scene Segmentation Based on RGB-D Image and Deep Learning

Semantic segmentation of traffic scenes has potential applications in intelligent transportation systems. Deep learning techniques can improve segmentation accuracy, especially when the information from depth maps is introduced. However, little research has been done on the application of depth maps to the segmentation of traffic scene. In this paper, we propose a method for semantic segmentation of traffic scenes based on RGB-D images and deep learning. The semi-global stereo matching algorithm and the fast global image smoothing method are employed to obtain a smooth disparity map. We present a new deep fully convolutional neural network architecture for semantic pixel-wise segmentation. We test the performance of the proposed network architecture using RGB-D images as input and compare the results with the method that only takes RGB images as input. The experimental results show that the introduction of the disparity map can help to improve the semantic segmentation accuracy and that our proposed network architecture achieves good real-time performance and competitive segmentation accuracy.

Study on Feed-Forward MAP-Based Vector Control Method of Vehicle Drive Motor

Contraposing to the shortage of narrow efficient area and over current when vector control method is applied to vehicle drive motors, this paper proposes a feed-forward MAP-based vector control method of vehicle drive motor. According to required motor torque and speed, directly control the magnitude and direction of voltage space vector to realize the aim of motor torque control. So, the calculation of torque current component and field current component is no need, which not only avoids over current that the PID closed-loop control leads to, improving the reliability of controller, but also avoids the dependence on current measurement, improving control precision and motor efficiency. Finally, simulation results and motor bench test prove this method can significantly enlarge efficient area, and it is suitable for vehicle running conditions.