Papers by Author: Yu Kun Sun

Abstract: Due to the lack of cruising ability in HEV(Hybrid Electric Vehicle), along with concerns about environmental issues, a hybrid power source built from a battery and an ultracapacitor is used as vehicular power source and is charged during braking processes. Based on the rule of “the lower ultracapacitor voltage, the less battery charging; the higher ultracapacitor voltage, the more battery charging”, this paper adopts a fuzzy logic control strategy to supervise the braking energy. Simulation results obtained using MATLAB/SIMULINK indicate that this method can effectively manage the energy distribution during regenerative braking processes and extend driving distance. Furthermore, the present approach provides an improvement in fuel economy and reduces pollutant emissions.

Abstract: In order to take the full advantages of battery and ultracapacitor of hybrid energy storage (HES) for hybrid electric vehicles (HEV) and solve the power allocation problems of the two energy storages when the working condition was changing, we propose a grid partition (GP) and adaptive fuzzy neural network (AFNN) control strategy. Firstly, the structure of AFNN wad determined by GP; Secondly, by adopting the back-propagation algorithm and least square method respectively, the front and back parameters of the AFNN were optimized, and the study efficiency of the parameters was raised. Finally, use the fuzzy membership functions and rules which generated automatically by AFNN in the control of HES for HEV. Under the ADVISOR 2002 simulation environment, verify the control strategy on the base of Urban Dynamometer Driving Schedule (UDDS) working condition. The results show that the battery and ultracapacitor could give full play to their respective advantages when the GP and AFNN control strategy was adopted, so the efficiency of the vehicle energy storage system could be enhanced and a higher efficiency of the braking energy recovery be obtained.

Abstract: The performance of the switched reluctance motor (SRM) greatly depends on magnetic properties of core material since it consists of only laminated-core and windings. This paper analyses the performance of an SRM with amorphous stator, which has very low iron losses. Copper loss, iron loss and efficiency of the SRM are estimated by finite element method. Furthermore, a suitable structure for the SRM with amorphous stator is examined on the optimization design.

Abstract: Radial weight and rotary torque load often demand large winding current in a bearingless switched reluctance motor (BSRM). This will tend to cause magnetic saturation. But traditional mathematic model can not fit for this saturated working state, which has formatted a sever limitation. With a BSRM model in Maxwell, its magnetic saturation characteristics were analyzed, and a critical criterion was computed. Then a novel mathematic model was established with Maxwell tensor method and confirmed by Finite element computing results. It could fit for both unsaturated and saturated working state, and also satisfy reversibility condition. These were both very useful for nonlinear decoupling with state feedback method and wide application in industry process. This proposed modeling and analyzing method could also provide useful references for motor’s optimization design and control algorithm research.

Abstract: A novel hybrid-stator bearingless switched reluctance motor (HSBSRM) was researched. After analyzing magnetic linkage characteristics, its mathematical model was built with finite element method, and a radial displacement self-sensing method was designed. This hybrid stator motor has unique advantages compared with those traditional bearingless switched reluctance motors, so radial displacement self-testing techniques have important research significance and practical value. Simulation and experimental results validated the proposed methods.

Abstract: Based on the introduction of the major problems in solid-state fermentation of straw to produce fuel ethanol and its development direction, this paper systematically discuss the process that currently used.Through analyzing the characteristics of all phases such as pretreatment, hydrolysis and fermentation and so on,we put forward a new kind of process scheme. In this process, we use a mixed method which combined dilute sulfuric acids with steam explosion pretreatment method, after water washing, adopt the method of synchronized saccharification solid-state fermentation,by adding pichia and cellulase enzyme hydrolysis,and we can get ethanol solution at the same time of enzyme hydrolysis. Then through filtered, purified, denatured and so on, at last, we get the fuel ethanol. The equipment that this process requires is simple, and its fermentation have high efficiency high and low energy consumption.

Abstract: Biological fermentation process is a complex nonlinear dynamic coupling process. As it is very difficult to measure the key biological parameters on line, the process control is unavailable to industrial production in time. In this respect, however, soft sensing can solve the above problem. To overcome some drawbacks of PSO and FNN, such as falling into local minimum occasionally and slow convergence speed, the extremum disturbed particle swarm optimization (tPSO) algorithm is proposed and then combined with fuzzy neural network (FNN) to optimize the network parameters. Furthermore, the tPSO-FNN is applied in the soft sensor modeling of lysine biological fermentation. Experiment results show that the model proposed could measure the key parameters. And the soft sensor model based on tPSO-FNN has higher precision and better performance than the model based on FNN.