Research On Multi-energy Matching And Adaptive Control Of Fuel Cell Electric Vehicle

Now, energy deficiency and environment pollution are two prominent global problems, developing electric vehicle is an important means to solve them. Fuel cell is becoming the first energy choice to drive electric vehicles for its merits, such as high energy conversion efficiency, zero emission, and low noise etc., but necessity of an auxiliary energy is determined by the character of power system and fuel cell. In this paper, the author accomplishes the research on adaptive matching control, taking fuel cell electric vehicle as research background and the multi-energy power system in the vehicle as research object. Main research results are as below:In this paper, the author analyses the character of some energy systems suitable for driving the electric vehicle, then discusses several topologies for the power system, finally proposes an adaptive matching plan for the multi-energy power system of fuel cell electric vehicle.After comparing and analyzing some topologies of power converters, the author chooses the bidirectional half-bridge non-isolation topology designing main circuit of the energy management unit. According to performance requirements of the whole power system, parameters of the main circuit are calculated. The control circuit and communication interface circuit of the energy management unit, and driver and protection circuit for IGBT, have been designed with DSP.as a core controller.After thoroughly studying the characters of fuel cell and auxiliary energy, the operation modes of the fuel cell electric vehicle has been analyzed. According to energy matching control goals and principles, the author has proposed a real-time power adaptive following control algorithm to realize energy matching in the electric- electric hybrid power system.In view of the nonlinearity and parameters time variability of loads at output ports, while the bidirectional power converter time-sharing working in two modes, a compound controller, based on multimode control theory, has been designed, containing Bang Bang control and neural network PID. In big deviation mode, Bang Bang control has been used to enhance the speed of dynamic response, while in middle and small deviation mode, the author has designed PID regulators for power system output voltage and auxiliary energy current, based on BP neural network. The regulators make use of good approaching ability to nonlinear plant and strong self-learning capability of the BP neural network, and good tracking performance and quick response characteristic of PID controller.