| Nowadays,facing the serious air pollution and excessive consumption of petroleum resources caused by the growth of fuel vehicle population worldwide,major automotive companies are devoting themselves to developing new energy vehicles which are high-efficient and clean.Because electric power is the most widely used and most convenient energy,the research and development of electric vehicles has received special attention.Hybrid electric vehicle(HEV)is considered to be the easiest technical scheme to realize industrialization and be accepted by consumers.It has both engine drive system and electric drive system,combining the advantages of high power density of traditional internal combustion engine vehicle and energy saving and low emission of pure electric vehicle.HEV has become a research hotspot in the automotive industry.In this paper,based on the actual project "Development of Hybrid HCU of a HEV ",the energy optimization and dynamic coordination control problem in the process of mode switching of CVT parallel HEV are studied.The main research content includes the following elements:Firstly,the vehicle steady-state simulation control model is built based on AMESim and the rule-based energy management strategy is developed by Matlab/Simulink.The rationality of proposed strategy is verified by joint simulation which can obtain the fuel economy of the vehicle.The above part lays foundation for the development of energy optimization algorithm and mode switching coordination strategy.In order to ensure the optimal power allocation of the system to improve the fuel economy,the dynamic programming algorithm is used to optimize the output power of the engine and motor globally with the control target of minimizing equivalent fuel consumption.Aiming at the energy optimization effect,the average integrated energy transfer efficiency is defined and the theoretical fuel consumption calculation model of the system is put forward.And then the contribution of the main fuel-saving factors is quantitatively analyzed.Secondly,the modeling of dynamic characteristics for key components such as engine and wet clutch is studied.Because of the obtained dynamic characteristic data of engine from the real vehicle project,the dynamic torque model of the engine is trained with BP neural network tool,and the effect of the training model is verified.As for the hydraulic execution system of the wet clutch,based on the analysis of its structure,the detailed modeling and analysis of the key components like the proportional pressure solenoid valve and the hydraulic cylinder are carried out.In addition,in order to tracking the output oil pressure of the hydraulic execution system,the traditional PID and feedforward-feedback control algorithm are adopted based on the model,and the tracking control effect is verified by comparison.Thirdly,the sectional coordination control strategy for typical mode switching process is developed.Based on the rule-based steady-state energy management strategy,the different working modes of the system are classified according to the state of the wet clutch,and the typical switching process from pure electric driving to engine-driven charging mode is selected for research.The process is divided into several stages and analyzed dynamically.Considering the torque response characteristics of the engine and the dynamic operation process of the wet clutch,the sectional coordination control strategy based on model predictive algorithm is developed.The strategy enables the engine,motor and wet clutch to cooperate and coordinate in the process of mode switching,so as to achieve a smooth mode switching.Lastly,the hardware-in-the-loop(HIL)and real vehicle test verification work are carried out.The HIL simulation platform is designed to verify the real-time performance of the developed strategy,and the rationality and smoothness of mode switching in the process of driving are verified by the real vehicle test. |
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