The Research And Optimization Of A Parallel Hybrid Electric Vehicle Energy Management Algorithms Using Driving Pattern Recognition

By the advantages of the economics, the hybrid vehicles have the bright future in the automotive industry. But it should not be overlooked is that hybrid vehicles in the design and operation are still many issues to be resolved, such as the coordination of the energy and control of dynamic systems, and associated the definition of transmission,the components need to consider,other options and other factors.In this dissertation, the research is about parallel hybrid electric vehicle power management algorithms. During parallel hybrid electric vehicle operation process, particularly in the transmission of the energy flow coordination issues, there are two main aspects: the engine torque distribution and battery power balance. The first relates to the process to meet the needs of the driver torque under the premise of the use of the power source. The second aspect is reflected in the hybrid electric vehicle battery under the premise of continued control traffic capacity. These two aspects are the main issues :energy management system in the coordination of power sources and energy flow.This dissertation deals with a conceptual design of new intelligent energy management system applicable to parallel HEVs. This design was motivated by some limitations of existing approaches for energy management control, whose main stream focused on single-layered approaches that use only the current vehicle state for decision-making in connection with torque distribution and charge sustenance tasks; little consideration is generally given to driving situations and driving style of the driver. As a result, the majority of the proposed concepts do not address the effects of variations in driving situations on the vehicle emissions and fuel consumption over the spectrum of driving situations to which the vehicle may be subjected. In this dissertation, a \driving situation awareness"-driven intelligent energy management system for parallel HEVs is proposed and developed. A key concept adopted in the development of an energy management system is based on the idea that driving environment (situation) as well as the driving modes of operation of the vehicle and the driving style of the driver directly a ect fuel consumption and pollutant emissions.Jilin University shoulders one the 863 Project: the development of control system of hybrid electric transit bus power train. Depending on this project, the thesis is about the parallel hybrid electric vehicle energy management algorithms for the city bus. The main contents are as follows:First of all, a forward simulation model of the HEV is constructed in the MATLAB/Simulink——CRUISE environment using empirical modeling approach with the aid of theoretical modeling. It provides the necessary simulation platform for the development of the control strategy.With the application of orthogonal optimization design to the characteristic parameter design of the standard cycle run, the impact of the HTB's important design parameters on the fuel economy was analyzed synthetically in order to find out the primary and secondary factors including battery capacity, control parameters etc, which set the foundation for the further research.In the previous vehicle structures and control platform strategy , on the basis of which a"driving situation awareness" module is added to , objectives are: under real-time operating speed, contrast the main characteristics of parameters to the status of standard working conditions, after identification by the standard parameters on the control status of the vehicle energy management adaptive algorithm, and finally to balance the SOC automatically, the status of Identity Module according to the normal cycle of a certain reasonable outcome given the correct identification.Finally using of the first vehicle performance evaluation indicators, which integrated consideration of vehicle fuel economy and battery SOC changes, evaluation and use based on that to the above energy management algorithm guidance control strategy simulation results analysis to prove that the reliability of identification , fuel economy and SOC both have increase by adaptive algorithm in energy management.