Optimization Study On Powertrain Matching And System Control For Plug-in Hybrid Electric Urban Bus

The shortage of energy and the environmenta l deterioration are currently important factors which restrict the sustainable development of the global economy and society, and the automobile industry is one of the main sources of energy crisis and environmental pollution, so it is strategically significant to study and develop the electrical vehicles(EVs) that could save energy and reduce the pollution. Among them, the plug- in hybrid electric vehicle s(PHEVs) are the most promising energy-saving types with high industrialization and marketabilit y for the coming period. The parameters matching of the hybrid powertrain and the development of the energy management strategy are two key technologies in the research and development process for hybrid electric vehicle(HEV). In this dissertation, the optimal parameters matching and the optimal energy management for the hybrid powertrain of PHEV are investigated.Considering the objectives of PHEB development and the characteristics of the target driving cycle for bus, the structure of PHEB powertrain is designed. And, the characteristics of the single-axle series-parallel PHEB are analyzed, including the potential operating mode and the powertrain power flow. Based on the power demand characteristic of the target driving cycle, the cycle-based parameters matching method for hybrid powertrain is proposed through comprehensively analyzing the power output and the operational efficiency characteristics of each power unit.The Dynamic Programming(DP) algorithm is applied to solve the optimal energy management problem of the PHEVs. The state and control variables are determined based on the structure characteristics of the PHEB hybrid powertrain, and the optimization mathematical model and the control-oriented static backward simulation model are constructed. The pretreatment method, which previously determine the effective control- variable set based on the hybrid powertrain characteristic, and the parallel computing method for solving the DP are proposed, which effectively decrease the DP computation load. The numerical issues, including the discretization resolution of the relevant variables and the boundary issue of their feasible regions, are considered when implementing DP to solve the optimal control problem of the PHEVs. The compromise between the optimization accuracy and the computational burden for using DP algorithm is systematically investigated.The hybrid powertrain parameters optimization method based on the optimized energy management strategy is proposed, which ensures the fairness of performance evaluation of every powertrain scheme. The parameters optimization of the PHEB hybrid powertrain is mathematically modeled. The integrated optimization platform for the PHEB hybrid powertrain is established based on the professional optimization software “Isight”.The exploratory optimization method and the numerical optimization method are combined to design the combinational optimization algorithm to improve the optimizing efficiency. The pretreatment method of the simulation condition, which synchronously reduces the battery capacity and the distance of target cycle with the same proportion, is proposed. This manyfold and efficiently decreases the computation load during searching the optimum powertrain scheme. The final parameters of the PHEB hybrid powertrain are determined, by which the fuel economy increases by about 4.4% comparing to the initial un-optimized case.The basic PHEV operation modes are defined based on the battery state of charge(SoC) profile, including the pure electric driving(PED), the hybrid driving charge depleting(HDCD) and the hybrid driving charge sustaining(HDCS). For the PHEB, three different energy management strategies, which are combined with two or three of the basic operation modes, are put forward and comparatively exa mined based on the co-simulation platform integrating AVL CRUISE and Matlab/Simulink. If some trip information can be approximately known in advance such as the trip distance and the mean power demand, the PED+HDCD+HDCS strategy comprises optimally of the PED mode, the HDCD mode and the HDCS mode would be the best rule-based energy management strategy.By introducing the optimization theory to the model predictive control(MPC), the global optimization problem is transformed into the local optimization prob lem in the prediction period. Using DP to solve the optimization problem within the prediction period, the scrolling micro-scale DP is constructed. The multi- Markov model is used to predict the vehicle speed within the forecast horizon, and the MPC-based PHEB energy management strategy is proposed. The CCP-based online measurement/calibration system for the vehicle control unit(VC U) is developed, and its function is verified by the online experiments. The effectiveness of the simulation experiment based on the co-simulation platform for the PHEB is verified by vehicle testing.