Research On Energy Management Control Strategy For Plug-in Hybrid Electric Vehicle In Vehicle-following Scenario

With the development of intelligent network technology,different vehicle state and driving cycle information can be transmitted via vehicle-to-vehicle/vehicle-to-infrastructure(V2V/V2I)in real time.The use of V2V/V2 I was effective in improving the energy management performance of plug-in hybrid electric vehicle(PHEV).Compared with separate PHEV,how to keep the PHEV safe and reduce fuel consumption during vehiclefollowing scenarios had become an urgent issue to be solved.The complexity of urban public conditions that could cause driver fatigue and rear-end collision accidents.Therefore,the dissertation took plug-in hybrid electric bus(PHEB)as the research object.The energy management control strategy based on model predictive control during vehicle following scenario was studied.The specific research contents were as follows.Firstly,considering the multi-objective energy management optimization problem of PHEB driving safety and fuel economy,and deep fusion of the problem,a nonlinear model predictive control(NMPC)strategy was proposed.That strategy can transform the optimization problem into a series of quadratic programming sub-problems over a finite receding horizon and reduce the complexity in the optimization process.Furthermore,the rolling optimization processes in NMPC strategy using sequential quadratic programming(SQP)algorithm.To ensure the normal application of SQP algorithm,engine brake specific fuel consumption and motor efficiency were pre-fitted.Finally,the effectiveness of NMPC strategy was verified in MATLAB/Simulink environment,and the robustness and real-time performance of the strategy were verified under the hardware-in-loop test scenarios.Secondly,considering the finiteness of the prediction accuracy of the conventional NMPC strategy under actual traffic conditions.adaptive stochastic predictive control(ASMPC)strategy was proposed.The Markov-based stochastic driving modeling was established based on the driving condition of Chongqing 303 bus line.That was highly correlated with the driving condition and making up for the limitations of nonlinear prediction model.In addition,an adaptive weighting factor between driving safety index and fuel economy index was designed,which can be change with traffic information.Finally,the effectiveness of ASMPC strategy was verified in MATLAB/Simulink environment,and the robustness and real-time performance of the strategy were verified under the hardwarein-loop test scenarios.