Predictive Energy Saving Control For Hybrid Electric Vehicle With Consideration Of Road Condition Information

Energy shortages and environmental pollution are becoming more and more serious.Hybrid Electric Vehicles(HEV)have become the main development direction during the transition from traditional vehicles to electric vehicles due to their high efficiency and low emissions.Energy management strategies have a significant impact on the efficiency of HEV vehicles.By using information about driving conditions,road gradients and other operating conditions,the fuel economy of HEVs can be more fully improved.Therefore,this paper designs an energy management strategy for parallel HEV considering road condition information.The main work is as follows:Firstly,by analyzing the structure and working principle of each part of the HEV,a hybrid vehicle model equipped with a CVT was built in AMESim.The model includes the motor and control part,battery,gearbox,clutch,drive shaft,etc.A rule-based controller model was built in MATLAB/Simulink to verify the functionality and rationality of the built vehicle model,laying a foundation for the design of the following energy management strategies.Secondly,a two-layer energy-saving optimization control strategy considering road condition information is designed.The upper layer of this strategy uses the information of the road slope ahead and comprehensively considers the fuel economy and comfort of the vehicle.By optimizing the driving and braking forces of the vehicle,the speed trajectory that minimizes the energy consumption of the entire vehicle is obtained.The lower layer designs an energy management strategy based on driving conditions prediction.First,a series of driving condition data is collected from real vehicles.After processing and analysis,317 sets of characteristic parameters of short operating conditions are extracted,and then the characteristic parameters of short operating conditions are used to Train and verify the BP neural network.Driving conditions are divided into urban driving conditions,suburban driving conditions and high-speed driving conditions.Then,based on model predictive control,an energy management strategy that takes driving condition information into consideration is designed in MATLAB.The weight coefficient of each item in the objective function is determined by the fuzzy controller according to the driving condition of the vehicle and the state information of the vehicle.The torque of the engine and the motor and the gearbox of the gearbox are obtained through the combination of the Pontryagin principle of maximum Principle(PMP)and the dichotomy method.Finally,the hierarchical predictive energy-saving control strategy is verified and analyzed.First,the speed optimization algorithm considering the road slope is verified under 5 different road conditions;Secondly,under the NEDC and WLTC driving cycles,the MPC control strategy without considering the driving condition information and the energy management strategy of the variable weight MPC considering the driving condition information are compared and verified;Then,according to the actual speed curve obtained by the driver,the driving task of each road section and the actual road gradient information are obtained.By using the slope information,the economic speed of each road section is optimized,and at the same time,the driving condition information is used for energy distribution strategy.