The Control Strategy Optimization Of Parallel Hybrid Electric Vehicle Using Vehicle-to-vehicle(V2V) Communication And Vehicle-to-infrastructure(V2I) Communication

With the issue of environment pollution and energy consumption becoming seriously,Hybrid Electric Vehicles(HEVs),as an efficient solution,has been attracting more attention recently to save the energy and reduce the emissions.Intelligent and connected vehicles technology will be a future trend for the automobile industry.As the technology of internet of vehicles developed rapidly,it provides a better way to optimize the control strategy for HEVs.The performance of control strategy of HEVs highly depends on the driving cycle.To combine the information of internet of vehicles can be helpful for the driving cycle prediction.This is useful for the online decision and optimization of HEVs.This thesis is funded by National high technology research and development“863 program”-development of medium hybrid electric bus.The research objective is a single shaft parallel hybrid electric vehicle,which focus on the optimal control strategy of HEVs using V2 V and V2 I communication from the aspect of theoretical and experiment.Firstly,the HEVs model is established.The structure of a single shaft parallel hybrid electric vehicle is introduced.The forward simulation model is also built,including engine,motor,battery etc,which are implemented based on MATLAB/Simulink.To demonstrate the effectiveness of model,the bench tests are conducted in comparision with the simulation results for key component,providing a basis for the subsequent analysis of control strategy in the following chapter.Secondly,a velocity prediction approach is proposed by exploiting the information of internets of vehicles.In view of the close relationship between control algorithm of HEVs and driving cycle,the traffic model is established for the city condition.The real-time traffic data as well as velocity are obtained by constructing a studied traffic scenario.Combined with the data from Vehicle-to-Vehicle communication(V2V)and Vehicle-to-Infrastructure communication(V2I),the velocity is predicted over different temporal horizons using chaining neural networks(CNN).A simulation of Back-Propagate(BP)neural network is also conducted to predict the velocity.In addition,the relationship between prediction error and prediction horizon is investigated.The sensitivity study of different parameters for CNN is conducted as well.Simulation results of three cases indicates that the performance of CNN is better than BP.Thirdly,the adaptive Equivalent Consumption Minimization Strategy(A-ECMS)is introduced to distribute the power for the HEVs.The equivalent factor(EF)is significant to the optimal performance of ECMS.To this end,the torque split strategy is analyzed for different EF to illustrate the meaning of EF.The principle of Pontryagin's Minimum Principle(PMP)is also given.The adaptation law of EF is derived by illustrating the relationship between co-state of PMP and EF of ECMS.When using traditional adaptation law,the SoC cannot better converge to the reference value because the unsteady phenomenon may happen.In view of this issue,a new adaptation law for the EF applied in ECMS is devised to investigate the effects of future velocity on fuel economy and to impose charge-sustainability.Compared with traditional adaptation law,this new adaptation law considers the impact of predicted velocity on EF.Simulations are conducted in three cases over different prediction horizons to demonstrate that the ECMS with new adaptation law is better than one with traditional adaptation law in terms of fuel economy.In addition better charge-sustainability is achieved as well.Finally,a coordinate torque recovery strategy is presented based on the motor torque compensation.In the torque recovery process,it is hard to recover to the target torque as engine torque response is limited by emissions or control algorithm in Engine Control Unit(ECU),which results in poor vehicle power performance.To solve this problem and taking the different torque characteristics between the engine and the motor into consideration in the dynamic coupling process,the engine torque is dynamically compensated by motor torque to improve the power performance.The torque recovery control strategy is compared to methods,which do not use motor torque compensation.The comparative test results demonstrate that the proposed torque recover control strategy improves the power of the vehicle effectively and ensures the comfort.