Research On Driving Control Strategy For Extended Range Electric Bus Based On Particle Swarm Optimization Algorithm

With the increasing number of traditional car ownership,the energy crisis and environmental pollution problems are becoming more and more serious.Therefore,many countries are embarking on the development of new energy vehicles.Pure electric vehicles have the advantages of high efficiency,cleanliness and zero emissions,but they also have shortcomings such as short cruising range,long charging time and imperfect supporting facilities.The extended-range electric vehicle not only has the advantages of pure electric vehicles,but also compensates for the shortcomings of short driving range.It can be used as an ideal model for the transition from traditional fuel vehicles to pure electric vehicles.Therefore,it is of great significance to study the driving control strategy of the extended-range electric vehicle to promote the application of new energy vehicles.This article relies on the enterprise cooperation project "The System Development of Extended-range Electric Bus",with the extended-range electric bus as the research object,according to the design indicators,completed the parameter matching of powertrain.Established the driving control strategy based on the ideal reference SOC curve.Built the model of the vehicle and the driving control stratagy.Optimized the parameters by particle swarm optimization algorithm based on simulated annealing.The main research contents are as follow:Firstly,determining the vehicle control system architecture and performance indicators based on the market demand.According to these,matching the characteristic parameters of the drive motor,power battery and range extender.Check the vehicle's power and pure electric driving range.Secondly,formulating the vehicle driving control strategy based on the vehicle performance index and power system parameter matching results.Selecting four cycle conditions which have the same characteristics and different distances,then using the dynamic programming algorithm with the target of the least driving cost to obtain the optimal trajectory of the reference SOC.Thirdly,based on the information of condition and rated discharge power of power battery,determining the optimal working range of the range extender,and selecting the difference between the actual SOC and the reference SOC and the power demand of the vehicle as the judgment amount.Formulating the two points plus power-following driving control strategy based on the ideal reference SOC curve.Fourthly,building the vehicle forward simulation model on the Cruise simulation platform,and building the driving control strategy in MATLAB/Simulink software.Integrate control strategy model and vehicle model by compiling DLL file.Setting and simulating some calculation tasks in the Cruise.The results show that the matching calculation of the power system is reasonable,and the designed drive control strategy is also effective and feasible.Fifthly,in order to achieve a better fuel economy under the condition of satisfying the dynamic conditions,thus using the particle swarm optimization algorithm based on simulated annealing to optimize the judgment threshold of the control strategy with the total driving cost as the optimization target.The result shows that the vehicle fuel economy is improved under the premise of ensuring the dynamic conditions and drving range,and the rational use of energy is achieved.Lastly,building the hardware-in-the-loop test platform which is based on the the xPC-Target.Simulating and checking the operate environment,detecting the real-time input and output performanc,and verifying the control response of the vehicle controller.The results show that the control program can realize the corresponding control function in the controller,and the vehicle can follow the target speed well.The test system meet the real-time performance of the vehicle control system and the validity of the control logic.