Regenerative Braking System For EV With Fuzzy Control Based On Particle Swarm Optimization

The number of people owning cars has increased recently,but conventional fuel vehicles continue to account for the majority of car ownership,which causes issues with environmental pollution and the use of oil resources.Pure electric vehicle development has drawn attention due to its simple structure,zero pollution,and potential to partially ease the pressure of the oil resource shortage.However,due to battery capacity restrictions,pure electric vehicles’ range is significantly lower than that of conventional fuel vehicles.As a result,one of the current hot topics in research is how to use regenerative braking technology to improve energy utilization and extend the range of vehicles.In this paper,the regenerative braking control strategy is studied for a pure electric vehicle.And the main work is as follows:Firstly,the parameters for the drive motor,power battery,and transmission ratio of the pure electric vehicle are calculated while taking into account the demands of national standards for vehicle economy and dynamics.The AVL CRUISE software was used to build a pure electric vehicle model and verify the economy and power of the vehicle,providing a vehicle platform for the subsequent joint simulation.Secondly,the basic principle of regenerative braking and the factors that affect regenerative braking energy recovery are explained.Based on ECE regulations,the I curve,and an f-line set with a road surface adhesion coefficient of 0.7,a front and rear axle braking force distribution strategy is created after conducting a force analysis of the vehicle’s braking process.The input variables for the fuzzy controller are the brake pedal stroke,battery SOC,and vehicle speed,while the output variable is the electric mechanism braking force share coefficient k.Thirdly,a hierarchical pure electric vehicle regenerative braking fuzzy control strategy is established using MATLAB/Simulink.The first layer is based on the front and rear axle braking force distribution scheme according to the whole vehicle demand braking force,and a fuzzy controller is used to realize the primary distribution of front axle braking force;the second layer,according to the braking intensity,the braking conditions are divided into light braking,moderate braking and emergency braking,and realize the secondary distribution of braking force to the front axle under the three braking conditions respectively.The regenerative braking fuzzy control strategy is loaded in the vehicle model,and the range contribution is used as evaluation indexes,and NEDC and CLTC-P are used as simulation conditions to verify that the regenerative braking control strategy can achieve energy recovery.Finally,the inflection points and boundary points of the affiliation function of the fuzzy controller and the fuzzy rules are used as the parameters for optimization,and the output k of the fuzzy controller is selected as the evaluation index.The fuzzy controller is optimized by using the particle swarm algorithm,and the optimized fuzzy controller is reloaded into the regenerative braking control strategy and simulated under cyclic and conventional operating conditions.The results show that the optimized regenerative braking fuzzy control strategy can effectively improve the vehicle driving range and ensure braking safety at the same time.