Research On Control Strategy Of Regenerative Braking In Parallel Hybrid Electric Vehicle

The development of the electric vehicles is the best option to solve the energycrisis and mitigate environmental pressure. At present in our country, electric vehiclehas not been large-scale production; fuel cell electric vehicles are still in developmentstage, exist the technical bottleneck problems. As a transition vehicle type, hybridelectric vehicles not only improve the traditional vehicles in low fuel economy;achieve energy conservation and emissions reduction, but also overcome thedrawback of the electric vehicles’ short mileage. Regenerative braking is one of theenergy saving and emission reduction technologies of the hybrid electric vehicles.While regenerative braking, regenerative braking force provided by the motorparticipate in the vehicle braking and achieve braking energy recycling. However,regenerative braking force will be restricted by impact factors, such as vehicle speed,motor performance and battery charging current. During a large strength braking, thesimple use of regenerative braking is unable to meet the requirements of thetotal braking. Therefore, hybrid electric vehicles must retain the traditional hydraulicbraking system. How to ensure the braking security and maximize the energyrecovery ability is one of the focuses of the research of regenerative braking system.This paper studies the control strategy of regenerative braking system in order tocoordinate the contradictions between the braking stability and the recovery of theenergy as much as possible. The main research contents are as follows.(1) Designed the front and rear braking force distribution strategy and logicthreshold control strategy of regenerative braking. Ascertained the front and rearbrake force distribution range of the hybrid electric vehicle based on the brakingdynamics of traditional vehicles and the research on ECE regulations, then respectivelydesigned the front and rear braking force distribution strategy and logic thresholdcontrol strategy of regenerative braking on the basis of analyzing the main influencing factors of regenerative braking force distribution aimed at the braking stability andmaximizing the recovery of braking energy. The mathematical models of the proposedstrategies were established in ADVISOR. Simulation experiments on the regenerativebraking performance were carried out. Comparing with the default control strategy ofADVISOR，the proposed strategies veriifed the validity of the model and thesuperiority of the control strategy proposed.(2)Designed a regenerative braking force controller based on fuzzy logic. Thefuzzy controller had three inputs including the vehicle speed, braking strength andbatteries’ state of charge (SOC). The only one output was the ratio k between theregenerative braking force and the current maximum braking force provided by themotor. Model based on fuzzy logic control was established under the Matlab/Simulinkenvironment. Simulation experiments under four different driving cycles were carriedout. Comparing with the default control strategy of ADVISOR and the logic thresholdcontrol strategy, the control effect of the fuzzy logic control strategy was superior tothe other two strategies.(3)Use CS-PSO hybrid algorithm to optimize the membership function of thefuzzy controller. By analyzing the advantages and disadvantages of Particle SwarmOptimization (PSO) algorithm and Cuckoo Search (CS) algorithm, a hybridoptimization algorithm of PSO and CS was proposed. Three benchmark functionswere selected to demonstrate the effectiveness of CS-PSO algorithm. Then，themembership function of the fuzzy controller of regenerative braking was optimizedrespectively by using PSO algorithm and CS-PSO algorithm. The fuzzy logic controlstrategy and the optimized fuzzy logic control strategies were simulated under UDDSdriving cycle in ADVISOR，taking braking energy recovery rate as the evaluationindex. The results showed that the optimized control strategy using optimizationalgorithm improved the braking energy recovery rate signiifcantly. Comparing withthe control strategy using PSO algorithm, the control strategy using CS-PSOalgorithm also improved the braking energy recovery rate slightly, veriifed that CS-PSO algorithm can make up for the shortcoming of PSO algorithm which couldbe easily fall into local optimum.