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Firstly, the parameters of hyperbolic tangent model with magneto-rheological (MR) damper are identified via particle swarm optimization algorithm based on the mechanical properties test. Then, the functional relationship between the identified parameters and input current is fitted by curve fitting toolbox. Ultimately, Simulink toolbox is utilized to design and establish the hyperbolic tangent simulation model with MR damper, meanwhile, the different input currents and sinusoidal signals with other amplitudes and frequencies are selected for simulation and comparative analysis. The results show that parameter identification accuracy of particle swarm optimization algorithm is high. The simulation and experiment data under combination conditions with different input currents and other sinusoidal signals are in good agreement, which verifies the generality and accuracy of the parameter identification results.

In order to enhance the recovery rate of regenerative braking energy on the premise of ensuring braking safety and stability, an improved braking force distribution scheme for front and rear wheels is proposed in combination with ECE regulations, ideal braking force distribution curve (I) and f lines. Meanwhile, a kind of fuzzy controller is designed, which takes the braking strength, vehicle speed and super-capacitor SOC as input and the ratio of regenerative braking force to total braking force of front wheels as output. The forward and backward simulation module is established in MATLAB and embedded into ADVISOR. In addition, combined with the secondary development of the top layer model of the compound power supply for pure electric vehicle to complete simulation and analysis under the CYC_UDDS driving cycle condition. The results show that the regenerative braking control strategy can improve the recovery rate of braking energy and increase the driving mileage of pure electric vehicle.