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A Novel Three Steps Composited Parameter Matching Method of an Electromagnetic Regenerative Suspension System
Technical Paper
2019-01-0173
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
The electromagnetic regenerative suspension has attracted much attention recently due to its potential to improve ride comfort and handling stability, at the same time recover kinetic energy which is typically dissipated in traditional shock absorbers. The key components of a ball-screw regenerative suspension system are a motor, a ball screw and a nut. For this kind of regenerative suspension, its damping character is determined by the motor's torque-speed capacity, which is different from the damping character of the traditional shock absorber. Therefore, it is necessary to establish a systematic approach for the parameter matching of ball-screw regenerative suspension, so that the damping character provided by it can ensure ride comfort and handling stability. In this paper, a 2-DOF quarter vehicle simulation model with regenerative suspension is constructed. The effects of the inertia force on ride comfort and handling stability are analyzed. A novel three steps composited matching method is proposed to determine the non-linear damping character of the ball-screw electromagnetic regenerative suspension. In this composited method, a genetic algorithm is adopted to calculate the optimal damping coefficient within its linear range, probability statistics is applied to determine the constant damping force provided by the motor over constant damping range, and the decreasing damping force range is determined by the motor speed ratio. Through the above three steps, system parameters including the motor rated power and the lead of ball screw are determined. The effectiveness of the systematic parameter selection approach is validated through simulation.
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Cui, D. and Yongchang, D., "A Novel Three Steps Composited Parameter Matching Method of an Electromagnetic Regenerative Suspension System," SAE Technical Paper 2019-01-0173, 2019, https://doi.org/10.4271/2019-01-0173.Data Sets - Support Documents
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