Abstract
Distributed-drive electric vehicles (EVs) replace internal combustion engine with multiple motors, and the novel configuration results in new dynamic-related issues. This paper studies the coupling effects between the parameters and responses of dynamic vibration-absorbing structures (DVAS) for EVs driven by in-wheel motors (IWM). Firstly, a DVAS-based quarter suspension model is developed for distributed-drive EVs, from which nine parameters and five responses are selected for the coupling effect analysis. A two-stage global sensitivity analysis is then utilized to investigate the effect of each parameter on the responses. The control of the system is then converted into a multiobjective optimization problem with the defined system parameters being the optimization variables, and three dynamic limitations regarding both motor and suspension subsystems are taken as the constraints. A particle swarm optimization approach is then used to either improve ride comfort or mitigate IWM vibration, and two optimized parameter sets for these two objects are provided at last. Simulation results provide in-depth conclusions for the coupling effects between parameters and responses, as well as a guideline on how to design system parameters for contradictory objectives. It can be concluded that either passenger comfort or motor lifespan can be reduced up to 36% and 15% by properly changing the IWM suspension system parameters.
Highlights
Strict vehicle emission standards have been the main motivation for extensive research on electric vehicles (EVs), which are likely to replace the traditional internal combustion engine (ICE) in the near future [1,2,3,4]
The innovations described in this paper can be summarized as follows: (1) A novel Global SensitivityAnalysis (GSA) algorithm is proposed to investigate the coupling effects between parameters and responses for the dynamic vibration-absorbing structures (DVAS)-in-wheel motors (IWM) system, which can avoid the issues existing in conventional sensitivity analysis methods; (2) particle swarm optimization (PSO) is employed to provide two sets of parameters for improving ride comfort or mitigating motor vibration
For the primary purpose is to investigate the effect of component parameters on an IWM suspension system, all the candidate parameters are listed in the second column of Table 2; the nominal values of these parameters are from the literature [15, 16]
Summary
Strict vehicle emission standards have been the main motivation for extensive research on electric vehicles (EVs), which are likely to replace the traditional internal combustion engine (ICE) in the near future [1,2,3,4]. It shows great potential for active vibration control, and a representative DVAS-based IWM suspension system is shown in Fig. 1 [15, 16] Among these four types, the DVAS-based method is considered as a promising direction, for the reasons that it could achieve 35% improvement of ride comfort and 30% enhancement of road handling in comparison with the traditional ones [15]. The innovations described in this paper can be summarized as follows: (1) A novel GSA algorithm is proposed to investigate the coupling effects between parameters and responses for the DVAS-IWM system, which can avoid the issues existing in conventional sensitivity analysis methods; (2) PSO is employed to provide two sets of parameters for improving ride comfort or mitigating motor vibration.
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