Integrated Vibration Control of In‐Wheel Motor‐Suspensions Coupling System via Dynamics Parameter Optimization

Abstract

In order to integrally control the vibration of in-wheel motor- (IWM-) suspensions coupling system of an electric vehicle, a novel nonlinear dynamics model of the coupling system, which consists of the motor magnetic gap (MMG), is established. Synthesizing subtargets of the vertical vibration acceleration of bodywork, the vertical deformation of tire, the suspension travel, and the vertical fluctuation of MMG, a composite optimization mathematical model is set up. Based on artificial fish swarm algorithm (AFSA), a novel dynamics parameter optimization method is proposed to search the optimal parameter combination existing in the nonlinear dynamics model. Simulation analyses demonstrate that the proposed optimization method is superior to genetic algorithm (GA) under the same optimization conditions, and it can significantly decrease the fluctuation of MMG and improve ride comfort.

Development of Robust Guaranteed Cost Mixed Control System for Active Suspension of In-Wheel-Drive Electric Vehicles

“…Most of the above studies are focused on the lateral and longitudinal VDC systems, while optimization, design, and application of topology are still open and immaturate, and few research on the vertical vibration control of the IWMD-EV active suspension system is carried out [10][11][12][13].In practice, the suspension system of IWMD-EV mounts and integrates the motor, wheel hub, and speed reducer together, which causes the increase of unsprung mass of IWMD-EV. It will lead to the deterioration of the ride comfort of the vehicle and even affect the active safety.…”

Research on adaptive speed control method of an autonomous vehicle passing a speed bump on the highway based on a genetic algorithm

“…GA has been widely applied in engineering for optimization solution scenarios; for example, Sun et al ( 2017) optimized multiple indicators of vehicle active suspension characterization performance and achieved a better balance between ride comfort and stability through GA. Gao and Qi (2021) enhanced the vehicle ride comfort after optimization and matching of the suspension parameters by GA. However, current research on vehicle safety and comfort focuses on using GA or other intelligent algorithms to solve optimization problems considering a suspension's design parameters as variables to obtain the best vehicle suspension design parameters (Tey et al, 2016;Wang et al, 2019;Rodrigues et al, 2021) or the optimal parameters of the SCHs (Gheibollahi and Masih-Tehrani, 2021), and vehicle speed often exists as a hypothetical condition. This paper uses GA with an elitist strategy to obtain the optimal speed of an autonomous vehicle passing highway SCH to achieve the requirement of adaptive speed control in a real-world scenario.…”

Design of Constrained Robust Controller for Active Suspension of In-Wheel-Drive Electric Vehicles

“…Although the aforementioned research achievements were successful, most research papers are concerned with lateral and longitudinal VDC systems, and vertical vibration control of active suspension system of IWMD-EV is seldom tackled [13][14][15][16][17]. In practice, when the new vehicle topology structure for IWMD-EV is brought forward, the active suspension system with in-wheel-motor also changes particularly, in which motor, the wheel hub, and speed reducer are mounted and integrated; it causes the increased unsprung mass of IWMD-EV so that vehicle ride comfort will deteriorate and even road holding ability and safety will be probably reduced.…”