Ride comfort optimization of electric vehicle with a chassis-mounted motor drivetrain and composite mono-leaf spring suspension

Abstract
The impact of increased weight and changes in mass distribution on electric vehicles (EVs) ride comfort, handling dynamics, and overall performance has become a significant concern in the automotive industry. Drivetrain configuration, specifically electric motor location, is a pivotal design variable that influences EV dynamics. Two main configurations exist: in-wheel hub motor (IWM) system, where motors are integrated directly into the wheels, and chassis-mounted motor (CMM) system, where motors are mounted on the chassis. Accordingly, this research investigates a commercial passenger extended-range electric vehicle (ER-EV) featuring a CMM configuration. Its rear suspension includes a transverse composite mono-leaf spring made of glass fiber reinforced polymer (GFRP), designed to function exclusively as a spring element. The spring stiffness and damper damping parameters were evaluated experimentally to be implemented in a two-degree-of-freedom (2DOF) quarter car model to study the ride comfort dynamics. The genetic algorithm (GA) was implemented in MATLAB/Simulink to determine the optimal suspension component parameters. The ride comfort performance metrics, such as ISO-weighted body acceleration (BAC), dynamic tire load (DTL), and suspension working space (SWS), were presented in the frequency domain as power spectral density (PSD) and root mean square (rms) values. Results demonstrated that with a constant rms SWS of 0.02 m and an unsprung-to-sprung mass ratio of 0.1, the optimized suspension reduced BAC and DTL by 12.6% and 13.2%, respectively, during minor random road excitation.