article · Journal of Engineering and Applied Science
Electric vehicles face ride comfort and handling challenges due to increased overall weight and altered mass distribution. This study examines an extended-range electric passenger vehicle featuring a chassis-mounted motor drivetrain and a rear suspension built with a transverse glass fibre reinforced polymer mono-leaf spring. Experimental testing was conducted to determine the stiffness of the composite spring and the damping characteristics of the shock absorber. These measurements were incorporated into a two-degree-of-freedom quarter-car simulation model. A genetic algorithm was then applied in MATLAB and Simulink to optimise the suspension parameters. Under minor random road excitation, the optimised configuration achieved a 12.6 percent reduction in ISO-weighted body acceleration and a 13.2 percent decrease in dynamic tyre load, while maintaining a constant suspension working space root mean square value of 0.02 metres at an unsprung-to-sprung mass ratio of 0.1.
Heavy battery packs and electric drivetrains alter how vehicles respond to road bumps, often compromising passenger comfort and road grip. By combining lightweight composite leaf springs with algorithmic parameter tuning, automotive designers can counter these weight penalties. This approach helps deliver smoother rides, improved tyre contact with the road, and lighter suspension assemblies for modern electric passenger cars.
This work is relevant to automotive original equipment manufacturers and suspension component suppliers developing electric passenger vehicles. The findings demonstrate an applied design and simulation methodology based on experimentally verified components. Because the performance gains were validated using quarter-car computer simulations rather than full-vehicle track testing, the technology is at an applied research stage requiring multi-body dynamic modeling and prototype road testing before commercial deployment.
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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.
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DOI: 10.1186/s44147-026-01176-3
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