The rapid evolution of electric vehicles (EVs) has led to the development of
innovative approaches to optimize ride comfort, handling, and the overall
suspension performance. EVs introduce unique challenges due to their distinct
weight distribution, powertrain dynamics, and noise characteristics, unlike
their conventional internal combustion engine (ICE) counterparts. This paper
outlines an advanced damping force modeling methodology using machine learning
(ML) techniques to enhance the suspension design process for next-generation
EVs. The analysis is based on data-driven ML algorithms, i.e., Gradient
Boosting, Random Forest, and Neural Networks, to simulate the nonlinear and
frequency-dependent phenomenon of dampers in different operating conditions. A
comprehensive dataset, generated through simulation and experimental testing,
captures the effects of road profiles, vehicle dynamics, and damping settings.
Additionally, this research evaluates the impact of machine-learned damping
force predictions on critical ride and handling metrics, including ride comfort,
road-holding ability, and energy efficiency. The results demonstrate that the ML
models can enhance the iterative design process considerably and help to create
the adaptive suspension systems that will address the particular requirements of
EVs. This paper contributes to advancing the state-of-the-art of the suspension
modeling, incorporating the ML-based insights in the development cycle. It
highlights the possibility of artificial intelligence to transform suspension
design, paving the way for superior ride quality and vehicle performance in
electric mobility.