Optimizing suspension parameters for ride comfort in four-wheel hub-motor electric vehicles using multibody dynamics and multi-objective algorithms

The integration of transmission systems in four-wheel hub motor-driven (4WHMD) electric vehicles enhances efficiency and handling but increases unsprung mass, reducing ride comfort compared to centralized motor-driven (CMD) vehicles. This paper presents an optimization framework to address this challenge, advancing mechanism design through multibody dynamics analysis and multi-objective optimization. First, high-fidelity multibody dynamics models for 4WHMD and CMD vehicles are established, capturing nonlinear suspension behavior and wheel-ground interactions to quantify vibrational energy transfer and ride comfort metrics. Second, a comparative study between 4WHMD and CMD vehicles are conducted. Third, a surrogate model is developed using design of experiment (DOE) and gaussian process regression (GPR), enabling rapid evaluation of suspension parameters while reducing computational complexity. Finally, multi-objective simulated annealing algorithm (MOSA), non-dominated sorting genetic algorithm (NSGA-II), and hybrid multi-objective optimization algorithm (HMOA) are applied to optimize riding comfort performance, balancing five indices with a trade-off strategy. The optimized 4WHMD perform better in vertical and roll acceleration than CMD vehicle, but worse in suspension dynamic deflection and wheel dynamic load, with HMOA demonstrating the comprehensive ability of global and local search. This research contributes to the development of high-performance mechanical systems through integrated dynamic modeling and intelligent optimization techniques.