Multidisciplinary Optimization Design of Electric Vehicle X-by-Wire Chassis Integrating Parameter Coupling and Uncertainties

X-by-wire chassis plays an important role in intelligent vehicle motion control. However, the traditional deterministic optimization method is difficult to achieve the global optimal and robust comprehensive performance of the chassis due to the multidisciplinary coupling relationship and uncertainties among steering, suspension, and drive subsystems. This article proposed a multidisciplinary optimization (MDO) design framework incorporating the coupling relationship and uncertainties of structural parameters into the X-by-wire chassis. First, the dynamics models of hub motor driving, differential steering, and semiactive suspension were established. Based on energy, safety, and ergonomic, the performance indicators of the X-by-wire chassis were deduced and quantified. Second, the coupling relationship and the influence of uncertain factors on the reliability of constraints were analyzed by Monte Carlo and 6- $\sigma $ methods. The uncertain bi-level collaborative optimization (UBLCO) was proposed by integrating uncertainty analysis and bi-level integrated system collaborative optimization (BLISCO). The proposed framework can ensure constraint reliability at the system level, allocate design variables to subsystems based on parameter coupling and uncertainty analysis, and achieve discipline decoupling through parallel optimization. The optimization results showed that UBLCO could effectively improve the reliability and robustness of the chassis performance under uncertainty. Finally, both the effectiveness and feasibility of the uncertain MDO in X-by-wire chassis were further verified through the road tests, which are expected to provide a reference for the comprehensive design of the X-by-wire chassis of electric vehicles.