The camber and cornering properties of the tire directly affect the handling stability of vehicles, especially in emergencies such as high-speed cornering and obstacle avoidance. The structural and load-bearing mode of non-pneumatic mechanical elastic (ME) wheel determine that the mechanical properties of ME wheel will change when different combinations of hinge length and distribution number are adopted. The camber and cornering properties of ME wheel with different hinge lengths and distributions were studied by combining finite element method (FEM) with neural network theory. A ME wheel back propagation (BP) neural network model was established, and the additional momentum method and adaptive learning rate method were utilized to improve BP algorithm. The learning ability and generalization ability of the network model were verified by comparing the output values with the actual input values. The camber and cornering properties of ME wheel were analyzed when the hinge length and distribution changed. The results showed the variation of lateral force and aligning torque of different wheel structures under the combined conditions, and also provided guidance for the matching of wheel and vehicle performance.

轮胎的外倾角和转弯性能直接影响车辆的操纵稳定性，尤其是在紧急情况下，例如高速转弯和避障。非气动机械弹性（ME）轮的结构和承载方式决定了当采用不同的铰链长度和分配数组合时，ME轮的机械性能会发生变化。通过将有限元方法（FEM）与神经网络理论相结合，研究了不同铰链长度和分布的ME轮的弯度和转弯性能。建立了ME轮后向传播（BP）神经网络模型，并利用附加动量法和自适应学习率法对BP算法进行了改进。通过将输出值与实际输入值进行比较，验证了网络模型的学习能力和泛化能力。当铰链长度和分布改变时，分析了ME轮的外倾角和转弯性能。结果表明，在组合条件下，不同车轮结构的侧向力和对准扭矩的变化，为车轮和车辆性能的匹配提供了指导。