Aiming at the problem that the kinematics feature dataset of different traffic flow, average speed, acceleration, and traffic time are affected by high-dimensional, irrelevant, and redundant factors, and the kinematics feature dataset is a multiobjective, multi-constrained and complex nonlinear optimization system, the improved multi-objective evolutionary soft subspace clustering algorithm (iMOSSC) is proposed to mine the micro-stroke segments with different kinematic characteristics and realize data classification. The algorithm uses iNSGA-II as the base algorithm and performs local search operator and repair operator operation in the feature space to accelerate convergence and improve the accuracy of the solution. The feasibility and effectiveness of the algorithm are verified by 12 sets of UCI standard dataset. The classified kinematics characteristic data is used to construct the Xi'an urban road trajectory database. Compared with the iMWK-HD algorithm in the collected kinematics feature data of circulation condition, the feature importance degree of the iMOSSC algorithm is more reasonable, the stability is better, the accuracy is higher, and the classification effectiveness is more obvious than the iMWK-HD algorithm. The excavated kinematics data is imported into the Optimumlap simulation software to construct the actual road circulation condition trajectory database. Based on the ADVISOR commercial software platform for the simulation module. The simulation results show that the fuel economy, acceleration time, and gradeability of the novel dual-mode&dual-motor hybrid drive system are better than those of gasoline vehicles and Prius vehicles when operating under actual road conditions.
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