Research on the durability test method of electric driving systems based on fuzzy clustering and particle swarm algorithm

Compared to traditional fuel vehicles, the structure of pure electric vehicles (BEVs) and the actual driving behaviour of users have changed. Therefore, the original durability evaluation conditions of traditional fuel vehicles cannot fully cover the use of new energy vehicles. In the past, the determination of durability targets was mainly based on user data collection, but this work required a lot of manpower and material resources to meet the engineering requirements. In this paper, the fuzzy clustering method is used to mine the user trajectory to obtain the user-based endurance target of pure electric vehicle, and then according to the durability target, the particle swarm method is used to correlate the user behaviour and the proving ground, and the proving ground test method of electric drive system is developed. Studies have shown that user data mining methods can obtain more user information, so as to better formulate durable target close to users. The particle swarm algorithm can improve the simulation correlation accuracy and reduce the iteration time, which shortens the simulation iteration time by more than 80% compared with polynomials. The test acceleration ratio of 7:1 in relation to user behaviour with the proving ground. During the durability test of the electric drive system of pure electric vehicles, it is found that the test specification can well reflect the user's motor operation during actual driving.