Based on the statics and quasi-statics analysis methods, the thermal deformation calculation model of a deep-groove ball bearing was constructed for the helical gear transmission system of a high speed electric drive, and the radial and axial bearing stiffness values of the bearing were calculated under the thermal deformation in this study. The obtained radial and axial stiffness values were introduced into the established dynamics model of helical gear system, and the influence of changed bearing stiffness, resulting from the thermal deformation, on the nonlinear dynamic characteristics of gear pair was analyzed using the Runge–Kutta method. The results show that the axial and radial deformations of bearing occur due to the increase of working speed and temperature, in which the axial stiffness of bearing is improved but the radial stiffness is reduced. The decreasing degree of axial stiffness and the increasing degree of radial stiffness decrease with the gradually increasing working rotational speed. When considering the influence of thermal deformation on the bearing stiffness, the helical gear system will have nonlinear behaviors, such as single periodic, double periodic, and chaotic motion with the change of working speed. Therefore, in order to improve the nonlinear dynamic characteristics of high speed electric drive gear systems, the influence of bearing stiffness change on the dynamic performance of a gear system should be considered in the industrial applications.

The low mechanical efficiency of metal belt’s continuously variable transmission (CVT) limits its application in new energy vehicles. To further improve CVT efficiency and reduce the energy consumption of electric vehicles (EVs) with CVT, this paper proposes a pure electric CVT configuration and a clamping force control strategy. The slip characteristics of CVT are obtained through a bench test, the dynamic model of CVT slip is established, and a clamping force fuzzy control strategy is designed. The strategy is studied by simulation under extreme conditions and standard driving cycles. The simulation results show that the proposed clamping force control strategy has good adaptability. Under extreme conditions, this strategy can ensure that CVT does not undergo macro slip, while reducing the clamping force by 12.86–21.65%. Energy consumption per 100 km is 14.90 kWh in NEDC, which is 6.67% lower compared with the traditional strategy. CVT average efficiency and average transmission efficiency increased by 3.71% and 6.40%. The research results demonstrate that adjusting the CVT clamping force through fuzzy control based on the slip rate can improve the CVT efficiency and energy economy of EVs, which provides a certain reference for CVT clamping force control strategy development and the application of CVT on EVs.

Hybrid electric vehicles equipped with continuously variable transmission show dramatic improvements in fuel economy and driving performance because they can continuously adjust the operating points of the power source. This article proposes an optimal control strategy for continuously variable transmission–based hybrid electric vehicles with a pre-transmission parallel configuration. To explore the fuel-saving potential of the given configuration, a ‘control-oriented’ quasi-static vehicle model is built, and dynamic programming is adopted to determine the optimal torque split factor and continuously variable transmission speed ratio. However, a single-criterion cost function will lead to undesirable drivability problems. To tackle this problem, the main factors affecting the driving performance of a continuously variable transmission–based hybrid electric vehicle are studied. On that basis, a multicriterion cost function is proposed by introducing drivability constraints. By varying the weighting factors, the trade-off between fuel economy and drivability can be evaluated under a predetermined driving cycle. To validate the effectiveness of the proposed method, simulation experiments are performed under four different driving cycles, and the results indicate that the proposed method greatly enhanced the drivability without significantly increasing fuel consumption. Compared to a single-criterion cost function, the use of multiple criteria is more representative of real-world driving behaviour and thus provides better reference solutions to evaluate suboptimal online controllers.