Aiming at the shortcomings of only optimizing the gear ratios of two-speed transmission in the optimization process of two-speed powertrain parameters of electric vehicles, the optimization of two-speed powertrain parameters of electric vehicles based on genetic algorithm is proposed. The optimization process is to optimize the main performance parameters of the drive motor and the gear ratios of two-speed transmission. That is, taking the economy and dynamic of the electric vehicle as the fitness function, the gear ratios of two-speed transmission is optimized under the main performance parameters of different drive motors, so as to find the powertrain parameter with the best fitness function value. Among them, the AMESim software is used to build the vehicle optimization model, the genetic algorithm is improved by MATLAB, and the improved genetic algorithm is used to optimize the vehicle optimization model. The results show that the optimization of the vehicle’s economic and dynamic performance has been improved, indicating that this optimization method is effective.

An in-wheel motor, as a key part of an in-wheel driving system, needs to satisfy strict restrictions on thermal balance for increasingly high-power density in limited space and weight. Therefore, a new in-wheel motor with an innovative water-cooling system for one newly developed electric vehicle was developed. Based on the mechanical structure of the motor, all potential water-cooling layouts were first designed with consideration of mechanical strength and manufacturability. A thermal conjugate simulation model of the developed in-wheel motor was then built, and its thermally fluid–solid interactions were investigated in this study. All potential water-path layouts of the motor were compared regarding the cooling effect and fluid resistance, which impact the performance of the motor. Fluid flow velocity and fluid state, determined by the water-path layout, significantly impact the cooling effect of the motor. The well-designed water-cooling system significantly reduces the motor’s temperature at a low cost on required coolant-driven pressure which benefits the efficiency of the developed motor. A prototype of the developed motor with the optimal water-path layout was built and tested on the test rig. The developed motor provides outstanding thermal performance.

In order to improve the temperature maintenance capacity for the battery of the extended-range electric vehicle (EREV) in a low-temperature environment, a microencapsulated phase-change material suspension (MPCMS)-based integrated thermal management system (ITMS) is proposed. The working modes of the proposed ITMS are divided based on series-parallel connections of the battery thermal management system (BTMS), motor thermal management system, motor thermal management system, and auxiliary power unit (APU) thermal management system; the structural parameters of the proposed ITMS are determined by robust design, and the system performance difference between the proposed ITMS and the traditional BTMS is verified through the comparative simulation in −20 °C environment. The results show that the proposed ITMS can significantly delay the decline of battery temperature in the charge-depleting (CD) stage and can reduce the time of the positive temperature coefficient (PTC) heater being on by 27.26%, and the total time being on by 54.82%. During the charge-sustaining (CS) stage, when the PTC heater is off, the average battery temperature will increase by 15.33 °C compared with the traditional BTMS. Based on the proposed ITMS, the temperature maintenance capability for the battery can be significantly improved, and the energy consumption of the PTC heater and vehicle can be reduced by 48.12–100% and 13.44–33.58%, respectively.

A test scenario generation method based on combinatorial testing (CT) and Bayesian Network for autonomous vehicles is proposed in this paper. Firstly, some parameters are selected to describe the test scenarios which are classified according to road types and driving tasks. Then, the constraint sets for the scenarios with forbidden tuples are established to avoid the generated cases do not conform to the reality, in which the construct constraint set (CCS) algorithm is utilized to compute implied constraints. Furthermore, the Bayesian networks is used as the probabilistic models of the scenarios, where the traffic participants are represented as object nodes and the relative relationships between the participants are converted into the network structures. Finally, an improved automatic efficient test case generator (AETG) is developed to generate test cases. By considering both probability and frequency, the select function is designed for determining the values of scenario parameters. And the generation mode can be changed by modifying the weight and target parameters. The effectiveness of the proposed method is evaluated by generating six typical test scenarios. Compared with other algorithms, the numbers of test cases in the sets generated by this method are less and the probability deviations are smaller.

The amount of lubricating oil in a gear reducer greatly influences components’ temperature and efficiency of the reducer. A thermal network simulation model of a gear reducer for electric vehicles based on amesim software was built for thermal and efficiency analysis of the reducer, where heat production and transfer processes of each component in the reducer are considered. Moreover, secondary development submodels were developed to calculate the convection coefficients in real time based on different lubricating oil temperatures and working conditions. Next, the experiments were conducted to verify the developed model regarding transmission efficiency and lubricating oil equilibrium temperature in the reducer. The deviation between the numerical and experimental results is less than 5%, which indicates high accuracy of the developed thermal network model. Thereafter, efficiency and heat balance simulation tests under different working conditions were performed to determine an optimal lubricating oil supply, which is 14 mm gear-oil-immersion oil supply in this study. Finally, a comparison between the reducer with the optimal oil supply and that with normally used 22 mm gear-oil-immersion supply in the NEDC cycles in low temperature was done. The reducer with the optimal oil supply shows 1.032% energy saving. The method presented in this article provides a good guide to design and optimization of the gear reducer for electrical vehicles.