Lithium-ion batteries (LIBs) are commonly used in electric vehicles (EVs) due to their good performance, long lifecycle,
and environmentally friendly merits. Heating LIBs at low temperatures before operation is vitally important to protect the
battery from serious capacity degradation and safety hazards. This paper reviews recent progress on heating methods that
can be used onboard. The existing methods are divided into two categories, namely external heating methods and internal
heating methods, mechanisms, advantages and limitations of each method are systematically reviewed. Then, the rates of
temperature rise, energy consumptions, and maximum temperature gradient of diferent methods are quantitatively summarized to compare the heating performances of each method. In addition, features related to the onboard application of eachmethod are qualitatively compared, which is essential for the rapid cold start of EVs in frigid weather. Finally, prospects of external and internal heating methods are given. This paper aims to provide researchers and engineers with guidelines about how to select a method based on their requirements and application environments.

Over the past decade, the electric vehicle industry of China has developed rapidly, reaching one of the highest technological levels in the world. Nevertheless, most electric buses currently serve urban areas, being unsuitable for all-climate operations. In response to the objective of massively adopting electric vehicles for transportation during all the events of the 2022 Beijing Winter Olympics, a dual-motor coaxial propulsion system for all-climate electric vehicles is proposed. The system aims to meet operating requirements such as high speed and adaptability to mountainous roads under severely cold environments. The system provides three operating modes, whose characteristics are analyzed under different conditions. In addition, dual-motor collaborative control strategy with collaborative gearshift and collaborative power distribution is proposed to eliminate power interruption during gearshift process and achieve intelligent power distribution, thus improving the gearshift quality and reducing energy consumption. Finally, gear position calibration for all-climate operation and proper gearshift is introduced. Experimental results demonstrate the advantages of the proposed dual-motor coaxial propulsion system regarding gearshift compared with the conventional single-motor automatic transmission.

Fleets of autonomous vehicles including shuttle buses, freight trucks, and road sweepers will be deployed in the Olympic Village during Beijing 2022 Winter Olympics. This requires intelligent charging infrastructure based on wireless power transfer technology to be equipped. To increase the misalignment tolerance of a high-power wireless charger, the robustness of the magnetic coupler should be optimized. This paper presents a new type of unipolar coupler, which is composed of three connected coils in series. The dimensional configuration of the coils is analyzed by the finite element method. The characteristic parameters of the coil are identified with their influence on the self-inductance and coupling coefficient. An expert model is built, whose feasibility can be verified in the aimed design domain. Combined with the expert model, an improved simulated annealing algorithm with a backtracking mechanism is proposed. The primary coil can reach the expected characteristics from any starting parameter combination through the proposed optimization algorithm. Under the same conditions in terms of external circuit parameters, ferrite usage, and aluminum shielding, the offset sensitivity of the magnetic coupler can be reduced from 58.79% to 18.89%. A prototype is established, validating the feasibility of the proposed coil structure with the optimized parameter algorithm.

A high-speed motor in a drive system causes several challenges to the reliability of the mechanical parts of electric vehicles and leads to issues with noise, vibration and harshness (NVH). Thus, a two-speed powershift transmission is considered an effective way to improve the dynamic, economic and comfort performance of electric vehicles. A newly designed dual-pump hydraulic control system for a two-speed powershift transmission with two wet clutches is presented, in which the mechanical oil pump is linearly affected by the vehicle speed and the electric oil pump is controllable. By integrating the dynamic model of the hydraulic system into one of the powertrains with a two-speed transmission, a co-simulation dynamic model is proposed. To satisfy the flow and pressure demand of the hydraulic system, a dual-pump control strategy is presented, in which the electric oil pump is controlled by the mechanical oil pump following the minimum energy consumption principle. The World Light Vehicle Test Procedure (WLTP) cycle simulation results show that the energy consumption of the proposed hydraulic system can be reduced by 58.2% compared to the previous single-pump system developed by the authors with a constant main-line pressure control strategy. On the basis, the best configuration of the two pumps can further reduce the energy consumption of the hydraulic system by 23.2% compared to that of two-oil pumps with preset displacement.

With the fast-developing deep learning models in the field of autonomous driving, the research on the uncertainty estimation of deep learning models has also prevailed. Herein, a pyramid Bayesian deep learning method is proposed for the model uncertainty evaluation of semantic segmentation. Semantic segmentation is one of the most important perception problems in understanding visual scene, which is critical for autonomous driving. This study to optimize Bayesian SegNet for uncertainty evaluation. This paper first simplifies the network structure of Bayesian SegNet by reducing the number of MC-Dropout layer and then introduces the pyramid pooling module to improve the performance of Bayesian SegNet. mIoU and mPAvPU are used as evaluation matrics to test the proposed method on the public Cityscapes dataset. The experimental results show that the proposed method improves the sampling effect of the Bayesian SegNet, shortens the sampling time, and improves the network performance.

Efficient detection of performance limits is critical to autonomous driving. As autonomous driving is difficult to be realized under complicated scenarios, an improved genetic algorithm-based evolution test is proposed to accelerate the evaluation of performance limits. It conducts crossover operation at all positions and mutation several times to make the high-quality chromosome exist in candidate offspring easily. Then the normal offspring is selected statistically based on the scenario complexity, which is designed to measure the difficulty of realizing autonomous driving through the Analytic Hierarchy Process. The benefits of modified cross/mutation operators on the improvement of scenario complexity are analyzed theoretically. Finally, the effectiveness of improved genetic algorithm-based evolution test is validated after being applied to evaluate the collision avoidance performance of an automatic parallel parking system.

The lane-change transportation research usually focuses on the efficiency and stability of the macro traffic flow while ignoring the driving comfort of individual vehicles. And many studies of lane-change models are often limited to the performance of a single vehicle, which leads to a lack of macroscopic evaluation. To solve the above limitations, an automatic lane-change generalized dynamic model is adopted. In this model, the lane-change behavior of an individual vehicle is considered as the generalized excitation and the restraining force between vehicles is described with the car-following model. Macro and micro evaluation indexes are also adopted to evaluate the automatic lane-change behavior in traffic flow. Furthermore, this paper proposes a modified intelligent driver model (IDM) to describe the state change process during lane change. The hyperbolic tangent transition function is used to eliminate the vehicle state mutation. The simulation results show that the proposed automatic lane-change generalized dynamic model can reflect the macro and micro parameters of the traffic flow. And compared with the traditional IDM model, the proposed HC-IDM model achieves higher comfort performance and lower fluctuation of traffic flow.