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Table of Content
22 February 2022, Volume 5 Issue 1

    Heating Lithium-Ion Batteries at Low Temperatures for Onboard Applications: Recent Progress, Challenges and Prospects

    Cheng Lin, Weifeng Kong, Yu Tian, Wenwei Wang & Mingjie Zhao
    2022, 5(1):  3-17.  doi:10.1007/s42154-021-00166-w
    Abstract ( )   PDF  
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    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.

    Collaborative Control of Novel Uninterrupted Propulsion System for All-Climate Electric Vehicles

    Cheng Lin, Xiao Yu, Mingjie Zhao, Jiang Yi & Ruhui Zhang
    2022, 5(1):  18-28.  doi:10.1007/s42154-021-00170-0
    Abstract ( )   PDF  
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    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.

    Magnetic Coupler Robust Optimization Design for Electric Vehicle Wireless Charger Based on Improved Simulated Annealing Algorithm

    Zhenpo Wang, Lantian Li, Junjun Deng, Baokun Zhang & Shuo Wang
    2022, 5(1):  29-42.  doi:10.1007/s42154-021-00167-9
    Abstract ( )   PDF  
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    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.

    Crowdsourced Road Semantics Mapping Based on Pixel-Wise Confidence Level

    Benny Wijaya, Kun Jiang, Mengmeng Yang, Tuopu Wen, Xuewei Tang & Diange Yang
    2022, 5(1):  43-56.  doi:10.1007/s42154-021-00173-x
    Abstract ( )   PDF  
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    High-definition map has become a vital cornerstone in the navigation of autonomous vehicles in complex traffic scenarios. Thus, the construction of high-definition maps has become crucial. Traditional methods relying on expensive mapping vehicles equipped with high-end sensor equipment are not suitable for mass map construction because of the limitation imposed by its high cost. Hence, this paper proposes a new method to create a high-definition road semantics map using multi-vehicle sensor data. The proposed method implements crowdsourced point-based visual SLAM to align and combine the local maps derived by multiple vehicles. This allows users to modify the extraction process by using a more sophisticated neural network, thus achieving a more accurate detection result when compared with traditional binarization method. The resulting map consists of road marking points suitable for autonomous vehicle navigation and path-planning tasks. Finally, the method is evaluated on the real-world KAIST urban dataset and Shougang dataset to demonstrate the level of detail and accuracy of the proposed map with 0.369 m in mapping errors in ideal condition.

    Dual-pump Control Algorithm of Two-speed Powershift Transmissions in Electric Vehicles

    Yanfang Liu, Lifeng Chen, Tianyuan Cai, Wenbo Sun, Xiangyang Xu & Shuhan Wang
    2022, 5(1):  57-69.  doi:10.1007/s42154-021-00160-2
    Abstract ( )   PDF  
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    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.

    Pyramid Bayesian Method for Model Uncertainty Evaluation of Semantic Segmentation in Autonomous Driving

    Yang Zhao, Wei Tian & Hong Cheng
    2022, 5(1):  70-78.  doi:10.1007/s42154-021-00165-x
    Abstract ( )   PDF  
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    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.

    Performance Limit Evaluation Strategy for Automated Driving Systems

    Feng Gao, Jianwei Mu, Xiangyu Han, Yiheng Yang & Junwu Zhou
    2022, 5(1):  79-90.  doi:10.1007/s42154-021-00168-8
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    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.

    Evaluation of Automatic Lane-Change Model Based on Vehicle Cluster Generalized Dynamic System

    Yangyang Wang, Xiaolang Cao & Xinyuan Ma
    2022, 5(1):  91-104.  doi:10.1007/s42154-021-00171-z
    Abstract ( )   PDF  
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    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.