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  • 2025, Vol. 16 No. 5 Published on:31 October 2025 Previous issue    Next issue
    Review, Progress and Prospects
    Research progress and prospect on safety of all-solid-state batteries
    GUO Chunli, TANG Shengkai, CUI Yu, MAO Yuqiong
    2025, 16(5):  657-678.  doi:10.3969/j.issn.1674-8484.2025.05.001
    Abstract ( 575 )   HTML ( 377)   PDF (3718KB) ( 3079 )  

    All-solid-state batteries (ASSBs) possess potential performance advantages, such as high safety and high energy density, making them a strategic frontier in global power battery technology competition, which has been incorporated into the development strategies of major countries including China, the United States, Japan, South Korea, etc. Currently, the research & development of ASSBs has entered a critical breakthrough phase, with the leading enterprises such as Toyota, BYD, and CATL expecting to initiate the applications of ASSBs in electric vehicles around 2027. However, before large-scale application, comprehensive performance evaluation and failure analysis of ASSBs are still required to ensure their safe and reliable operation under complex working conditions in electric vehicles. Notably, existing research indicates that ASSBs still suffer from risks of thermal runaway and are not absolute safe, as their failure mechanisms under complex operating conditions remain inadequately understood. In light of this, this paper systematically reviews the potential safety issues of ASSBs from the perspectives of materials, interfaces, and cell design, including the intrinsic thermal stability of key materials such as cathodes, anodes, and solid state electrolytes; high-temperature thermochemical reactions at the cathode/anode-electrolyte interfaces; lithium dendrite growth and the resulting internal short circuits; and toxic gas production and environmental hazards during battery failure. Building on this analysis, the paper further outlines future research strategies for the safety of ASSBs from the perspectives of in-depth failure-mechanism analysis, optimization of key materials and interfacial stability, and system-level gas management and thermal protection, thereby offering systematic theoretical support and practical guidance for their safety assessment and engineering deployment.

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    Automotive Safety
    Effective area estimation method based on performance degradation mechanism of rolling-lobe air springs
    WU Mingyu, WANG Yafei, CHEN Junjie, ZHONG Hong, LI Yaochao, WEI Yintao, LIU Xiang, ZHANG Yifei
    2025, 16(5):  679-687.  doi:10.3969/j.issn.1674-8484.2025.05.002
    Abstract ( 210 )   HTML ( 151)   PDF (1860KB) ( 118 )  

    An effective area prediction model was built based on composite material theory and fatigue degradation mechanisms to predict the dynamic response behaviors of rolling-lobe air springs over their full lifecycle. The evolving fatigue characteristics of cords and rubber materials were introduced to establish a multi-physical coupling relationship, in which the effective area was modeled as a function of the fatigue cycles and the deformation excitation amplitude under force. Dynamic validation tests were carried out under different fatigue cycles and deformation excitation amplitudes. The results show that the model prediction error is within 1% at different degradation stages. The effective-area increases with both the fatigue cycles and the deformation excitation amplitude; but decreases with the elastic modulus of the cords and the rubber materials. The effective-area growth trend at 50 °C accelerates and exhibits nonlinear characteristics.

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    Recognition of the dangerous driving behaviors and the driving styles in weaving areas based on a hybrid neural network
    CHENG Zeyang, DUAN Yiyang, YANG Mengmeng, FENG Zhongxiang, WANG He, ZHU Xiaojun, BAO Lixia
    2025, 16(5):  688-697.  doi:10.3969/j.issn.1674-8484.2025.05.003
    Abstract ( 153 )   HTML ( 98)   PDF (4543KB) ( 76 )  

    A hybrid neural network analysis method was proposed based on historical trajectory data to identify and predict dangerous driving behaviors and driving styles in weaving areas. The different dangerous driving behaviors were clustered and analyzed by using the K-means++ algorithm with the key features of the longitudinal velocity, the lateral acceleration, and the longitudinal acceleration being extracted to characterize different driving styles. A driving style prediction model was constructed based on a Long Short-Term Memory (LSTM) network, a Convolutional Neural Network (CNN), and a Knowledge-Attention Network (KAN), with conducting digital simulations and ablation comparison experiments. The results show that the model has the Area Under Curve (AUC) of the Receiver Operating Characteristic (ROC), a dimension-one quantity, of 0.846. the model's classification and prediction accuracy of dangerous driving behaviors and driving styles increased by 6.73%, 3.12%, and 4.72%, while increasing the generalization verification accuracy by 6.3%, 2.5%, and 3.9%, compared with models using LSTM, CNN-LSTM, and LSTM-KAN.

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    Effect of vehicle crash severity and advanced restraint system parameters on occupant injuries
    DENG Gongxun, CAI Yani, LEI Feibing, LIU Hengjin, QI Lulin, FAN Yubo
    2025, 16(5):  698-706.  doi:10.3969/j.issn.1674-8484.2025.05.004
    Abstract ( 203 )   HTML ( 95)   PDF (2668KB) ( 282 )  

    Aiming at the matching problem of advanced restraint system in vehicle crashes, the distribution frequencies of vehicle Occupant Load Criterion (OLC) and the advanced restraint system parameters during the frontal rigid barrier collisions conducted over the recent three-year of a company were statistically analyzed. A Finite Element (FE) for crash simulation matrix was established. The Kruskal-Wallis non-parametric test and the Spearman correlation analysis methods were used to investigate the vehicle OLC effect and the restraint system parameters on occupant injuries. The results show that the increased OLC significantly increases the occupant injuries severities (the correlation coefficient ρ=0.66, the significance p value<0.01) while the airbag vent size and the retractor TTF (time to fire) cannot significantly affect occupant injuries. The increased seatbelt first-level load limiter mitigates head injury but increases chest compression. Using the Pyrotechnic Lap Pretension (PLP) to pretension lap belt and the Crash Locking Tongue (CLT) to cut off the transfer of seatbelt forces can slightly decrease the chest compression. Moreover, the occupant hip restraint is enhanced and the movements of hip and legs are reduced, which alleviate the vehicle interior-leg impact severity and significantly reduce the lower limbs injury risks.

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    Improved secondary prediction algorithm for tripping and non-tripping rollover for rollover warning
    TIAN Yang, LI Xiangpeng, FAN Yihong, LI Zhaonan, LI Liang
    2025, 16(5):  707-715.  doi:10.3969/j.issn.1674-8484.2025.05.005
    Abstract ( 91 )   HTML ( 82)   PDF (1804KB) ( 197 )  

    An improved dual-prediction rollover warning algorithm was proposed with an incorporating New Rollover Indicator (NRI) to accurately identify vehicle roll dynamics and predict rollover risks. The NRI applicable to both trip and non-trip rollovers was derived from a 6-degrees of freedom model. A combined primary-secondary prediction strategy was developed and integrated with the NRI, validated through the Carsim software and the Matlab/Simulink software co-simulation and hardware-in-the-loop tests. The results show that the proposed algorithm improves the vehicle stability with maintaining the prediction accuracy, and reduces the warning activations by 39.5% and 47.3% with decreasing the maximum warning peak time by 77.2% in rollover situations, compared to conventional secondary and primary prediction methods respectively in non-rollover scenarios. Therefore, the NRI implementation enhanced the algorithm's responsiveness to road excitations with expanding its applicability.

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    Design and application of durability simulation system for automotive mechanism based on co-simulation
    SHAO Longfei, MENG Yan, PANG Huan, WANG Daocheng, SONG Pankun, YE Zhiqiang
    2025, 16(5):  716-724.  doi:10.3969/j.issn.1674-8484.2025.05.006
    Abstract ( 113 )   HTML ( 79)   PDF (1722KB) ( 38 )  

    A co-simulation-based durability simulation system was developed based on the nonlinear coupling between gradual damage accumulation and performance degradation of components to enhance the efficiency and accuracy of durability evaluations for automotive mechanisms. This system constructed a closed-loop iterative process which involved dynamic response extraction, damage modeling, and parameter updating, and enabled dynamic coupling analysis of damage evolution and performance degradation. The system integrated key functional modules, which included a database, co-simulation, and durability evaluation, to assess the durability of a specific suspension mechanism. The results show that, compared to fixed-step iterative methods, the variable-step iterative approach improves the accuracy of wear depth evaluation for the lower ball bowl of a faulty vehicle suspension by 21.2% to 28.3%, demonstrating that the system provides a high-precision and high-efficiency digital solution for the durability design of automotive mechanisms.

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    Automotive Energy Efficiency and Environment Protection
    Electromechanical state stability evaluation method of series hybrid electric powertrain for heavy vehicle
    ZHONG Hao, WANG Weida, YANG Chao, YANG Liuquan, ZENG Gen, LI Tonghui
    2025, 16(5):  725-735.  doi:10.3969/j.issn.1674-8484.2025.05.007
    Abstract ( 67 )   HTML ( 5)   PDF (5479KB) ( 38 )  

    An evaluation methodology for electromechanical state stability was investigated to ensure the safe and stable operation of a heavy-duty vehicle with Series Hybrid Electric Powertrain (S-HEP). Eight dynamic indices were proposed based on the mechanical and electrical characteristics of S-HEP, including engine-generator torque coordination and energy supply coordination between primary and secondary power sources. The weights of evaluation indices were determined to achieve multi-index integration by using the analytic hierarchy process. A fuzzy comprehensive evaluation method was designed by combining fuzzy theory. The method mapped subjective fuzzy evaluations into quantitative scores through membership functions. The electromechanical state stabilities of a vehicle data were evaluated by using real-world tests under 11 working conditions. The results show that the comprehensive scores of a vehicle are 1.11, 2.45, 3.13, and 3.6, respectively for the low-speed (with an average speed being 18 km/h) constant velocity condition. the high-speed (73 km/h) constant velocity condition, the medium-speed (0~40 km/h) cyclic condition, and the sharp acceleration condition (0~40 km/h). These results demonstrate the effectiveness and repeatability of the proposed method. The evaluation outcome can quantify the level of electromechanical state stability of the system.

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    Hierarchical energy management strategy for PHEVs based on segmented SOC trajectory prediction
    DAI Lihong, JIN Nini, MO Zonghua, HU Peng, WAN Wenjun, LIU Haoye, WANG Tianyou
    2025, 16(5):  736-746.  doi:10.3969/j.issn.1674-8484.2025.05.008
    Abstract ( 175 )   HTML ( 31)   PDF (5607KB) ( 88 )  

    A hierarchical energy management strategy-adaptive initial equivalent factor strategy (HEMS-AIEFS) was proposed to achieve near-global optimal energy allocation under real driving conditions. HEMS-AIEFS adopted a two-layer structure: The upper layer implemented a node-split state-of-charge (SOC) planning method for batteries, which used a dynamic programming (DP) algorithm to generate the relevant data for training neural network models. These models can predict the SOC node trajectories of different road sections in real time; In the lower layer, the predicted equivalent consumption minimization strategy (P-ECMS) was used to track the predicted SOC trajectories, in which the adaptive initial equivalent factor strategy (AIEFS) was added to set the initial equivalent factor (EF0). The results show that the proposed AIEFS reduces fuel consumption by 2.36% to 7.69% compared to the conventional method of determining the initial equivalence factor, and that HEMS-AIEFS saves 1.56% to 9.13% of fuel consumption under different operating conditions comparing to the CD-CS strategy and requires 4.9% to 5.6% of the computation time of the DP algorithm. This study provides an effective optimization method for plug-in hybrid elective vehicle (PHEV) energy management optimization and demonstrates the potential application of navigation information in PHEV energy management optimization.

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    Online estimation of electric bus energy consumption based on vehicle dynamics and improved FFRLS algorithm
    ZHANG Xinfang, YAN Yiping, ZHANG Zhe, XU Zhigang, ZHANG Licheng
    2025, 16(5):  747-756.  doi:10.3969/j.issn.1674-8484.2025.05.009
    Abstract ( 96 )   HTML ( 5)   PDF (3574KB) ( 44 )  

    To improve the performance of electric bus energy consumption prediction models in terms of real-time capability, accuracy, and interpretability, this paper proposed a hybrid energy consumption prediction model that combined vehicle dynamics modeling and data-driven parameter identification for different operating conditions. The model established instantaneous power equations for acceleration, constant speed, and deceleration conditions, and calculated cumulative energy consumption through driving segment partitioning. The forgetting factors recursive least squares (FFRLS) method was introduced for online parameter identification, and the particle swarm optimization algorithm (PSO) was used to optimize the initial parameters and forgetting factors, resulting in the development of the real-time online predictive energy consumption model IFFRLS. The results show that the proposed FFRLS model performs excellently, achieving a maximum R-squared (R2) of 0.977 and a mean absolute percentage error (MAPE) of 11.16%, significantly outperforming the unmodified model.

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    Active grille shutter technology for hybrid electric light-duty trucks based on computational fluid dynamics
    ZHENG Songfeng, QIAN Duode, GONG Zhen, QIAN Yejian
    2025, 16(5):  757-765.  doi:10.3969/j.issn.1674-8484.2025.05.010
    Abstract ( 173 )   HTML ( 35)   PDF (3174KB) ( 38 )  

    The application of active grille shutter (AGS) technology was investigated to address the challenge of traditional fixed grilles in hybrid commercial vehicles failing to dynamically adapt to differentiated thermal management demands under dual-heat-source coupling conditions. The impact of AGS de-flection orientation and angle on the aerodynamic and thermal balance performance of a hybrid light truck was analyzed using computational fluid dynamics (CFD) simulations. And an optimized AGS control strategy was proposed to enhance heat dissipation efficiency while reducing aerodynamic drag, thereby balancing energy consumption and thermal regulation requirements in complex oper-ating scenarios. The results show that the full operating condition of AGS can significantly increase the air intake of the intercooler and radiator, improving the thermal balance performance. The lower deviation of the AGS blades can guide the airflow to the cooling components, and avoid complex chassis parts, resulting to enhance the heat transfer situation and reduce the drag coefficient by about 2.85%. Under the conditions of 50 km/h climbing and 110 km/h high-speed, setting 60° and 75° grille opening can meet the heat transfer needs of the hair cabin, but also reduce the wind resistance coefficient of about 3.21% and 3.88%, respectively. Therefore, it is an important technical means to improve the thermal balance performance and energy consumption state to carry out the AGS optimal matching design and to associate the AGS control strategy on the hybrid light truck model.

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    Multi-field coupling inhibits dendrite growth in zinc electrodeposition
    CHEN Yunxiang, ZHAO Jiahui, WANG Keliang, LIU Hanchao, WANG Hengwei, ZHANG Tianfu, PEI Pucheng
    2025, 16(5):  766-772.  doi:10.3969/j.issn.1674-8484.2025.05.011
    Abstract ( 115 )   HTML ( 6)   PDF (6023KB) ( 59 )  

    A electro-deposition morphology of zinc metal under different external fields was studied to effectively address the key issues such as safety risks and reduced cycle life caused by uncontrollable dendrite growth in traditional zinc metal batteries. The synergistic mechanism of current density and external field conditions on dendrite growth was particularly explored. The dendrite growth conditions of zinc metal under different current densities were tested by applying magnetic fields, ultrasound, and temperature fields separately. The results show that all three external fields can improve the zinc deposition morphology. The magnetic field promotes the convection of the electrolyte through the Lorentz force, the temperature field enhances the ion mobility by reducing the solution viscosity, and the ultrasound achieves micro-region stirring through the cavitation effect. These mechanisms jointly strengthenes the mass transfer process in the electrolyte. Especially in the case of applying ultrasound, a current density of 15 mA/cm2 is still guaranteed to be free of dendritic dendrite deposition.

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    Intelligent Driving and Intelligent Transportation
    Trajectory tracking control based on adaptive prediction time-domain MPC
    ZHENG Xunjia, CAO Zeyi, CHEN Xing, LIU Hui, GAO Jianjie
    2025, 16(5):  773-783.  doi:10.3969/j.issn.1674-8484.2025.05.012
    Abstract ( 147 )   HTML ( 6)   PDF (2467KB) ( 100 )  

    A trajectory tracking control algorithm integrating fuzzy control strategy with time-domain adaptive adjustment model predictive control (MPC) was proposed. to address the issue that road curvature and vehicle speed information are usually not considered in autonomous vehicle trajectory tracking control, and to suppress lateral deviations during vehicle trajectory tracking while enhancing the anti-interference ability of the control system, A vehicle kinematic model and a model predictive controller were established, different speed conditions were designed, road curvature and desired vehicle speed were taken as fuzzy control inputs, and the prediction horizon parameters of the MPC algorithm were optimized via the fuzzy controller. Joint simulations using Carsim and Simulink were carried out to implement trajectory tracking control at different speeds on two trajectories with distinct curvatures. The results show that, in the double lane change scenario, compared with the fixed-horizon controller and linear quadratic regulator (LQR), the adaptive time-domain MPC controller achieves a maximum reduction of 85.81% and 78.86% in lateral errors at low speed (30 km/h) and high speed (90 km/h) respectively; in the multi-curve scenario, it realizes a maximum reduction of 96.32% and 86.4% in lateral errors at low speed and high speed respectively. These findings confirm that the proposed control strategy can significantly improve the system's tracking performance, effectively reduce trajectory deviations and maintain the dynamic stability of the vehicle under different speed conditions.

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    Skeleton guided hierarchical autonomous valet parking path planning method with lane constraints
    PENG Qianlong, JIN bieshu, WANG Jianqiang, WANG Guangwei
    2025, 16(5):  784-792.  doi:10.3969/j.issn.1674-8484.2025.05.013
    Abstract ( 97 )   HTML ( 5)   PDF (2689KB) ( 60 )  

    A lane-level skeleton guided hierarchical path planning method for lane considered, skeleton assistance in conjunction with reeds-shepp (RS) curve (LCSA-RS) was proposed to address the real-time and safety challenges of path planning for automated valet parking in complex parking scenarios. The method employed a five-layer architecture: the parking spot decision layer determined optimal park-in/park-out points based on parking map; the map abstraction layer integrated skeletonization algorithm with lane constraints to construct sparse topological map; the global guidance layer generated key waypoint sequences using the A* algorithm; the path optimization layer produced smooth paths satisfying kinematic constraints within circle regions around key points; and the collision detection layer performed real-time risk assessment and triggered path replanning when necessary. The results show that, compared with the hybrid A* algorithm, the proposed LCSA-RS reduces the number of nodes searched in the global planning phase to one-thousandth of the former and shortens the total planning time by 95.5%, while confining planned paths within their respective lanes and preventing multi-vehicle path conflicts, thus providing a novel solution for real-time path planning in complex parking environments.

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    DV-PointPillars 3D object detection model based on dual pooling attention mechanism and vertical feature fusion
    PAN Yuheng, REN Chen, LU Weijia, LI Yang
    2025, 16(5):  793-801.  doi:10.3969/j.issn.1674-8484.2025.05.014
    Abstract ( 151 )   HTML ( 11)   PDF (3417KB) ( 55 )  

    A DV-PointPillars 3D object detection model based on dual pooling attention mechanism and vertical feature fusion was proposed to improve the issues of insufficient pillar feature representation ability and false/missed detection in pillar-based 3D object detection methods for point clouds. The max and average dual pooling attention mechanism was introduced into the encoding network. By utilizing both max pooling attention and average pooling attention mechanisms, this approach can fully leverage the point cloud information within pillars, thereby the representation ability of pillar features was improved. A vertical region feature generation network was designed to obtain the feature information of the pillars in the vertical direction, and the features were fused in the backbone network to improve the information compression problem caused by the encoding method, reduce misjudgment and improve the recognition ability of occlusion. Experiments were conducted on three categories of cars, pedestrians and cyclists using the KITTI dataset from three levels of difficulty: simple, medium and difficult. The results show that: compared with the PointPillars model, the average 3D detection average precision of the DV-PointPillars model for the three categories of vehicles, pedestrians, and cyclists increased by 4.02%, 5.17%, and 5.09% respectively after adding three modules, which verifies the effectiveness of the proposed method.

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    Clamping force estimation and control strategy based on electro-mechanical braking system
    DONG Zuomin, LIU Fengyi, LI Jinzhi, WANG Zhijian, LIU Zhaoyong
    2025, 16(5):  802-811.  doi:10.3969/j.issn.1674-8484.2025.05.015
    Abstract ( 113 )   HTML ( 3)   PDF (2176KB) ( 72 )  

    A clamping force estimation and control strategy for electro-mechanical braking system was proposed to meet the demand for high accuracy and fast response of braking system for autonomous vehicles. A double seventh degree polynomial was used to realize the envelope estimation of the clamping force, by fitting the polynomial curves during the clamping and releasing processes and introducing the first-order inertial link to describe the hysteresis characteristics at different releasing points, and a feed-forward dual-loop proportional-integral-derivative (PID) control method of the clamping force was designed to realize the high accuracy and fast response of the clamping force. The results show that the maximum error of the clamping force estimation is 528.17 N, and the estimation error is controlled within 4.40%; the control error is 270 N under four typical signals, and the error is controlled within 2.25%. The estimation and control strategy has good estimation and control accuracy under complex working conditions, and the fast response speed also enhances the reliability of the electro-mechanical braking system.

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