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  • 2026, Vol. 17 No. 1 Published on:28 February 2026 Previous issue   
    Review, Progress and Prospects
    From decoupling to synergy: A paradigm shift in data and computation co-scheduling for intelligent connected vehicles
    YUAN Hong, HUANG Kaisheng, TIAN Guangyu
    2026, 17(1):  1-17.  doi:10.3969/j.issn.1674-8484.2026.01.001
    Abstract ( 71 )   HTML ( 13)   PDF (1586KB) ( 79 )  

    Intelligent vehicle cyber-physical systems (IVCPS) are pivotal for transcending the limitations of single-vehicle intelligence, yet their performance is constrained by the conflict between massive data demands and dynamic, scarce communication and computation resources. This conflict stems from the strong coupling between data flow scheduling and computation task scheduling. Prevailing research often adopts a decoupled approach by optimizing these two aspects independently, overlooking the resultant systemic performance bottlenecks and lacking a comprehensive framework for Data-Computation Co-Scheduling. Therefore, this paper systematically reviews the paradigm shift in IVCPS scheduling from resource-driven independent optimization to task-driven integrated co-design. It dissects the evolution of coordination mechanisms, from explicit coordination to implicit fusion, and identifies key future research directions, particularly in applying multi-agent reinforcement learning to resolve distributed resource conflicts and ensuring the trustworthiness of artificial intelligence (AI) decisions. This study aims to establish a clear theoretical framework for the core issue of data-computation co-scheduling, providing crucial theoretical and technical support for the architectural design of next-generation intelligent transportation systems and advanced autonomous driving.

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    An inquiry into the current development status of China's liquid biofuel industry
    LIU Hongrong, WANG Lele, GAO Jianyong
    2026, 17(1):  18-32.  doi:10.3969/j.issn.1674-8484.2026.01.002
    Abstract ( 53 )   HTML ( 3)   PDF (1671KB) ( 28 )  

    Against the macro-backdrop of the global response to climate change and energy transition, deep decarbonization in the transportation sector has emerged as a new focal point of international geopolitical competition. Anchored in the “Dual Carbon” goals (carbon peak by 2030 and carbon neutrality by 2060), this article presents a systematic review and in-depth analysis of the four key biomass liquid fuel industries: bio-methanol, sustainable aviation fuel (SAF), bio-ethanol, and biodiesel.
    The article begins with a global perspective, detailing the industrial landscapes of pioneering regions such as the European Union, the United States, and Singapore. By analyzing the EU Renewable Energy Directive (RED Ⅲ), the EU Emissions Trading System (EU ETS), and the tax credit mechanisms within the U.S. Inflation Reduction Act (IRA), it reveals how internationally coordinated “green premium” mechanisms and binding compliance mandates collectively accelerate the commercialization cycle of industrial-scale liquid biofuel. In particular, the article highlights that mandatory decarbonization processes in international aviation and maritime transport—exemplified by the International Civil Aviation Organization’s Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA) mechanism and the International Maritime Organization’s Net-Zero Framework (NZF), which are catalyzing unprecedented demand for sustainable aviation fuel (SAF) and bio-methanol.
    The article then identifies a structural contradiction in China’s biomass liquid fuel industry characterized by “massive capacity with overseas market dependence.” While China maintains stability in the bio-ethanol sector and has gradually opened export channels for the SAF industry through a “whitelist” mechanism—alongside launching refueling pilots in multiple locations—the biodiesel industry has been severely impacted by EU’s anti-dumping investigations, resulting in a sharp decline in export volumes and chronically low plant utilization rates, exposing the vulnerability of over reliance on overseas markets. Furthermore, the domestic market contends with systemic constraints, such as the absence of specific tariff codes (HS codes), disorderly competition for feedstock, low localization of technical equipment, and a lack of mandatory blending standards. These issues prevent massive, planned capacities—such as over 10 million tons of bio-methanol—from materializing into actual economic benefits.
    In response to emerging challenges and opportunities, the article proposes a strategic framework for establishing an “internal industrial circulation” system. It suggests leveraging the national policy shift from “dual control of energy consumption” to “dual control of carbon emissions”. The key recommendations include implementing a comprehensive life-cycle green certification system for liquid biofuels; enhancing fiscal and financial incentives to support sustainable development; and explicitly prioritizing biomass feedstocks in national energy allocation strategies. Finally, the article calls for expediting domestic mandatory blending pilot programs in high-impact sectors such as aviation and maritime transport to overcome critical bottlenecks constraining industrial advancement. Collectively, these measures aim to achieve a strategic leap from “raw material export” to “high-value fuel application”.

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    Automotive Safety
    Research on pedestrian collision injury assessment based on monocular pose estimation
    WANG Meijun, MENG Yu, ZHENG Chao, PENG Xiaorui, XU Yan
    2026, 17(1):  33-39.  doi:10.3969/j.issn.1674-8484.2026.01.003
    Abstract ( 53 )   HTML ( 3)   PDF (1531KB) ( 37 )  

    A monocular pose-estimation-based method for evaluating pedestrian collision injuries in front of a vehicle was proposed to enable quantitative injury assessment of pedestrian emergency postures in traffic accidents. Posture parameters were extracted from single-view accident images using a pose estimation algorithm, and a skinned multi-person linear (SMPL) model was mapped to a multi-body model through joint matching to achieve rapid reconstruction of emergency postures. Subsequently, collision simulations of femur injuries were conducted using MADYMO under four typical postures, including standing, leaning forward, squatting, and evasive running. The results demonstrate that the proposed method achieves simulation accuracy comparable to that of manual modeling, with the mean differences in AIS2+ and AIS3+ injury-risk probabilities across the four postures as low as -3.5% and -1.22%, respectively, indicating that the proposed method improves the automation and reproducibility of posture modeling, and can provide quantitative references for vehicle structural optimization and pedestrian protection design.

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    Active anti-rollover control strategy for multi-axle heavy-duty vehicles based on torque distribution
    WU Zhongtao, SHEN Lilin, LI Bingbing, YIN Guodong, CHEN Boli
    2026, 17(1):  40-49.  doi:10.3969/j.issn.1674-8484.2026.01.004
    Abstract ( 38 )   HTML ( 3)   PDF (1993KB) ( 20 )  

    A multi-axle heavy-duty vehicle active anti rollover control system based on torque distribution was developed to improve the active anti rollover performance of multi-axle heavy-duty vehicles under high-speed extreme rollover conditions, taking the distributed electric drive multi axle heavy-duty vehicle as the object. The lateral load transfer rate was used as the roll criterion, and an Anti-Chattering Fuzzy Sliding Mode Control algorithm was proposed to obtain the desired additional yaw moment, and the differential yaw moment was generated by reasonably distributing all wheel drive torque to ensure the roll stability of the vehicle. And the effectiveness of the proposed method was verified by driver in the loop experiment. The results show that the proposed control method can successfully avoid vehicle rollover under the extreme condition of driver manual operation, and the maximum absolute value of vehicle lateral load transfer rate is reduced by about 2%, the maximum roll angle is reduced by about 4.9%, the time of lateral load transfer rate exceeding roll threshold decreases by 42.2%.

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    Effects of occupant anthropometry on thoracolumbar injury risk in reclined postures during frontal collisions
    HUANG Zhishan, PAN Di, HAN Yong, XIAO Zonghan, LIU Hui, QIN Zhenyuan
    2026, 17(1):  50-58.  doi:10.3969/j.issn.1674-8484.2026.01.005
    Abstract ( 31 )   HTML ( 3)   PDF (3848KB) ( 14 )  

    A full-vehicle frontal 100% overlap rigid barrier crash model was established to investigate the impact of anthropometric differences on occupant thoracic and thoracolumbar spine (T11—L5) injuries in reclined posture. The kinematic and injury responses of occupants in standard and reclined postures were compared, using the human finite element models THUMS (total human model for safety) of the 5th percentile female (5 F), 50th percentile male (50 M), and 95th percentile male (95 M). The results show that the reclined posture generally increases injury risk with injury patterns exhibiting significant body size specificity. Under the reclined posture, the 5 F exhibites the most pronounced thoracic injury risk, with lung pressure reaching 1 230 kPa and maximum principal rib strain reaching 4%. Injury risks for the 50 M and 95 M are concentrated in the thoracolumbar spine, the peak axial force and flexion moment at L2 of the 50 M exceeds the thresholds by 44% and 129%, respectively; the peak axial force at L1 of the 95 M exceeds the threshold by 38%, and the peak flexion moment at L5 exceeds the threshold by 111%. Moreover, the lateral bending moment of the thoracolumbar spine increases with larger body size.

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    Quantitative evaluation of automated driving safety oriented general functions detection
    MA Teng, MA Yulin, LI Yicheng, PAN Jiabao, XU Shucai
    2026, 17(1):  59-69.  doi:10.3969/j.issn.1674-8484.2026.01.006
    Abstract ( 37 )   HTML ( 3)   PDF (2120KB) ( 23 )  

    A quantitative safety-driving evaluation framework was proposed for autonomous driving functions of intelligent connected vehicles (ICVs) in China. Grounded in a standardized functional assessment protocol, the framework specifically targeted two critical safety-related capabilities, which were “identification and response to the dynamic states of surrounding vehicles”, and “identification and response to pedestrians and non-motorized vehicles”. A comprehensive evaluation model was developed, integrating three methodological components, which were extraction of expert driver behavioral features from naturalistic driving data, vehicle inverse dynamics decoupling to isolate control-relevant motion states, and F-norm-based matrix quantification of trajectory deviation and response timeliness. A high-fidelity co-simulation environment was constructed to enable rigorous validation by integrating PreScan, CarSim and Simulink. The quantitative safety-driving scores were obtained by applying this framework to two ICV platforms equipped with an industry-standard black-box autonomous driving system. The results demonstrate that the proposed method yields scores significantly more consistent with empirically observed expert driving behavior. Relative to conventional evaluation approaches, the framework improves overall performance assessment accuracy by 38.11% and 68.57% for the two test vehicles, respectively.

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    Automotive Energy Efficiency and Environment Protection
    Optimization of energy management in methanol-electric hybrid propulsion systems
    LI Zhao, LONG Wuqiang, TIAN Hua
    2026, 17(1):  70-78.  doi:10.3969/j.issn.1674-8484.2026.01.007
    Abstract ( 36 )   HTML ( 3)   PDF (1917KB) ( 22 )  

    A method was proposed by using integrating globally optimized dynamic programming (DP) with real time control to address the energy-saving and emission-reduction needs of inland waterway vessels and to improve the energy management strategies for methanol electric hybrid propulsion systems. A quasi steady state model was developed based on bench test data to characterize the fuel consumption and emission patterns of the methanol engine. The DP strategy was employed to establish a globally optimal benchmark for energy allocation. The real time control performance of the strategy was validated by using an NI PXIe Hardware In the Loop (HIL) platform. The results show that the DP strategy reduces the fuel consumption by 28.2% and lowers the pollutant emissions of HC, CO2, and NOx by 41.0%, 42.6%, and 30.5% respectively, compared with the deterministic Rule Based (RB) strategy. Therefore, it provides a practical energy management solution for methanol fueled hybrid vessels.

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    Carbon shell coating technology for proton exchange membrane fuel cell catalysts
    ZHU Caihan, LI Dewei, WANG Yunan
    2026, 17(1):  79-87.  doi:10.3969/j.issn.1674-8484.2026.01.008
    Abstract ( 41 )   HTML ( 3)   PDF (5809KB) ( 14 )  

    A carbon shell coating technique applicable to mesoporous catalysts was developed to address the issues of particle agglomeration and dissolution in platinum-based catalysts, and to enhance catalyst activity and durability through physical isolation. A selective carbon shell coating was applied to platinum nanoparticles anchored on the outer surface of mesoporous carbon catalysts. The effects of impregnation time, prepolymerization time, and dopamine concentration on the mesoporous catalyst pore structure and performance were systematically investigated. The results indicate that the sensitivity factors influence the catalyst structure and performance following the order: prepolymerization time, impregnation time, dopamine concentration. The optimal carbon shell coating parameters are determined as prepolymerization for 90 min, impregnation for 10 min, and dopamine concentration of 0.3 mg/mL. Under the condition, the obtained carbon shell coated catalyst exhibits a mesoporous structure retention rate of 95% and a carbon shell thickness of approximately 0.83 nm. The half-wave potential is increased by 46 mV compared to the pristine catalyst. After 30 000 cycles of accelerated degradation testing, the half-wave potential decays by only 5 mV, representing an 86% improvement in durability relative to the pristine catalyst.

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    Research on effects of flame tube hole diameters on combustor combustion performance based on neural network
    FU Xueqing, CHEN Chuang, ZHOU Daoqing, ZHANG Yan, CAO Xiaolin, LI Xin, JI Jianbo, CHEN Peng, XUE Xingxu, LI Yaozong
    2026, 17(1):  88-95.  doi:10.3969/j.issn.1674-8484.2026.01.009
    Abstract ( 30 )   HTML ( 6)   PDF (1997KB) ( 15 )  

    The combustion performance of combustors in micro turbine engine is affected by flame tube hole diameters, lacking analytical methods for evaluating the combined effects of multiple flame tube hole diameters. It is difficult to clearly determine the diameter distribution ranges of primary hole, middle hole, and dilution hole of flame tube that can achieve high combustion efficiency and low overall temperature distribution factor (OTDF). To address this issue, a combustion performance prediction model was established by learning from the results obtained from the calibrated model of a combustor with the help of neural work. It has been utilized to study the combined effects of multiple hole diameters on combustion performance. The results show that the influence of dilution hole, primary hole and middle hole diameters on combustion efficiency gradually decreases, and the influence of dilution hole diameter on the overall temperature distribution factor (OTDF) is significantly higher than that of primary hole and middle hole diameters. Combustion efficiency is low and OTDF is high at different primary hole and middle hole diameters in case of small dilution hole diameter. With the increase of dilution hole diameter, high temperature combustion zone enlarges obviously in middle zone, which is benefit to increase combustion efficiency. In the meantime, the jet depth of the air flowing from dilution holes increases, which strengthens the mixing of air with high temperature gas and reduces OTDF.

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    In-situ experimental characterization for the thermal properties of cylindrical lithium-ion power batteries
    ZHANG Xiaojun, WANG Jie, SHENG Lei, ZHANG Huanjvan, SHI Junming, WANG Qian
    2026, 17(1):  96-103.  doi:10.3969/j.issn.1674-8484.2026.01.010
    Abstract ( 19 )   HTML ( 0)   PDF (2253KB) ( 7 )  

    A theoretical model and an in-situ method were proposed for measuring the thermophysical properties of batteries with considering heat loss factors to investigate the radial thermal conductivity and the specific heat capacity of cylindrical lithium-ion batteries of type 21700 and type 18650 in the temperature range of -20~60 °C to simulate the operating conditions from extreme cold to high temperature. The results show that both the radial thermal conductivity and the specific heat capacity increase with increasing temperature, with thermal conductivity increasing by 7.6% and specific heat capacity by 23%. The battery steel casing enhances the heat dissipation and the temperature uniformity, having a greater impact on the specific heat capacity than the impact on the thermal conductivity. The measuring accuracy for radial thermal conductivity reaches 95.2% with accounting for heat loss, and the accuracy for specific heat capacity reaches 98.7%, both are higher than the maximum accuracy of 93.8% which obtained without considering heat loss.

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    Research on thermal spread laws of lithium-ion batteries in enclosed spaces
    FENG Xupeng, LI Chengbing, LI Rui, XIAO Ke, PENG Junheng, WU Sixiang, LIU Bohao
    2026, 17(1):  104-113.  doi:10.3969/j.issn.1674-8484.2026.01.011
    Abstract ( 28 )   HTML ( 2)   PDF (2376KB) ( 10 )  

    Using the high-energy-density ternary lithium-ion battery model LGMJ1 as the subject of investigation, the research integrated experimental data with chemical reaction kinetic modeling to conduct coupled one-dimensional and three-dimensional numerical simulations to investigate the propagation characteristics of thermal runaway in confined environments and evaluate protective strategies for lithium-ion batteries. The impact of three key parameters—the pressure-relief vent area, the air convective heat transfer coefficient, and the thermal insulation layer properties—on thermal propagation within a battery module was investigated, based on a coupled thermal-gas dynamics model developed using Amesim and STAR-CCM+. The results show that the arrangement of battery modules has a significant impact on battery thermal propagation. The fewer adjacent contact batteries with the triggering cells, the easier it is to trigger battery thermal propagation. Coordinated optimization of the vent area and convective cooling conditions can achieve efficient suppression of the thermal propagation process. Among the insulation materials, ceramic aerogel has the best performance and can effectively inhibit the propagation of thermal runaway.

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    Economic feasibility analysis on low-cost hydrogen utilization in China's hydrogen-powered transportation
    ZHAO Jiayi, HU Wenyu, LIAO Mengke, HAN Tianyi, ZHOU Honglian, LI Zhongzheng
    2026, 17(1):  114-121.  doi:10.3969/j.issn.1674-8484.2026.01.012
    Abstract ( 28 )   HTML ( 4)   PDF (1954KB) ( 8 )  

    The cost components and influencing factors of hydrogen production via water electrolysis was systematically analyzed to evaluate the economic viability and development potential of hydrogen fuel cell heavy-duty trucks (HFC-HDTs). Focusing on 49-ton heavy-duty trucks, the total cost of ownership (TCO) of HFC-HDTs, battery electric trucks, and diesel trucks was compared, while the predicting future cost reduction trends was predicted. The results indicate that grid stability and electricity price fluctuations significantly affect the economics of electrolytic hydrogen production. Driven by policy subsidies and technological advancements, the cost of HFC-HDTs in 2024 is 2.82 CNY/km, approaching the 2.30 CNY/km of diesel trucks. Furthermore, cost parity between hydrogen and diesel is expected to be achieved by 2030 if the hydrogen price drops to 30 CNY/kg, and by 2025 if the price falls to 25 CNY/kg. Consequently, with the significant reduction in vehicle manufacturing costs, HFC-HDTs are poised to achieve cost competitiveness against diesel trucks in the future, provided that hydrogen prices decrease further.

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    Intelligent Driving and Intelligent Transportation
    Path planning method for leader-follower multi-vehicle formation with integrating GoT-SAC
    WANG Yue, DUAN Hongwei, ZHONG Wei, YANG Lu, HE Lei, CHAI Fulai, SHI Xiaoyang
    2026, 17(1):  122-129.  doi:10.3969/j.issn.1674-8484.2026.01.013
    Abstract ( 32 )   HTML ( 3)   PDF (2427KB) ( 14 )  

    A leader-follower formation path planning method was proposed through integrating the Goal-oriented Transformer (GoT) and the Soft Actor-Critic (SAC) on the Mecanum Wheeled intelligent platform, named GoT-SAC, to enhance the stability and efficiency of formation operation in unknown environments. Experimental validation was conducted in both the Gazebo environment and on a miniature physical platform. The results show that the GoT-SAC model convergences within 95~100 training episodes. The average relative pose error reduces from 18 cm to 6 cm with a path-length relative-difference being below 5% compared with the manual remote-control strategy. Therefore, the proposed method achieves stable formation and efficient obstacle avoidance without relying on prior map information.

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    Predictive cooperative-adaptive cruise-control for the intelligent- connected vehicles in the mixed traffic
    HAN Dongming, CHENG Sizhe, WANG Jinxiang, LIU Yahui, YIN Guodong
    2026, 17(1):  130-139.  doi:10.3969/j.issn.1674-8484.2026.01.014
    Abstract ( 35 )   HTML ( 0)   PDF (3015KB) ( 8 )  

    A predictive Cooperative Adaptive Cruise Control (CACC) method was proposed based on the Physics-Informed Neural Network (PINN) and a longitudinal behavior prediction algorithm for human-driven vehicles (HDV) was developed, to address the behavioral uncertainty of HDV in mixed traffic environments including intelligent-connected vehicles. The PINN-based HDV behavior prediction model was constructed, in which an optimization problem with differential-equation constraints was transformed into a neural network parameter fitting problem. The predicted states of HDV were used as reference inputs to design a predictive CACC controller for mixed traffic based on Model Predictive Control (MPC) with soft constraints, referred to as PINN-MPC. The proposed controller was validated through simulations on the HighD dataset. The results show that the PINN controller with physical information improves the acceleration prediction accuracy by 28.9% within a 3 s prediction horizon compared with neural networks without physical information. Therefore, the proposed cruise control strategy enhances both driving safety and ride comfort.

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    Distributed active perception path planning for the estimation of parking occupancy status
    YANG Zongru, HU Yunze, LIU Shiqi, GUAN Yang, WU Wei, LIU Chang
    2026, 17(1):  140-148.  doi:10.3969/j.issn.1674-8484.2026.01.015
    Abstract ( 30 )   HTML ( 2)   PDF (2546KB) ( 11 )  

    A path planning algorithm, named “multi-vehicle Monte Carlo Bayes filter tree (MV-MCBFT)”, was proposed for the distributed active perception of multiple autonomous vehicles to estimate the occupancy status of parking lots in real time. A sequential, feed-forward cooperative motion planning strategy driven by predicted observations was proposed by constructing a probabilistic state-transition model for parking lots, with designing a multi-source Bayesian filtering fusion mechanism, and with incorporating submodular maximization principles. The results show that the MV-MCBFT achieves near-optimal performance consistent with the traversal algorithm in terms of entropy reduction ratio and estimation accuracy, while consuming only 1% of the runtime required by the traversal algorithm. The MV-MCBFT has the entropy reduction ratio by 43.70% and the estimation accuracy by 51.43% comparing with the random-walk algorithm. Therefore, the proposed method enhances the effectiveness of parking lot state estimation.

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