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Welcome to Journal of Automotive Safety and Energy,
汽车安全与节能学报

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  • 2025, Vol. 16 No. 4 Published on:30 August 2025 Previous issue    Next issue
    Review, Progress and Prospects
    Progress of mobile charging robot for photovoltaic energy storage and charging
    LI Shunming, WANG Changrong, SHI Wenbei
    2025, 16(4):  505-520.  doi:10.3969/j.issn.1674-8484.2025.04.001
    Abstract ( 301 )   HTML ( 114)   PDF (2064KB) ( 724 )  

    To address the rapidly growing charging demands of new energy vehicles, mobile charging robots integrated with photovoltaic energy storage and charging systems have emerged as a crucial direction in research and development. This paper outlines the necessity and significance of developing photovoltaic energy storage systems and mobile charging robots for new energy vehicles, along with their fundamental operational modes. It presents the structural framework and core advantages of the photovoltaic energy storage and charging system, as well as the classification and scenario-specific adaptability of mobile charging robots. Furthermore, the economic viability, safety, and reliability of photovoltaic energy storage and charging mobile robots are analyzed. The study reviews the current research status of three key technologies—autonomous charging, path planning, and charging port recognition and insertion—and evaluates their respective strengths and limitations. This paper also summarizes the development of a new system for application-oriented research on photovoltaic energy storage and mobile charging robots, along with its key enabling technologies, and explores various specialized application scenarios. Finally, the paper identifies the challenges faced by photovoltaic energy storage and charging technologies in areas such as energy transmission efficiency, safety and stability, dynamic programming, charging port identification and insertion, advanced energy storage solutions, and the expansion of application domains. It also provides insights into the future development trends of mobile charging robots.

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    Automotive Safety
    Modeling and simulation of solenoid valve for one box electro-hydraulic braking system
    ZHAO Xinyu, XIONG Lu, ZHUO Guirong, LI Jing, SHU Qiang, PAN Guangliang
    2025, 16(4):  521-528.  doi:10.3969/j.issn.1674-8484.2025.04.002
    Abstract ( 630 )   HTML ( 104)   PDF (2684KB) ( 90 )  

    In order to explore the working characteristics of the pressure boosting valves and pressure reducing valves of the One Box Electro-Hydraulic Braking System (EHB), multi-field-coupled modeling and simulating methods of pressure boosting valves and pressure reducing valves were proposed, and results were verified by innovative testing bench. The structures and working principals of pressure boosting valves and pressure reducing valves were introduced. Each physical characteristic of pressure boosting valves and pressure reducing valves were precisely modeled. Multi-field coupling simulations, including electromagnetic field, flow field, and motion field, and experimental verifications for the pressure boosting valve were conducted. Simulations and experimental verifications were performed for the pressure reducing valve, including electric circuit, electromagnetic field, and motion field. The results show that the simulation error of the flow rate of pressure boosting valve is lower than 1.5 mL/s, the open delay response error of pressure reducing valve is smaller than 1.3 ms, and the close delay response error smaller than 0.3 ms, indicating that the proposed simulation method has a high accuracy, and providing a guidance for the control of the valves.

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    Emergency vehicle detection in noisy environments based on acoustic spectral-temporal information fusion
    LI Hao, ZHOU Hao
    2025, 16(4):  529-538.  doi:10.3969/j.issn.1674-8484.2025.04.003
    Abstract ( 190 )   HTML ( 66)   PDF (8078KB) ( 114 )  

    An in-vehicle detection method was proposed based on the fusion of spectral and temporal features to detect the external emergency vehicle sirens during high-speed driving. The input audio signal was transformed using the fast Fourier transform, and its log-Mel spectrogram was computed to extract spectral features. A convolutional neural network was used to model the raw waveform in the time domain, yielding temporal features. A coordinate attention mechanism was used to fuse and enhance the spectral and the temporal representations. The fused features were subsequently fed into a classifier for final detection. The experiments were conducted on both public and real-recorded datasets. The results show that on the LSAD-EVSRN dataset, the proposed method achieves an AUC (area under the receiver operating characteristic curve) score of 98.92%, with representing an improvement of 14.88% compared to using temporal features alone, and 2.52% compared to using spectral features alone. These results confirm the effectiveness of the fusion strategy, with a high robustness particularly under noisy conditions.

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    Protective effect of mechanical massage car seat on occupant injury in rear-end collision
    ZHU Huiting, MOU Yanyan, LAN Yang, XIANG Lei, YANG Jie, CHENG Zhihua, WANG Junliang, YANG Na
    2025, 16(4):  539-547.  doi:10.3969/j.issn.1674-8484.2025.04.004
    Abstract ( 217 )   HTML ( 72)   PDF (6921KB) ( 214 )  

    To evaluate the potential injury risks of mechanical massage seats during vehicle rear-end collisions, this study employed the Hybrid III 50th percentile male dummy model to conduct comparative crash simulations between conventional automotive seats and mechanical mas-sage seats, with particular focus on analyzing occupant injuries to the head, neck, chest, and lumbar spine. The results showed that when using the 3ms resultant acceleration as the chest injury criterion, the values for mechanical massage seats and conventional seats are 26.6 g and 27.7 g, respectively, both meeting requirements; for the normalized neck injury criterion (Nij), conventional seat occupants exceedes the threshold of 1, indicating significant injury risk, while mechanical massage seat occupants demonstrates excellent performance across all neck injury metrics with an Nij value of 0.51, providing better protection; mechanical massage seats show greater advantages in reducing head injury risk, with lower HIC values for occupants; regarding lumbar injuries, the maximum force on conventional seat occupants is 1 670 N compared to 1 800 N for mechanical massage seat occupants, with the maximum LIC values being 4.32 and 3.67, respectively, both meeting safety standards and ensuring passenger safety. This research verifies the safety and reliability of mechanical massage seats in rear-end collisions, providing important reference value for future development and widespread application of mechanical massage seats.

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    Structural parameter design of brake-by-wire system for light commercial vehicle based on multi-objective optimization
    ZHANG Tao, CHEN Yuguo, QIN Yufu, WANG Yanzi, WANG Yang, MA Rui
    2025, 16(4):  548-557.  doi:10.3969/j.issn.1674-8484.2025.04.005
    Abstract ( 150 )   HTML ( 5)   PDF (2051KB) ( 61 )  

    To meet the high-performance braking requirements of light commercial vehicles, an optimization design method for the structural parameters of the brake-by-wire (BBW) system based on multi-objective optimization was proposed. This method established the basic configuration of the wire-controlled motion system, formulated optimization objective functions and constraint conditions for braking force, transmission efficiency, and the end motion speed of the actuator. The weights of each objective function were calculated using the fuzzy analytic hierarchy process, and a multi-objective comprehensive optimization function was constructed through linear weighting. Based on this function, the particle swarm optimization algorithm was employed to solve for the optimal values of the multi-objective function, thereby determining the structural optimization parameters of the wire-controlled motion system. This ensured that the overall performance of the system reached its optimal state. The results indicate that following the parameter optimization design, the braking force of the BBW system increases by 8.8%, the end movement speed of the actuator improves by 12.5%, and the transmission efficiency rises by 5.1%. The proposed optimization method for the comprehensive performance index effectively enhances the braking capacity and operational efficiency of the BBW system.

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    Automotive Energy Efficiency and Environment Protection
    Simulation study on energy absorption characteristics of laminated windshield under sunlight
    ZHANG Peilin, LI Yibing
    2025, 16(4):  558-567.  doi:10.3969/j.issn.1674-8484.2025.04.006
    Abstract ( 163 )   HTML ( 60)   PDF (1945KB) ( 104 )  

    To study the effect of solar radiation on the energy absorption properties of laminated windshields, a photothermal-mechanical coupling simulation model was proposed, which took into account the effect of sunlight on the temperature of polyvinyl butyral (PVB) interlayer and the mechanical property of the laminated glass. This study conducted a photothermal modeling of the temperature rise of glass under sunlight, and converted the temperature results into PVB interlayer modulus using dynamic mechanical analysis (DMA) tests. The modulus was used as the input for the headform-windshield impact finite element model to calculate the energy absorption property and pedestrian protection property of the windshield. The results show that in summer, when the transmitted solar power of the windshield is 700 W/m2, the steady-state temperature of the interlayer increases to 70 ℃, the modulus of the interlayer decreases to about 1/500 of that at room temperature, and the critical speed of the headform penetrating the windshield decreases to 20 km/h from 40 km/h tested experimentally at room temperature. Solar radiation reduces the modulus of the interlayer by increasing the temperature of the interlayer, thus reducing the energy absorption performance of the laminated windshields, and increasing the risk of the head penetrating the windshield and having a secondary collision with objects inside the cabin.

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    Optimized design of wind-liquid double cycle for lithium-ion battery system in energy storage power station
    LIU Jinyi, WANG Yan, PANG Yingjie, YU Ruiguang, MOU Ruitao, LU Languang, LI Yalun, WANG Hewu, ZHANG Lilei, LI Mingming
    2025, 16(4):  568-576.  doi:10.3969/j.issn.1674-8484.2025.04.007
    Abstract ( 137 )   HTML ( 4)   PDF (2756KB) ( 65 )  

    A thermal management system with dual air-liquid circulation was proposed based on the temperature homogeneity control and the dynamic temperature difference regulation to enhance the temperature uniformity in lithium-ion battery systems for energy storage power stations. The system utilized air cooling under low-to-medium temperature conditions to combined air-liquid cooling in high-temperature environments. Simulation experiments investigated 4 control parameters including the air volume, the air temperature, the coolant temperature, and the coolant flow rate. The heat exchange structure was optimized through 1D-3D co-simulations to analyzing the significance of temperature rise and the uniformity of performance parameters. The results show that under twice the rated high-power discharge, the proposed system reduces the end-of-discharge temperature difference by 18% compared to the conventional bottom cold plate structures. A 300 s air supply cycle achieves a 31% reduction in the temperature difference (of 1.18 °C) versus a 100 s cycle. Parameter sensitivity decreases in the order as the air temperature, the air volume, the coolant temperature, and the coolant flow rate. Therefore, the dual air-liquid circulation design enhances dynamic temperature difference control with extending the service life of energy storage battery systems.

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    Eco-car-following strategy based on the CO2 emission characteristics of car-following pairs
    YU Qian, GUO Yuanyuan, YANG Mingpeng, ZHANG Yuting
    2025, 16(4):  577-586.  doi:10.3969/j.issn.1674-8484.2025.04.008
    Abstract ( 143 )   HTML ( 4)   PDF (2709KB) ( 130 )  

    An eco-car-following (ECF) strategies was explored with the CO2 emissions of car-following behavior in mixed traffic flow under the environment of intelligent connected vehicles. The vehicle trajectory data was used to extract multi-dimensional car-following behavior feature parameters. An eXtreme Gradient Boosting (XGBoost) model was established with calculating and analyzing the effects of car-following behavior feature parameters on CO2 emissions during the car-following process by using the Shapley Additive exPlanations (SHAP) algorithm. The intelligent driver model of human-driven vehicles was calibrated. The Simulation of Ur-ban MObility (SUMO) platform was using to simulate 11 mixed traffic scenarios. The Adaptive Cruise Control (ACC) and the Cooperative Adaptive Cruise Control (CACC) models were employed for Connected and Automated Vehicles (CAVs). The results show that the instantaneous mass CO2 emissions of CACC-CACC vehicle pairs de-crease by more than 60% when the proportion of CACC vehicles exceeds 50%. There-fore, the strategy reduces CO2 emissions for CAVs and CACC-CACC car-following pairs in mixed traffic flow scenarios.

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    Intelligent Driving and Intelligent Transportation
    Deep reinforcement learning-based strategy for freeway ramp metering
    HAN Yu, CHEN Zhixuan, WANG Yixuan, LI Chunjie, LEI Wei, JIAO Yanli, LIU Pan
    2025, 16(4):  587-597.  doi:10.3969/j.issn.1674-8484.2025.04.009
    Abstract ( 153 )   HTML ( 8)   PDF (6446KB) ( 62 )  

    Given that current research on ramp control methods based on reinforcement learning (RL) has not thoroughly addressed key issues such as learning cost and policy transferability during policy training, the practical application of these control strategies remains challenging. To address this issue, this paper proposed a RL approach aimed at optimizing ramp control strategies and conducted extensive simulation experiments to investigate the portability of the proposed method. A ramp control model was constructed, and a model training method based on deep reinforcement learning was proposed. The bottleneck in a certain convergence area of Rongwu Expressway in the main external road network of Xiongan District was selected as the experimental scenario. The deep RL algorithm was used to train the ramp metering model, and the performance of the control strategy during the training process was compared with the classical ramp control method, thereby quantitatively analyzing the learning cost. Different simulation models and multiple sets of model parameters were selected as the test environment, and the influence of the differences between the training environment and the test environment on the control strategy was analyzed. The results show that when the difference between the training environment and the test environment is within 20%, the RL control method is significantly superior to the classical ramp control method in improving the traffic efficiency. However, when the difference exceeds 20%, the effects of the two methods are comparable.

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    Lateral control for unmanned mining trucks based on fuzzy MPC
    ZONG Jianzhuang, WU Guangqiang, MAO Libo, GUI Yuhui
    2025, 16(4):  598-609.  doi:10.3969/j.issn.1674-8484.2025.04.010
    Abstract ( 145 )   HTML ( 4)   PDF (3615KB) ( 66 )  

    To address the issue of steering lag and improve the accuracy of lateral control in unmanned mining trucks, this study proposed a lateral control algorithm based on fuzzy model predictive control (FMPC). First, the vehicle dynamics model and tracking error model were established. Subsequently, a vehicle state prediction method based on dynamic preview time was designed, and the tracking error was calculated according to the predicted vehicle state after the preview period. Furthermore, by integrating fuzzy control with model predictive control (MPC), an MPC controller was developed that adaptively adjusts the weight matrices of both lateral error and heading angle error. The effectiveness of the proposed FMPC algorithm was validated through hardware-in-the-loop simulation experiments and real-vehicle tests. The results indicate that, in the hardware-in-the-loop simulation, the maximum lateral error of the FMPC algorithm is reduced by 43.0% compared to the Pure Pursuit algorithm. In real-vehicle experiments conducted under two operational conditions—empty-load uphill driving and heavy-load parking—the maximum lateral errors are reduced by 50.1% and 17.6%, respectively, in comparison to the Pure Pursuit algorithm, demonstrating that the FMPC algorithm achieves superior control performance and significantly enhances the lateral control accuracy of unmanned mining trucks.

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    End-to-end decision-making model for multi-task autonomous driving
    OUYANG Delin, QIU Yifan, WANG Yingchen, YANG Liang, MIN Haigen, WANG Wenjun, LI Guofa
    2025, 16(4):  610-619.  doi:10.3969/j.issn.1674-8484.2025.04.011
    Abstract ( 164 )   HTML ( 27)   PDF (2003KB) ( 93 )  

    To address the challenges of spatiotemporal feature processing and inter-task dependencies in autonomous driving decision-making, this paper proposed an end-to-end driving decision model based on a 3D window self-attention mechanism. By applying window self-attention to compute the spatiotemporal features of the input sequence, and combining multi-task learning with loss weight allocation, the model effectively extracts features from driving videos and predicts vehicle speed and steering angle. The results demonstrate that the proposed model achieves prediction accuracies of 86.32% for steering angle and 85.36% for vehicle speed, outperforming models such as FMNet, Swin-Transformer, and MobileT-DSM. Moreover, it requires only 57.48 GFLOPs of computational cost, exhibiting superior spatiotemporal feature extraction as well as a better trade-off between performance and efficiency.

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    Multi-objective structural optimization of heavy truck frame based on SIMP algorithm and GRSM algorithm
    ZHANG Xiao, LIU Yong, JIANG Xuesheng, LIAO Yilong, HE Feng
    2025, 16(4):  620-628.  doi:10.3969/j.issn.1674-8484.2025.04.012
    Abstract ( 154 )   HTML ( 5)   PDF (1997KB) ( 45 )  

    A multi-objective structural optimization on high-strength lightweight design was conducted to address the issues of the deformation and cracking in heavy truck frames. A finite element model of the frame was established by using a pre-processing software Hypermesh with taking a specific 11-meter heavy truck frame as the research subjects. The original heavy truck frame underwent multi-condition multi-objective topology optimization using the Solid Isotropic Material with Penalization (SIMP) method to identify the optimal load-bearing structure. The global response surface method (GRSM) was employed for multi-objective dimensional optimization of the heavy truck frame to reduce its mass. The static and the modal analyses were performed on the frame under the conditions of the full-load bending, the full-load torsion, the full-load cornering, and the full-load braking. The results show that the optimized frame achieves an average stiffness increase of at least 21.1%, a minimum increase of 8.9% in low-order average dynamic frequency, and a mass reduction of 3.2%. Therefore, this has enabled the high-strength lightweighting of heavy truck frames.

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    Research on AEB control of autonomous vehicles based on sensor fusion perception
    GAO Chaojun, LI Yicheng, CAI Yingfeng, WANG Hai, JIANG Jin
    2025, 16(4):  629-637.  doi:10.3969/j.issn.1674-8484.2025.04.013
    Abstract ( 163 )   HTML ( 39)   PDF (1726KB) ( 101 )  

    To address the limitations of existing automatic emergency braking (AEB) systems—such as the susceptibility to obstacle misidentification in complex scenarios, the insufficient consideration of the preceding vehicle's acceleration, and the lack of control precision—this paper proposed an obstacle detection approach that integrated visual and LiDAR perception. A hierarchical AEB control strategy based on model predictive control (MPC) was designed to determine the desired braking deceleration, and a proportional-integral-derivative (PID) controller was employed to regulate the vehicle's brake master cylinder pressure. The results show that the proposed obstacle detection method can accurately identify obstacles in complex scenarios. Furthermore, the controller enables the vehicle to achieve a 100% deceleration rate across various AEB test scenarios, with braking acceleration being output as intended. The proposed methodology effectively enhances both safety and ride comfort during the automatic emergency braking process.

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    Simulation of intelligent vehicle trajectory tracking based on neural network adaptive MPC
    WANG Lin, CHEN Qinghua, YE Hongling, WANG Pengfei, XU Chi, QIAN Aiwen
    2025, 16(4):  638-647.  doi:10.3969/j.issn.1674-8484.2025.04.014
    Abstract ( 247 )   HTML ( 101)   PDF (1731KB) ( 164 )  

    The weight matrix of traditional model predictive control (MPC) controllers usually relies on manual experience for parameter tuning, making it difficult to adapt to complex dynamic environments. Therefore, a method for adaptive adjustment of MPC weight matrices based on backpropagation (BP) neural networks was proposed. Firstly, the intelligent vehicle dynamics model with MPC control was established to analyze the influence of different weight coefficients on the vehicle trajectory tracking performance, secondly the data were constructed to train the BP neural network model, and the BP neural network adaptive MPC controller was constructed using the Matlab/Simulink module to jointly simulate with Carsim, and finally, a double-shift simulation condition was designed from different speeds and road adhesion coefficients to validate the robustness of the controller under different working conditions. The results show that the BP neural network-based adaptive MPC controller achieves favorable control performance across different speeds when the road surface adhesion coefficient is 0.85. At a speed of 65 km/h, the vehicle under the fixed-weight MPC control approaches destabilization, whereas the root-mean-squares (RMS) of the lateral displacement deviation and lateral angle deviation for the adaptive controller are reduced by 44.17% and 66.66%, respectively. The proposed controller also exhibits strong performance on road surfaces with varying adhesion coefficients—most notably on slippery roads with an adhesion coefficient of 0.35. When traveling at 30 km/h under such conditions, the RMS values of the two deviations are decreased by 27.49% and 49.54% compared to the fixed-weight MPC controller. This neural network-based approach for adaptive adjustment of MPC controller weights can provide valuable insights for enhancing trajectory tracking performance in medium-and high-speed cooperative control of intelligent connected vehicles, as well as in autonomous navigation systems for special operation vehicles.

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    Adaptive identification of dynamic parameters for commercial buses based on SQP and GRNN
    FANG Xibo, NING Yigao, ZHAO Xuan, ZHOU Meng
    2025, 16(4):  648-656.  doi:10.3969/j.issn.1674-8484.2025.04.015
    Abstract ( 164 )   HTML ( 27)   PDF (2077KB) ( 62 )  

    An adaptive identification strategy was proposed based on the generalized regression neural network (GRNN) model and the sequential quadratic programming (SQP) algorithm to obtain and identify the key dynamic parameters of commercial vehicles in real time. A GRNN model was established and trained using the training data obtained via the SQP algorithm, with being enabled to adaptively identify key parameters according to the vehicle’s operating states. A co-simulation platform was built with integrating the TruckSim and the Matlab/Simulink to conduct simulation experiments under various driving conditions. The results show that compared with a fixed-parameters model, under the sine wave steering input condition, the maximum error of the vehicle’s sideslip angle is reduced by 73.9% than the TruckSim model with the maximum error of the roll angle being reduced by 76.7%. Meanwhile, these two errors are reduced by 98.0% and 63.1% under the double-lane change condition, respectively. Therefore, these results demonstrate the feasibility and effectiveness of the proposed method.

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