Table of Content

25 September 2024, Volume 52 Issue 9

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Energy，Power & Electrical Engineering

Two-Stage Distributional Robust Optimization for the Expansion Planning of Photovoltaic Cluster



ZENG Jun, WANG Tianlun, HUANG Zhipeng, et al

2024, 52(9):  1-11.  doi:10.12141/j.issn.1000-565X.240117

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With the deepening of the two-carbon target, the penetration rate of renewable energy is increasing year by year, and its consumption problem has attracted much attention. Distributed renewable energy cluster is a new mode of accommodating renewable energy. It is necessary to consider the influence of source-load uncertainty in planning and operation. In this paper, based on the new photovoltaic grid-connected planning of distributed photovoltaic cluster, considering the uncertainty of source and load, a distributed photovoltaic cluster expansion planning method based on two-stage robust optimization was proposed. Considering the difference between the planning stage and the operation stage, it established a two-stage distributed robust optimization model, which takes the minimum annual equivalent cost as the objective and considers the unit output constraint and the power grid carrying capacity. In order to improve the computational efficiency, the historical data of regional distributed renewable energy and random load were reduced and modified by combining K-means clustering with extreme scenario method. Based on the modified scenario set, a probability distribution fuzzy set based on Wasserstein distance was constructed. The column and constraint generation algorithm was used to decompose the two-stage distributed robust optimization model into the main problem and the sub-problem. The main problem and the sub-problem were solved by iteration, which further improves the efficiency of the solution. In order to solve the sub-problem, Lagrange duality was introduced to transform the sub-problem into a deterministic optimization problem. Finally, a distributed photovoltaic cluster was taken as an example to carry out an example analysis. The results show that the proposed two-stage distributional robust optimization method for distributed photovoltaic clusters can coordinate the economy and robustness of the planning operation scheme. Model control parameters can be flexibly adjusted according to the size and reliability of historical scene sets to meet the different requirements of reliability and economy in various engineering application scenarios.

Bi-Level Robust Stochastic Optimal Configuration Method for Hydrogen Energy Storage System



LIU Mingbo, ZENG Guihua, DONG Ping, et al

2024, 52(9):  12-23.  doi:10.12141/j.issn.1000-565X.240123

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As a clean, pollution-free secondary energy source with high energy density, hydrogen energy is an ideal energy storage carrier for large-scale consumption of new energy. The electric-heat-hydrogen integrated energy system (EHH-IES), which couples hydrogen energy storage system (HESS) and renewable energy, provides new ideas and solutions for the consumption of new energy. Therefore, this paper focused on how to put in hydrogen energy storage equipment in an economically rational way, and aims to solve the problem of reasonable allocation of hydrogen energy storage equipment capacity and consider the impact of source and load uncertainty on the operation of electrothermal hydrogen integrated energy system. This paper proposed a method for optimizing the capacity of HESS in an EHH-IES considering seasonal storage and source-load uncertainty. Aiming at the relatively large prediction error of wind power and high forecasting accuracy of electric, heat and gas loads at first, the uncertain set and sampling scenario were used to elaborate source and load uncertainty, respectively. Then a bi-level robust stochastic optimization model for configuring hydrogen energy storage considering source-load uncertainty and seasonal storage was constructed, where the upper model optimizes the capacities of devices in hydrogen energy storage with the objective of minimizing total cost of annualized investment costs and operating costs, and the lower model was constructed as a two-stage robust stochastic optimization model to simulate the optimal operation scheme of the EHH-IES under the worst scenario of output wind power in typical days. Since the model is difficult to solve directly, particle swarm optimization and column and constraint generation algorithms were used to solve this type of complex model. Finally, through the analysis of case studies of an EHH-IES, the effectiveness of the proposed method was verified. The obtained solution for the optimal configuration of hydrogen energy storage system can promote the consumption of wind power and improve the economics of the system operation.

Optimization Approach of Inter-Regional Electricity Transmission Price Considering the Cooperative Game Strategy of Generators



DONG Ping, HUANG Shanchao, LIU Mingbo

2024, 52(9):  24-34.  doi:10.12141/j.issn.1000-565X.230769

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Due to the uneven distribution of load centers and renewable resources in geographical locations, the reasonable allocation of resources in a wider range requires cross-regional electricity transmission. With the development of inter-regional electricity transmission and the power market, generators participating in the inter-regional market clearing may exercise market power through cooperative game strategies to maximize their own interests, resulting in the reduction of the utilization rate of inter-regional electricity transmission channels and thus influencing the recovery of lines’ permitted income. In response to the above problems, this paper proposed a tri-level model to optimize inter-regional electricity transmission price considering the cooperative game strategy of inter-regional units in the electricity market environment. The upper layer is a transmission electricity price optimization model with the goal of stable recovery of permitted income. The middle layer is a decision-making model of the report volume with the goal of the maximum revenue of the alliance.The lower layer is the regional power market clearing model with the participation of power generators outside the region. An modified C&CG algorithm was implemented to solve the proposed model, decomposing the model into master and sub-problem to be solved in alternating iterations. The master and sub-problems as the two-layer model were solved by converting Carlo-Kuhn-Tucker condition (KKT) and big M method into equivalent monolayer model, while the bilinear terms in the master and sub-problem models were transformed by strong duality theorem and variable discrete parametric method. Finally, PJM-5 node system demonstrates the proposed tri-level optimization model can better reflect the strategic decision and market power behavior of cross-region clearing interest alliance formed by power generators outside the cooperative game strategy, ensuring the formulation of transmission price can realize the stable recovery of the permitted income of the line under the influence of the cooperative game strategy of power producers outside the region.

Mechanical Engineering

Research on Material Flow Behavior of Gear Blank with Deep Spoke During Multi Station Close Die Forging



XIA Qinxiang, PENG Chong, LIU Meihua, et al

2024, 52(9):  35-41.  doi:10.12141/j.issn.1000-565X.230619

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Exploring the material flow behavior during the closed die forging is the theoretical basis for controlling forming defects such as insufficient filling and obtaining high-precision die forgings. In response to the problem of insufficient filling during hot die forging of gear blanks with deep spoke, this study selected the SCr420H gear blank with deep spoke as the research object and designed a multi-station closed hot die forging process of “cutting—heating—upsetting—preforging Ⅰ—preforging Ⅱ—final forging” based on structure analysis of the gear blank. Based on Deform, it established a finite element simulation model for the entire process of multi-station closed hot die forging. The reliability of the finite element model was verified by the experimental results, and the material flow law during the multi-station closed hot die forging process was studied. The results show that a circular cake shaped blank can be obtained when the upsetting ratio is 3.7. The material flow along radial direction is uneven due to the friction effect between the upper and lower surfaces, resulting in a bulge shape at the waist of the billet during upsetting. A concave structure was formed on the bottom surface for positioning during pre-forging station Ⅰ, and it reduced the difficulty of material filling in subsequent stations. The material flow law during pre-forging Ⅱ and final forging are similar. The material mainly flows to the wheel flange part in the early stage of forming. It mainly flows to the wheel hub part in the middle stage, and flows to the rounded corner in the late stage. There is no defect of insufficient material filling during final forging. In the late forming stage, a small amount of metal material flows out of the guide gap of the upper and lower die of the final forging, forming a longitudinal flying edge. Through the production test, the well-filled gear blank with deep spoke was formed. The maximum error between the simulated value and the actual production value is not more than 3.15%, the longitudinal flying edge height is less than 0.5 mm, and the size deviation of each part is less than 0.2 mm, which verifies the rationality of the designed multi-stage closed die forging process.

Experimental Study on the Grinding Process and Adhesion Properties of Aluminum-Magnesium Alloy Abrasive Belts



ZHANG Junrui, FAN Wengang, WU Zhiwei, et al

2024, 52(9):  42-50.  doi:10.12141/j.issn.1000-565X.230558

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Aluminium-magnesium alloy is widely used in various fields due to its good material properties such as light weight and corrosion resistance. In view of the low efficiency of aluminum grinding alloy grinding wheel, the difficult surface quality and the surface adhesion of grinding wheel, this study proposed the use of abrasive belt grinding technology for the processing of aluminium-magnesium alloys. In order to study the law of grinding process of aluminium-magnesium alloy abrasive belt as well as the problem of adhesion characteristics easily produced in the grinding process, the study used 36, 60 two mesh alumina ceramic, silicon carbide, zirconium corundum abrasive belts to carry out experiments on aluminium-magnesium alloy grinding, and analyzed the material removal rate of aluminium-magnesium alloy, the noise of grinding, the energy consumption of grinding, and the rule of change of the abrasive belt material adherence rate under different grinding pressures and the rotational speed of abrasive belt. The results show that: under the same grinding parameters, zirconium corundum abrasive belt has the highest material removal rate and the lowest adhesion rate due to better abrasive toughness, impact resistance and sharpness, but the grinding noise and energy consumption are greater. So in aluminium-magnesium alloy abrasive belt grinding, zirconium corundum abrasive belts can be chosen to improve the grinding efficiency if the influence of noise and energy consumption is not considered. After the grinding pressure of three abrasive belts reaches 20 N, the adhesion rate of the abrasive belt reaches a stable formation stage, and the abrasive chips block the abrasive grain gap, which reduces the material removal efficiency. This conclusion can provide a reference for the selection of grinding pressure parameters for aluminium-magnesium alloy grinding belts. Within the range of process para-meters of 10~30 N and 1 500~3 500 r/min, the grinding pressure and the speed of abrasive belts have a great influence on the material removal rate and adhesion rate. However, the grinding belt speed has a greater effect on grinding noise, and the grinding pressure has a greater effect on grinding energy consumption. The conclusions of the study can provide certain reference for improving the efficiency and quality of aluminium-magnesium alloy belt grinding and reducing the grinding noise and energy consumption.

Loaded Tooth Contact Analysis for Double-Helical Planetary Gear Sets with Floating Components



JIANG Jinke, LI Meng

2024, 52(9):  51-61.  doi:10.12141/j.issn.1000-565X.230649

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Considering coupling characteristics of forces and contact gaps of tooth between planet gear pairs for double-helical planetary gears sets (DHPG) with one set planet gears whose free axial movement is restricted by sun gear and ring gear, this paper proposed a numerical method of loaded tooth contact analysis (LTCA) for DHPG, which provides theoretical references of tooth design and performance analysis for the important application of power split and convergence double helical gears. Firstly, based on the meshing theory, finite element method and optimization method, it established the equations of the deformation coordination, the balance of the meshing total force, and the forces balance of the radial and axial floating components. Secondly, precise geometric and mechanical characteristics of left and right tooth of each inner and outer gear pair were integrated closely,which can better reflect the mutual coupling effect of the distribution force system of gear pairs. Finally, load distribution, bearing deformations, load sharing coefficients and floating displacements of each gear pair were obtained quickly based the LTCA with finite element numerical calculation once.The result show that Radial floating of the sun and ring gear is contributed to load sharing of the planet gears, while axial free floating of planet gear is contributed to load sharing of left and right tooth of planet gears. In case of opposite axial forces on both tooth flank of planet gear without floating, namely overall axial force of the planet gear being greatly offset, the axial floating displacements are a little for planet gear with floating, so the load sharing of left and right tooth is improved a little.Conversely, in case of axial forces from the same direction on both tooth flank of planet gear without floating, the axial floating displacements are much for planet gear with floating, and the load sharing of left and right tooth is improved significantly. Besides, both the amplitude and mean value of loaded transmission are reduced, which are contribute to improving dynamic performances of DHPG with floating components. Furthermore, the geometric transmission error, load sharing coefficient and axial force of the inner gear pair are basically the same as the outer gear pair. It is the key to ensure the free axial movement of the planet gear based on structure to improve the uneven loads sharing between left and right teeth for DHPG.

Simulation and Experiment of the Dynamic Behavior of Slender Truss Boom the During Unloading Rebound



FU Ling, LIU Yang, LIU Yanbin, et al

2024, 52(9):  62-71.  doi:10.12141/j.issn.1000-565X.230523

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The slender truss boom is a key working component of the crane with truss boom, and unloading rebound impact is an important working condition that threatens the safety of slender truss boom crane. To address the dynamic behavior of slender truss booms under unloading impact, this paper used rigid flexible coupling multi-body simulation method and crane unloading impact experimental method to explore the variation law of dynamic stress under unloading rebound conditions of the truss boom, and the unloading impact dynamic load coefficient was calculated based on the dynamic stress of the truss boom. A refined simulation model of a boom tower crane equipped with slender truss booms was established using a rigid flexible coupling method, which includes load model and structural dynamic characteristic model. It analyzed the dynamic stress changes caused by the rebound vibration of the truss boom, and the distribution law of peak dynamic stress during unloading rebound of truss booms was discovered. According to the lifting performance table of cranes with different boom lengths, the relationship between the elevation stress relationship curve and the lifting performance curve was studied, and the sudden unloading condition of the crane corresponding to the maximum stress in the middle of the boom occurred was found. Based on the simulated results of crane unloading impact, a crane unloading impact experiment method based on simulation prediction was established. The sudden unloading impact experiment of the series of boom tower cranes was carried out. The error between the experimental and simulated values of the truss boom dynamic stress is less than 13%, proving that refined model simulation is an effective tool for solving the unloading impact dynamic response of truss boom. Through model simulation, the unloading impact dynamic load coefficient of the slender truss boom under critical situations was further predicted. It finds that there are defects in the relevant regulations on unloading impact dynamic load coefficient in the current crane design specifications. The impact of the truss boom slenderness ratio on the unloading impact dynamic load coefficient was explored, providing a basis for the optimization design of key crane structures.

Design and Analysis of the Active-Passive Hybrid Ankle Prosthesis Based on Electro-Hydraulic Actuation



PANG Hao, SHAN Shaopeng, HAN Yang, et al

2024, 52(9):  72-80.  doi:10.12141/j.issn.1000-565X.230567

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Wearing ankle prosthesis is an important means for patients with below-knee amputations to restore walking ability. The lower limb prostheses are divided into passive prostheses and power prostheses according to whether it can actively output torque. Power prostheses are further divided into active prostheses and active-passive hybrid prostheses. Passive ankle prostheses cannot provide active torque and have limited application scenarios. Powered ankle prostheses can output active torque, but it has the problem of incompatibility between low passive friction and high active transmission ratio. To improve the performance and adaptability of ankle prosthesis, this research proposed a new configuration of active-passive hybrid ankle prosthesis based on the principle of electro-hydraulic actuation from the perspective of practical application. Firstly, based on the analysis of the angle and torque of the human ankle joint, it designed the driving system of the ankle prosthesis and proposed the overall design scheme of the active-passive hybrid ankle prosthesis. Then, the mathematical model of the prosthesis system was established, the rationality of the prosthesis system was verified by the simulation analysis of the hydraulic system of the prosthesis, and the principle prototype of the prosthesis was developed. Finally, the performance of the prosthesis was verified by bench test and human walking experiment. The test results show that the maximum active output torque of the prosthetic ankle joint is 28 N·m when the walking speed is 1.0 m/s (close to the average walking speed of adults). The research results show that the active-passive hybrid ankle prosthesis proposed in this research can realize the active assist function in the human walking process, and can better fit the human ankle movement posture, enhance the wearing adaptability, and further reduce the volume and mass of the prosthetic. The work in this research provides a design idea and reference for the research of dynamic lower limb prosthesis.

Non-Gaussian Modal Parameters Simulation Methods for Uncertainty Structures



PING Menghao, ZHANG Wenhua, TANG Liang

2024, 52(9):  81-92.  doi:10.12141/j.issn.1000-565X.230582

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Structural uncertainty is commonly encountered in practical structural engineering problems. Considering the impact of uncertain factors on modal parameters is of significant importance in enhancing the robustness of structural dynamic analysis. In most developed methods involving the solution or estimation of random modal parameters for linear structures, the modal parameters are usually seen as Gaussian variables, and correlation among them is not getting much attention. However, the Gaussian and independence assumptions of the random mode parameters create simulation errors, affecting the robustness of the structural dynamics response predictions. To address this issue, this study proposed two approaches for simulating random modal parameters of respective discrete and continuous structures. For a discrete structure, its mode shapes are discrete. The random modal parameters are treated as correlated random variables. The correlated polynomial chaos expansion (c-PCE) method was applied to simulate non-Gaussianity and correlation based on the statistics of modal parameters. For continuous structures, random mode shapes are seen as correlated random fields. They can be represented in terms of correlated random variables by using the improved orthogonal series expansion method. Then they were combined with random natural frequencies to constitute a set of correlation variables, which are enabled to be simulated using standard Gaussian variables by utilizing the c-PCE. Finally, taking the truss structure and the plate structure respectively as examples, considering the non-Gaussianism of the modal parameters caused by the fluctuation of material parameters, the proposed random mode parameters can accurately simulate the statistical characteristics of the modal parameters, and further predict the random response of the structure. The simulation results verify the simulation accuracy of the proposed method for the random mode parameters and the necessity to consider the parameter correlations.

Modeling and Regulation of the Turbo-Electric Drive Compressor System for Fuel Cell Stack



ZHAO Rongchao, WANG Zhen, ZHU Zhiyong, et al

2024, 52(9):  93-103.  doi:10.12141/j.issn.1000-565X.230550

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The turbo-electric drive compressor with exhaust energy recovery function is the development trend of high-power fuel cell air management system. However, the turbo-electric drive compressor has problems such as low pressure ratio and low energy recovery rate under off-design conditions. In this paper, a high-power fuel cell system was taken as the research object. According to the intake and exhaust parameters of the stack, the three-dimensional aerodynamic design of the turbine expander and the compressor was completed. An electrochemical-flow heat transfer one-dimensional coupling model including the fuel cell stack and the turbo-electric drive compressor was established. The accuracy of the model was verified by the stack test data. Based on this model, the influence of turbine flow characteristics and valve adjustment methods on the exhaust energy recovery rate under full operating conditions was further studied. The results show that under the condition of small and medium load, the exhaust energy recovery rate increases by 5.26 percentage points for every 0.1 reduction of turbine flow coefficient. However, small flow coefficient will cause high stack pressure at the design point, so it is necessary to increase the pressure relief of bypass valve, which leads to the high complexity of the system and big exhaust energy loss. When the turbine flow coefficient is 1, a better energy recovery rate can be obtained under all working conditions. In addition, the valve preposition scheme is better than the postposition scheme, and the valve preposition can increase the energy recovery rate by 6.25% under off-design conditions. Combined with the scheme of turbine flow coefficient of 1 and valve preposition, the exhaust energy recovery rate of the turbo-electric drive compressor is 33.07% and 27.31% respectively at the design flow point and 50% design flow point.

Traffic & Transportation Engineering

Experimental Study on the Noise and Transmission of Automobile Door Sealing Cavity



ZHANG Binyu, WANG Yigang, YU Wuzhou, et al

2024, 52(9):  104-114.  doi:10.12141/j.issn.1000-565X.240023

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The phenomenon and mechanism of sound generation and transmission of the car door sealing system with wind excitation are complex, and its research is much less.Taking the door primary sealing cavities of the B-pillar and C-pillar and the sealing cavity of the backdoor of a SUV as the research objects, this paper extracted their structures which are equivalent to regular cavity structures. It established a small acoustic wind tunnel with a testing platform and the testing method for the cavity sound generation and transmission, and conducted experiment on the sound characteristics and influencing factors, as well as the sound transmission characteristics of different compression of sealing strips and sealing gaps for the cavities with wind excitation. The results indicate that the mechanism of whistling from the door sealing cavity (backdoor cavity) is different. At low wind speeds, it is generated by coupling resonance between self-sustained oscillation and Helmholtz resonance cavity, while at high wind speeds, it is generated by coupling resonance between self-sustained oscillation and cavity mode, and self-sustained oscillation and cavity resonance with broadband excitation are the essential reasons for other peaks in the spectrum. There are significant sound characteristic differences between B-pillar and C-pillar the cavities and the backdoor cavity due to the sealing strip. The changes in yaw angle, pitch angle, and turbulence intensity of the flow have a significant impact on the amplitude and frequency of self-excited oscillation excitation, which is one of the sources of peak noise with a bandwidth below 1 000 Hz in the car. For small sealing strips compression (such as 0~4 mm) the sound transmission may increase with the increase of compression. The research on the sound phenomenon and mechanism of small cavity in car door gaps based on cavity noise theory is missing from previous studies. The sound transmission conclusion of different compression of the sealing strip has guiding significance for the design of car door seals.

Charging Schedule Optimization of Battery Electric Bus Considering Nonlinear Charging Profile



XIONG Jie, LAI Kefan, LI Tongfei, et al

2024, 52(9):  115-130.  doi:10.12141/j.issn.1000-565X.230524

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Battery electric buses are increasingly applied and promoted in public transportation systems due to their advantages, such as low emissions and low noise levels. However, their limited driving range and long charging time necessitate frequent charging during daily operations, thus leading to a new charging scheduling problem. A reasonable charging schedule is of great significance in reducing the construction cost of charging facilities and charging costs. However, current research on optimizing electric bus charging schedules typically assumes a linear relationship between charging time and state of charge (SOC), and often neglects the comprehensive optimization of charging schedules and charging station operations, resulting in poor scenario reproduction and resource inefficiencies. Therefore, this paper further studied the optimization of charging schedules based on a predefined set of bus trip schedules. A mixed-integer programming model was developed to minimize the total system cost by optimizing the occurrence periods, the start and end times of charging, and the schedules of the charging piles synchronously. The model also fully considers time-of-use electricity pricing policy, partial charging strategies, and the nonlinear characteristics of battery charging. To solve the problem, this paper first linearized the nonlinear charging function of the battery into a piecewise linear one and then used the commercial solver Gurobi to obtain the optimal solution. Additionally, a tailored algorithm was designed based on the minimum-cost-maximum-flow theory and the deficit function. Multiple sets of experiments were conducted to validate the effectiveness of the proposed algorithm based on five bus routes in Beijing. The results, obtained through both Gurobi and the proposed optimized algorithm, demonstrate that the proposed algorithm can achieve a significant reduction in total system costs, ranging from 28.34% to 56.1% across various scenarios. These findings confirm the efficiency of the algorithm and its potential to optimize charging schedules effectively.

Classification and Identification of Risky Driving Behavior Based on Hybrid Strategy Improved ASO-LSSVM



HE Qingling, PEI Yulong, DONG Chuntong, et al

2024, 52(9):  131-141.  doi:10.12141/j.issn.1000-565X.230753

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This paper aims to solve the problem of slow convergence rate and large error of existing intelligent algorithms in the process of optimizing support vector machine to identify risky driving behavior. Firstly, Tent mapping was used to replace the random setting of population initialization of ASO algorithm to increase the diversity and quality of atomic population. Secondly, the hybrid mechanism of dimension-by-dimension pinhole imaging reverse learning and Cauchy mutation was used to improve the diversity of preferred positions of atomic individuals and overcome the problem that ASO algorithm is easy to fall into local optimum and premature convergence. Finally, the adaptive variable spiral search strategy was introduced to improve the atomic individual position update process, so as to improve the global search ability of ASO algorithm, realize the effective balance between global search and local development, and alleviate the problem that ASO algorithm is easy to fall into local optimum and lack of convergence accuracy. Taking the vehicle trajectory data of the exit ramp of Shanghai North Cross Channel as the input, the study used the hybrid strategy to improve the ASO algorithm so as to optimize the LSSVM parameters. And it constructed the classification and identification model of the risk driving behavior of the expressway exit ramp based on IASO-LSSVM. Numerical simulation results show that the average value, standard deviation, best fitness and worst fitness of the numerical simulation results of the IASO algorithm in 12 benchmark test functions are closer to the best optimization value. Compared with ASO-LSSVM and LSSVM, the accuracy, precision, recall and F₁ value of risk driving behavior classification and identification results of IASO-LSSVM model increased by 11.5~24.5, 14.1~29.0,15.1~28.6, 14.7~31.2 percentage points respectively, and the error range was the smallest in different types of risky driving behavior identification results. The accuracy and convergence rate of IASO algorithm are better than those of ASO algorithm, and the IASO-LSSVM model can be used for accurate identification of different types of risk driving behavior, which can provide data support and theoretical basis for reasonable discrimination of vehicle driving trajectory state and formulation of early warning and prevention measures of risk driving behavior.

Study on Evaluation and Influencing Factors of Cognitive Driving Ability in Elderly Drivers



CHEN Bingshuo, LI Yang, ZHAO Xiaohua, et al

2024, 52(9):  142-152.  doi:10.12141/j.issn.1000-565X.230579

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The number of elderly drivers in China continues to grow, and the changes in the driver structure pose challenges to traffic safety. Compared to drivers in other age groups, elderly drivers’ psychological function gradually declines and they are more prone to traffic accidents. Cognitive function is significantly correlated with driving safety performance. Based on the driving characteristics of elderly people, this article started from three cognitive functional areas of attention response ability, executive processing ability, and spatial perception ability, and designed driving simulation experiments to obtain cognitive driving behavior data. It analyzed the differences in driving behavior characteristics among young people, middle-aged people, and elderly people. By combining subjective and objective methods to determine indicator, weights, a method for calculating the cognitive driving behavior index was proposed. A generalized linear mixed model was established with driver attributes and cognitive function as independent variables and cognitive driving behavior index as dependent variable to explore the impact of different factors on cognitive driving ability. The results showed that age, weekly driving frequency, self-regulation, and TMT-B (Trail Making Test-B) were significantly correlated with cognitive driving behavior index, with MMSE (Mini-mental State Examination) showing marginal significant correlation. The cognitive driving behavior index of elderly drivers was greatly influenced by individual traits. Compared to the elderly, the cognitive driving behavior index of young people was worse, while that of middle-aged people was better. People with lower weekly driving frequency had better cognitive driving behavior index than those with higher weekly driving frequency. Drivers with low and medium self-regulation frequencies have better cognitive driving behavior indices than those with high self-regulation frequencies. TMT-B measurement showed that the cognitive driving behavior index of drivers with normal cognition was better than those with cognitive impairment. Starting from the perspective of human factors in traffic accidents, this study explored the cognitive challenges faced by elderly drivers, proposed a calculation method for the cognitive driving behavior index of elderly people, and analyzed the influencing factors, providing reference for simplifying the evaluation process of elderly driving suitability and formulating driving safety intervention strategies.

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2024, 52(9):  153. 

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