To effectively control the thermal management system of a dualstack fuel cell system, this paper establishes a fuel cell stack model based on test data, and couples it with key component models such as pumps and heat exchangers to form a complete thermal management system model. To stabilize the stack temperature under disturbances, a control strategy combining proportionalintegral (PI) with linear active disturbance rejection control (LADRC) was proposed and verified by simulation. Finally, an improved control strategy based on feedforward decoupling and cascade LADRC was proposed. The control effects before and after the improvement were compared through simulation. The results show that the improved control strategy can effectively reduce overshoot, and the integral time absolute error (ITAE), reflecting temperature control accuracy, can be reduced by up to 66.39%.

The paper reviews the formation mechanisms and detection methods of flooding and drying faults in proton exchange membrane fuel cells (PEMFCs). Firstly, the causes of flooding and drying faults are analyzed from the perspectives of structure, internal gas transport mechanism and water transport mechanism. Then, the fault detection methods based on modeling, datadriven approaches and direct imaging visualization are comprehensively investigated, and the technical characteristics of these diagnostic methods are deeply analyzed. Finally, considering the limitations and shortcomings of various diagnostic methods, the future improvement and development directions for diagnosing flooding and drying faults in PEMFCs are discussed. This includes utilizing technologies such as miniaturization and portability of electrochemical impedance spectroscopy (EIS) analysis equipment, improving battery model parameters and sharing battery fault big data.

To effectively reduce the noise of the fuel cell centrifugal air compressor system, a perforated muffler capable of broadband noise reduction was designed. Using the method of computational fluid dynamics coupled with computational aerodynamic acoustics, the noise reduction effect of the perforated muffler was analyzed under different operating conditions of the compressor. Additionally, the thermoacoustic transformation relationship inside the muffler was quantified. The results show that the cavity thickness and perforation rate of the perforated muffler play a decisive role in absorbing highfrequency sound wave components. Compared with the lowfrequency sound waves, the perforated muffler is more effective at attenuating highfrequency sound wave components. As the rotational speed increases, the muffling effect on the highfrequency components gradually enhances, while the effect on the lowfrequency components remains almost unchanged. The thermalacoustic conversion analysis of the perforated muffler shows that under lowspeed operation conditions, the energy of acoustic oscillation before and after muffling is almost completely converted into the exergy of the air. In contrast, under medium and highspeed operating conditions, the proportion of acoustic oscillation energy converted into air exergy is relatively small. To design a muffler that can achieve broadband noise reduction under various operating conditions, the attenuation of lowfrequency sound waves at high rotational speeds should be the primary optimization target. The improvement of thermodynamic performance before and after muffling at low rotational speeds should also be considered. The work presented in this paper provides a new method for reducing the aerodynamic noise of centrifugal air compressors, and offers a theoretical basis for designing highefficiency air compressor mufflers with wide working condition adaptability.

Taking a fuel cell electric light truck as the research object, a simulation model of the power system was built using Matlab/Simulink, in order to improve the economic efficiency and durability of the fuel cell system through the optimization of its energy management strategy. Based on the foundational fuzzy control, an improved fuzzy control strategy was developed to restrict the rate of change in the fuel cell output power. The performance of this strategy was compared with the finite state machine control strategy and the original fuzzy control to validate the simulation. The simulation results show that, under the NEDC and UDDS cycle conditions, the hydrogen consumption of the improved fuzzy control strategy is reduced by 5.65% and 8.29% respectively compared to the original strategy. Compared with the finite state machine control strategy, the improved fuzzy control strategy yields a reduction in hydrogen consumption by 16.63% and 10.64% with smaller fluctuations in the fuel cell output power, which results in a more stable performance and enhanced economic efficiency and durability of the fuel cell system.

Focusing on the hydrogen ejector used in fuel cells, a CFD simulation model was established to study the influence of structural parameters, such as the nozzle throat diameter D, the nozzle angle ø and the mixing chamber diameter D, on the ejector's performance. The results show that the influence of structural parameters on the ejector's performance varies across different power levels of the fuel cell stack. In the lowpower range, the entrainment ratio significantly increases with the nozzle angle, while in the highpower range, the entrainment ratio decreases as the nozzle angle increases. The influence of the mixing chamber diameter on the ejector's performance is opposite. In the lowpower range, the entrainment ratio decreases as the mixing chamber diameter increases, while in the highpower range, the entrainment ratio increases with the mixing chamber diameter. Based on the influence patterns, the design method for key structural parameters of the ejector was developed, and the optimal parameter range was obtained.

The biological fidelity of the advanced Pedestrian Legform Impactor(aPLI) in crash testing largely depends on its geometric structure and the hyperelastic mechanical properties of synthetic rubber used to simulate leg muscle. Based on the quasistatic uniaxial compression test data of rubber, both Ogden and Mooney Rivlin constitutive models are fitted to characterize the hyperelastic behavior of rubber. Following this, the material parameters are obtained and fitting curves are compared with the experimental curves to assess the accuracy of different constitutive models. The results show that the outcomes of the secondorder Ogden model better match the experimental data. To increase the accuracy of muscle rubber material parameters in the finite element model, a compression test finite element model is reconstructed. Taking the material parameters of the fitted secondorder Ogden constitutive model as the initial values, an optimization of the material parameters, μ₁, α₁, μ2 and a₂ in the model is performed using the adaptive response surface method combined with finite element analysis and optimization strategies. This yields a set of optimal material parameters for the material under quasistatic compression.

Automated Valet Parking (AVP) system is a comprehensive platform integrating intelligent driving environment perception, decision planning and motion control technologies. Trajectory planning is directly related to the efficiency, energy consumption, safety and comfort of the valet parking process. To outline the development status of autonomous parking trajectory planning technology, this paper first reviews the development history of parking technology, then investigates trajectory planning during parking, and analyzes the progress in AVP research. Recognizing that the transition from singlevehicle intelligence to multivehicle cooperation reveals greater potential for system optimization, this study subsequently outlines the fundamental methods and current research status of multivehicle cooperative trajectory planning, with a special focus on cooperative planning in parking scenarios. Finally, this paper analyzes existing issues and future development trends in AVP trajectory planning.

Aiming at the current industry issue of inadequate testing and evaluation indexes for the driving experience of intelligent parking assist system users, the paper proposes both subjective and objective evaluation indexes for driving experience. These indexes are experimentally validated and analyzed for correlation. Firstly, based on the functional logic of the intelligent parking assist system, a driving experience closedloop control system is established. Subsequently, combined with the closedloop system, the subjective evaluation index system is constructed using the experience ladder pyramid model. Then, the objective indexes are developed by using the GSM model. Finally, realvehicle tests were conducted on seven car models and analysis was performed using Pearson correlation coefficients. The test results show that the proposed evaluation indexes are suitable for assessing the driving experience, with all subjective and objective correlation coefficients above 0.5, which provides guidance for the design and evaluation of intelligent parking assist systems.

In order to promote the development of autonomous vehicle applications, conducting accurate and reliable safety testing and evaluation is essential. This paper proposes a safety evaluation method for autonomous vehicles tailored to highspeed ramp traffic scenarios using natural driving data. By analyzing the conflict characteristics in the confluence area, the models for calculating traffic conflict indicators such as TTC, PET and MSS are established to determine the safety evaluation indicators. The fuzzy clustering of natural driving indicator data is used to obtain the threshold ranges for these indicators. The autonomous vehicle simulation test has been built. The importance criterion weight distribution method based on interlayer correlation and the gray correlation scoring model are applied. The comprehensive evaluation scores regarding the safety of autonomous vehicles are calculated under different control algorithms. The results show a distinct correlation in the distribution of safety indices between the test vehicle's driving behavior and ideal driving behavior. By calculating the overall correlation degree, the scores can directly reflect the comprehensive safety performance of different autonomous driving systems.

The widespread adoption of electric vehicles has raised higher demands for the technology related to power batteries. Consequently, a thermal management system that keeps the battery within an optimal temperature range has become a core technical requirement for major manufacturers. In recent years, the focus has shifted towards lowtemperature thermal management technology, driven by the performance degradation and life decay of lithiumion batteries in winter's cold conditions. Based on the degradation mechanism of lithiumion batteries in cold conditions, the paper provides a comprehensive overview of the development status of lowtemperature thermal management systems. Additionally, in conjunction with the latest research progress, it summarizes a set of evaluation methods for lowtemperature thermal management of electric vehicles.