<div>Engine performance is affected by cooling airflow onto the engine cooling module. During initial design, frontal openings, grills, cooling module size, placement, and location are optimized to ensure sufficient airflow onto the cooling module. Currently, design concepts are validated using 3D computational fluid dynamics (CFD) simulations performed iteratively on full vehicle models to predict and optimize cooling airflow onto cooling modules. Each design concept iteration consumes significant time and resources. This study introduces a machine learning (ML) model to streamline underhood airflow prediction, reducing reliance on iterative CFD. Previous CFD simulation data is used to create a training dataset, which calibrates the ML model, describing underhood airflow as a function of input parameters. The relevant ML algorithm is used to calibrate the model, perform data fitting of the training values, after which a testing dataset is created to validate the model for a range of design parameters and vehicle conditions. Upon achieving the target testing accuracy (90% accuracy target in this particular case), the ML model is ready for implementation. The ML model is used to predict initial estimates of airflow and refine the design iterations, while CFD simulations are performed for the finalized concepts. This eliminates the need for expensive and lengthy design analysis iteration loops, effectively replacing them with a highly flexible model capable of predicting underhood airflow for even minor design changes quickly. Use of this model can decrease the time required per iteration by more than 90% compared to conventional CFD, thus enabling analysis of more designs in a given time frame.</div>

<div>A DRL (deep reinforcement learning) algorithm, DDPG (deep deterministic policy gradient), is proposed to address the problems of slow response speed and nonlinear feature of electro-hydrostatic actuator (EHA), a new type of actuation method for active suspension. The model-free RL (reinforcement learning) and the flexibility of optimizing general reward functions are combined with the ability of neural networks to deal with complex temporal problems through the introduction of a new framework called “actor-critic”. A EHA active suspension model is developed and incorporated into a 7-degrees-of-freedom dynamics model of the vehicle, with a reward function consisting of the vehicle dynamics parameters and the EHA pump–valve control signals. The simulation results show that the strategy proposed in this article can be highly adapted to the nonlinear hydraulic system. Compared with iLQR (iterative linear quadratic regulator), DDPG controller exhibits better control performance, achieves the EHA control objective at faster speed, and notably improves the ride comfort and handling stability of the car. Moreover, DDPG’s optimized valve–pump joint control strategy can reduce the energy consumption of the EHA system and improve the life of the hydraulic components while ensuring the control accuracy, solving the problem of low reliability of the active suspension system.</div>

<div>Hydroplaning contributes to approximately 20% of traffic accidents during adverse weather conditions, with factors such as velocity, water film thickness, tire inflation, and vehicle weight playing significant roles. This study aims to simulate the hydroplaning phenomenon using a fluid–structure interaction model based on the coupled Eulerian–Lagrangian (CEL) capabilities of ABAQUS. Results reveal that vehicle linear velocity is a key determinant of hydroplaning risk, with a positive correlation observed. The findings suggest maintaining speeds under 50 km/h to mitigate hydroplaning risk, contingent on well-maintained, properly inflated tires. Multiple linear regression analysis further demonstrates correlations among velocity, tire inflation, quarter vehicle load, and water film thickness in predicting the reaction force between the tire and roadway. The proposed scheme provides a predictive mechanism for hydroplaning risk under varying conditions, offering valuable insights into prevention strategies.</div> <div>The proposed scheme offers a valuable predictive mechanism for understanding and mitigating hydroplaning risk by analyzing key environmental and vehicle parameters. It identifies the critical factors influencing hydroplaning, including velocity, tire inflation, water film thickness, and vehicle load, while offering actionable insights to reduce risk. By employing advanced simulation techniques, specifically ABAQUS with CEL capabilities, the model provides a realistic and accurate representation of the hydroplaning phenomenon. Furthermore, the correlation analysis offers a comprehensive understanding of the relationship between multiple variables, enabling risk assessment under varying conditions. This approach not only highlights the underlying physics of hydroplaning but also supports evidence-based strategies for risk reduction and improved vehicle safety.</div>

<div>This article is mainly to present a deep learning–based framework for predicting the dynamic performance of suspension systems for multi-axle vehicles, which emphasizes the integration of machine learning with traditional vehicle dynamics modeling. A multitask deep belief network deep neural network (MTL-DBN-DNN) was developed to capture the relationships between key vehicle parameters and suspension performance. Numerical simulation–generated data were utilized to train the model. This model also showed better prediction accuracy and computational speed compared to traditional deep neural network (DNN) models. Full sensitivity analysis has been performed in order to understand how different vehicle and suspension parameters may affect suspension dynamic performance. Furthermore, we introduce the suspension dynamic performance index (SDPI) in order to measure and quantify overall suspension performance and the effectiveness of multiple parameters. The findings highlight the effectiveness of multitask learning in improving predictive models for complex vehicle systems. By using SDPI and MTL-DBN-DNN model for optimization, the optimized parameters resulted in a significant improvement in suspension dynamic performance.</div>

<div>This computational fluid dynamics (CFD) study examines the comfort parameters of an innovative air vent concept for car cabin interiors using a reduced order model (ROM) and proper orthogonal decomposition (POD). The focus is on the analysis of the influence of geometric and fluid mechanical parameters on the resulting jet, in particular on the deflection angle of the airflow and the total pressure difference along the outlet geometry. Different parameters of the investigated system, such as the surface orientation, the outlet height, the separator distance, and the separator height, lead to different effects on the airflow structure. The results show that changes in the air vent surface orientation are always accompanied by an increase in the deflection angle and the total pressure difference. In contrast, the variation of the outlet height ratio positively influences the deflection angle and the total pressure difference in terms of the requirements for air vent geometries. The study also examines the interaction of the geometric parameters and reveals complex correlations that influence the resulting air jet. A comprehensive understanding of these influences makes it possible to adapt the design and implementation of new and innovative air vent concepts to meet specific requirements. By balancing design considerations and technical requirements, optimized solutions are characterized by a high deflection angle and a reduced overall pressure difference for improved system performance and efficiency. Therefore, this evaluation provides a final framework for the design and implementation of an innovative air vent concept based on the volume flow vectoring that is tailored to specific application requirements.</div>

<div>In this article, a finite element analysis for the passenger car tire size 235/55R19 is performed to investigate the effect of temperature-dependent properties of the tire tread compound on the tire–road interaction characteristics for four seasons (all-season, winter, summer, and fall). The rubber-like parts of the tire were modeled using the hyperelastic Mooney–Rivlin material model and were meshed with the three-dimensional hybrid solid elements. The road is modeled using the rigid body dry hard surface and the contact between the tire and road is modeled using the non-symmetric node-to-segment contact with edge treatment. At first, the tire was verified based on the tire manufacturer’s data using numerical finite element analysis based on the static and dynamic domains. Then, the finite element analysis for the rolling resistance analysis was performed at three different longitudinal velocities (10 km/h, 40 km/h, and 80 km/h) under nominal loading conditions. Second, the steady-state traction analysis with the corresponding angular velocities of the mentioned longitudinal velocities range was carried out. In addition, a series of transient traction analyses were performed under 40 rad/s angular velocity (corresponding with the 50 km/h longitudinal velocity). The results show that the temperature plays a key role in the final value of the rolling resistance coefficient. Moreover, the longitudinal stiffness of the tire during the traction performance was investigated based on the various ambient temperatures, and it was observed that tire traction is very sensitive to the temperature-dependent properties of the tread compound.</div>

<div>The effectiveness of the negative suspension structure (NSS) in isolating the driver’s seat vibrations has been demonstrated based on the seat’s model or vehicle’s one-dimensional dynamic model. To fully assess the effectiveness and stability of the seat’s NSS (S-NSS) on different models of vehicles, the three-dimensional models of the vibratory rollers (VR), heavy trucks (HT), and passenger cars (PC) have been built to assess the effectiveness of S-NSS compared to the seat’s passive suspension (S-PC) and seat’s control suspension (S-CS). The effectiveness of S-NSS is then investigated under all operating conditions of vehicles. The investigation results indicate that under a same simulation condition, S-NSS improves the ride comfort and health of the driver better than both S-PS and S-CS on all VR, HT, and PC. However, the effectiveness of S-NSS on PC is lower than on both VR and HT while the effectiveness of S-CS on PC is better than on both VR and HT. Besides, the effectiveness of S-NSS with VR moving on the poor class of the ground surface is better than on the good class of the ground surface. In addition, under the change of the velocity and seat mass, the effectiveness of S-NSS on VR is not only higher than that on HT and PC but also very stable, conversely, the effectiveness of S-CS on PC is better than that on VR and HT. These results imply that S-NSS should be applied on the seat suspension of VR, HT, and PC to improve the comfort and health of the driver, especially on VR, while S-CS should be applied to PC to achieve its best isolation effectiveness.</div>

<div>This article presents a height control method for air suspension systems, which are influenced by strong nonlinearity and multiple coupling factors, based on model-free adaptive control (MFAC) using full-form dynamic linearization (FFDL). To address the impact of different damping coefficients of the shock absorber on the height control effect, an improved genetic algorithm is employed to globally optimize the relevant parameters involved in the design of the control law, thereby enhancing the height control performance. The precision of modeling the air suspension system has a direct impact on the simulation of both static and dynamic vehicle models, as well as the accuracy of height control. In this article, an equivalent thermodynamic model of the air suspension system is established based on the principle of energy conservation for height control research. Considering the nonlinearity of the air suspension system and the need to make additional assumptions before modeling, a MFAC method using FFDL is adopted for controller design. Traditional height control methods do not consider the impact of changes in the shock absorber damping coefficient on the height control effect. For different damping coefficients, the body height tracking error is large when using the same height control law initialization parameters. Therefore, an improved genetic algorithm is employed to globally optimize the MFAC parameters under different damping states. The effectiveness of the thermodynamic model of the air suspension system and the MFAC method for height control, with parameters tuned using the improved genetic algorithm, was validated through MATLAB/Simulink simulations.</div>

<div>The comfort of seats increasingly becomes a crucial factor in the overall driving experience, particularly as vehicles become increasingly integrated into people’s daily lives. Passengers often maintain a relatively fixed posture and have close contact with the seat for extended periods of time, leading to issues such as heat, humidity, and stickiness. In order to enhance the thermal comfort experienced by occupants, manufacturers are no longer satisfied with ensuring the thermal comfort performance of vehicles only through the HVAC system in the cabin, but also developed a microclimate control seat that adjusts the temperature through ventilation between the contact surface of the seat and the human body, trying to improve the thermal comfort of passengers more effectively. However, the ventilation ducts of these seats are commonly designed based on empirical or autonomous standards, and their effectiveness is subsequently assessed through test or simulation, typically under unloaded conditions. This approach fails to account for the impact of seat deformation on ventilation performance during actual use, resulting in a discrepancy between the intended design and the actual experience. This research aims to address this issue by using simulation methods to compare the deformation of ventilation ducts and their impact on ventilation performance in both unloaded and loaded seats. The findings reveal significant differences between the two conditions, highlighting the importance of considering seat deformation in the design of more precise microclimate control. Meanwhile, a simple simulation scheme was proposed for performance testing of seat ventilation.</div>

<div>Safety concerns surrounding new energy vehicles have gained increasing national and social attention. Bottom impacts to power batteries are a leading cause of fires and explosions in new energy vehicles. Focusing on the safety of power battery bottom impacts, this article first proposes applying honeycomb panels to the battery’s bottom guard plate. Through the ball impact test, the effect of honeycomb panel surface material thickness on bottom protection is studied, and the mechanism of the honeycomb panel’s ball impact protection is explored. Second, the honeycomb panel and the aluminum alloy plate are structurally compounded to improve the ball impact protection ability. Finally, the optimized composite bottom guard plate is assembled on the lower box of the power battery, and the whole package ball impact experiment is successfully verified. This study serves as a reference for future research on power battery bottom impact protection and the industrial application of bottom guard plates.</div>