Crash Safety Research Articles

Transfer learning for crash design

When designing the structure of a new vehicle, car manufacturers need to ensure the compliance with strict safety requirements. Aiming to support the engineers in the early phase of this process, we propose a transfer learning framework for crashworthiness. This work explores the possibility to infer knowledge on future situations by exploiting data coming from past development processes. During the early phases of automotive development, assessing the crash safety implies dealing with the challenge of low data availability. Here, the engineers have no hardware test to rely on and can access only few finite element simulations. Under these circumstances, an attractive concept to investigate is the development of a machine learning approach able to learn from the past designs and to transfer the acquired knowledge to the new ones. Transfer learning can serve to this aim. With it, one learns the basic knowledge from a source domain A, and transfers it to a target domain B, characterized by low data availability. Here, we propose a transfer learning framework and apply it to an explicatory industrial crash example. The components produced in the past constitute the source domain; the new component design is the target domain. The proposed methodology can serve as an innovative solution to support car manufacturers in the early phase of vehicle development and thus improve the performance in crashworthiness scenarios.

Axial Crushing and Energy Absorption Integrated Design of Modular Filled Double-Hat Beam Composite Structures.

In order to study the influence of modular filled and composite material forms on the axial crushing and energy absorption properties of structures, modular filled composite structures were constructed, and innovatively applied to the inner side of double-hat beam (DHB) structures in automobiles. The modular filled structures comprise hexagonal, quadrilateral, and triangular sections. By analyzing the collision performance of modular filled DHB structures, significant enhancements were observed in both the sectional characteristics and the specific Mean Crushing Force of modular filled DHBs compared to the conventional double-hat beam structure. These advancements notably improved the plastic deformation characteristics of the structures. Additionally, dynamic weightlessness experiments were conducted to validate the accuracy of the simulation model. Among the proposed schemes, namely QU-5, HE-5, and TR-5, notable improvements in crashworthiness were identified. Specifically, crashworthiness indicators increased by 32.54%, 78.9%, and 116.53%. Compared with other thin-walled structures, modular filled composite DHBs have advantages in axial crushing and energy absorption. By optimizing layout characteristics, the modular filled structures will achieve significant lightweight and energy absorption performance improvements. This work has clear reference value for automotive engineers and scholars to further explore the axial crash safety, platform modularization, and lightweight design of vehicles.

Optimization of crashworthiness design of foam-filled crash boxes under oblique loading for electric vehicles

To increase the energy-absorbing capability of frontal collision management systems and improve vehicle crash safety, foam-filled crash boxes should be optimized. On the basis of a double tubular construction, a novel foam-filled crash box with a different design is developed. The energy absorption capacity, initial peak force, and deformation modes of the original and improved crash boxes were examined using impact models. As opposed to the full-filling design, it is demonstrated that the filling design may utilize less foam while increasing specific energy absorption. The stability of continuing deformation after the first buckling is determined by the foam-filled crash box. For the foam-filled crash box, a better-optimized design technique is suggested using the Taguchi method and principal component analysis (PCA). Compression tests are used to validate the design concept. Therefore, the optimal design technique of the crash box is suitable and practical for the crashworthiness design of crash boxes, considering the combined effect of significant indicators for electric vehicles.

Coupled Multiphysics Modeling of Lithium-Ion Batteries for Automotive Crashworthiness Applications

Abstract Considerable advances have been made in battery safety models, but achieving predictive accuracy across a wide range of conditions continues to be challenging. Interactions between dynamically evolving mechanical, electrical, and thermal state variables make model prediction difficult during mechanical abuse scenarios. In this study, we develop a physics-based modeling approach that allows for choosing between different mechanical and electrochemical models depending on the required level of analysis. We demonstrate the use of this approach to connect cell-level abuse response to electrode-level and particle-level transport phenomena. A pseudo-two-dimensional model and simplified single-particle models are calibrated to electrical–thermal cycling data and applied to mechanically induced short-circuit scenarios to understand how the choice of electrochemical model affects the model prediction under abuse scenarios. These models are implemented using user-defined subroutines on ls-dyna finite element software and can be coupled with existing automotive crash safety models.

Crashworthiness analysis and optimization design of special-shaped thin-walled tubes by experiments and numerical simulation

Thin-walled structures are widely used as typical high-efficiency energy-absorbing structures in crash safety protection. This paper aims to combine the characteristic of the square tube which concentrates energy dissipation through angle elements with the stable fold deformation of the circular tube, and innovatively propose a special-shaped thin-walled tube that contains both angle elements and arc elements. The failure behavior and energy absorption characteristics of the special-shaped tube were investigated by compression test and numerical simulation. The simulation results showed that the special-shaped thin-walled tubes have a significant advantage in energy absorption, and the specific energy absorption of the special-shaped tubes is 31.57% and 75.2% higher than that of the equal-mass circular and square tubes, respectively. Next, through finite element analysis, the effects of various parameters and initial imperfections on the crashworthiness of the special-shaped tubes were compared. The results indicated that adjustments to geometrical parameters significantly impact the energy absorption properties and crushing behavior of thin-walled tubes. Moreover, the tube with induced circular holes exhibited an optimal deformation mode. Further, a multi-objective optimal design of the metal special-shaped tube was carried out to obtain the best parameter configuration of the metal special-shaped tube. Finally, the special-shaped structure was extended to CFRP/AL hybrid thin-walled tubes. The failure behavior and energy absorption enhancement mechanism of the hybrid special-shaped tubes were investigated with experiments, and the absorbed energy of the hybrid tube was about 20% higher than the sum of the absorbed energy of the single constituent tubes. The effect of the CFRP lay-up pattern on the crashworthiness of the hybrid tube was investigated by numerical simulation, and it was found that the energy absorption capacity of the hybrid tube could be improved by appropriately increasing the number of layers and the proportion of axial fibers.

High–speed train crash safety assessment for Train–moose collisions

High–speed train crash safety assessment for Train–moose collisions

Rate-dependent damage and failure behavior of lithium-ion battery electrodes

The mechanical properties and failure behavior of composite electrodes are critical to understanding the internal short circuits and ensuring the crush safety of lithium-ion batteries used in electric transportation. This study provides a comprehensive experimental investigation into the strain rate–dependent tensile/compressive behavior and failure mechanism of both anode and cathode, covering a range from quasi-static to dynamic conditions. A universal testing machine equipped with an industrial camera was used to assess the mechanical properties and observe the deformation process of the electrodes under quasi-static conditions. Additionally, a split Hopkinson bar system, coupled with a high-speed camera, was utilized to evaluate the mechanical properties and capture the deformation process of the electrodes under dynamic loading. The detailed deformation and failure modes of the electrodes were revealed by a combination of post-mortem characterization and images from high-speed cameras. The experimental results demonstrate a significant strain rate effect on the tensile and compressive mechanical behavior and failure mechanism of both anode and cathode. The rate dependence of tensile/compressive strength and failure strain were discussed, and the underlying mechanism for strain rate sensitivity was analyzed. The results and conclusions from this study provide an important foundation for the detailed modeling and failure prediction of lithium-ion batteries under impact loading. Further, these findings facilitate the safety design of electric vehicles by informing to enhance crash safety.

Study on Integrated Evaluation and Development Strategy of Pedestrian Protection in 2024 Edition of C-NCAP

This paper discusses the comprehensive assessment of pedestrian protection in the 2024 edition of China-New Car Assessment Program (C-NCAP) and its development strategies. The assessment plan has been adjusted according to the actual conditions of Chinese road traffic and vehicle characteristics, emphasizing comprehensive testing of vehicle safety performance. Improvement in structural crash safety significantly enhances a vehicle's ability to protect occupants in collision accidents and reduces injury risks. This article particularly focuses on pedestrian protection assessment and proposes a series of development strategies by integrating AEB VRU system testing with head and leg testing. These strategies aim to further enhance the level of pedestrian protection provided by vehicles and contribute to overall traffic safety improvement.

A modelling approach for laser welding of busbars to lithium-ion prismatic cell terminals to enhance failure prediction

Laser welding is increasingly being used to connect automotive battery packs and weld busbars to prismatic cell terminals due to its ability to weld a wide variety of materials at high speed. The choice of material for the connecting busbars is primarily based on weldability, weight, electrical/thermal conductivity and cost. Aluminum (Al) is increasingly being chosen as busbar material to meet these criteria. This paper investigates laser overlap welding of Al for producing busbar-to-terminal interconnects for prismatic cell assembly. To understand the strength of these laser-welded joints in the early stages of design, the joints must be modelled. In addition, the structural modelling of battery module connections is critical to understanding the impact resistance (crash safety) of electric vehicles. Fusion-based modelling of laser welding is computationally inefficient and difficult to integrate into large structural models e.g., full vehicle crash models. Instead, it is more computationally efficient to generate a structural model of the weld, tuned to experimental test data, which can be easily integrated into larger models. LS-DYNA was employed to model the mechanical behavior of 1 mm to 1 mm AA1050 laser welds. Welding was performed using a wobble head integrated with a 1 kW CW fiber laser system. The simulation is based on laser weld with a rectangular pattern and was developed from experimental data from lap shear, T-peel, cross-tension and torsion tests. This modelling approach was extended to obtain joint strength for laser welding a 1.5 mm 1060 Al busbar to a 4 mm 1060 Al prismatic cell terminal. The predicted load–displacement curves closely matched the test data.

Extensive hypothesis testing for estimation of crash frequency models

Estimating crash data count models poses a significant challenge which requires extensive knowledge, experience, and meticulous hypothesis testing to capture underlying trends. Simultaneous consideration of multiple modelling aspects is required including, among others, functional forms, likely contributing factors, and unobserved heterogeneity. However, model development, frequently affected by time and knowledge, can easily overlook crucial modelling aspects such as identification of likely contributing factors, necessary transformations, and distributional assumptions. To facilitate model development and an estimation that can extract as many insights as possible, an optimization framework is proposed to generate and simultaneously test a diverse array of hypothesis. The framework comprises a mathematical programming formulation and three alternative solution algorithms. The objective function involves minimizing the Bayesian Information Criterion (BIC) to avoid overfitting. The solution algorithms include metaheuristics to deal with an NP-hard problem and search through a complex and nonconvex space. The metaheuristics also enable to handle unique datasets through varying search strategies. The effectiveness of the proposed framework was ascertained using three distinct datasets, and published models used as benchmarks. The results highlighted the ability of the proposed framework to estimate crash data count models, surpassing benchmark models in terms of insights and goodness-of-fit. The framework provides several advantages, such as robust hypothesis testing, uncovering unique specifications and vital insights in the data, and leveraging existing knowledge to enhance search efficiency. The framework also exposes the vulnerability of traditional analyst efforts to fall into local optima, bias, and limitations in creating more efficient models. In a compelling example using crash data from Washington, the proposed framework unveiled insights overlooked by a benchmark published model, identifying speed, interchanges, and grade breaks as likely crash contributors, and revealing the potential danger of excessively wide shoulders. Conversely, the benchmark model identified fewer contributing factors and missed a crucial non-linear relationship between crash safety and shoulder widths. While wider shoulders are typically associated with improved safety, the proposed models suggest a safety threshold beyond which further widening could decrease safety. The introduction of random parameters in the analysis revealed a more nuanced relationship with crash frequency, thereby underlining the limitations of models incapable of capturing heterogeneity.

Injury Patterns in Vehicle Crashes: The Significance of Occupant Seating Position.

Investigating patterns among the outcomes of patients involved in motor vehicle accidents (MVAs) can provide information necessary to guide targeted interventions to improve road traffic safety. Our purpose is to identify any differences between passenger and driver injury severity and overall clinical course after MVAs. We performed a retrospective review and analysis of 3,693 patients involved in MVAs from 2016 to 2021. We divided the data into two groups, drivers and passengers, and compared the Injury Severity Score (ISS),Revised Trauma Score (RTS) on admission, days in the Intensive Care Unit (ICU), length of hospital stay (LOS), post Emergency Department (ED) disposition, discharge (DC) disposition, and signs of life on arrival (SOLA) to the ED. We compared mean ISS, New Injury Severity Score (NISS), RTS, length in ICU and LOS using a student's T-test and SOLA, post-ED and DC disposition using Chi-squareanalysis. We did not find any statistically significant difference in ISS, RTS, days in ICU, LOS, or SOLA between the drivers and passengers. However, we did find a statistically significant difference in the post-ED (X2= 113.743, p=<0.0001) and DC disposition (X2=41.172, p=<0.0001) of drivers and passengers.After the ED and DC, morepassengers were transferred to a higher level of care than expected, while the inverse was true for drivers. The number of drivers discharged to Skilled Nursing Facilities (SNFs) was also higher than expected, further contributingto the observed difference in DC disposition. Conclusion:Our study found no statistically significant difference between driver and passenger injury severity, length of hospital stay, days in ICU, and SOLA after an MVA. The clinical courses of the two groups were found to be significantly different based on post-ED and DC disposition data. We identified limitations, such as a relatively small sample size and insufficient data on specific car seat locations for passengers, underscoring the need for a more nuanced exploration. Future research must broaden its scope to encompass diverse crash scenarios, vehicle design and safety technologies, seat belt dynamics, and age- and gender-specific vulnerabilities.

Influence of Snowy and Icy Weather on Vehicle Sideslip and Rollover: A Simulation Approach

Many northern hemisphere countries have experienced exceptionally heavy snow, blizzards, and cold snaps in recent years, causing considerable public concern about the high crash rate and safety issues in road traffic. This study used the CarSim dynamics simulation to recreate several vehicle driving scenarios in snow and ice conditions. To explore the influence of speed, curve radius, and road adhesion coefficient on vehicle sideslip and rollover, four lateral stability evaluation indicators, namely lateral offset, lateral acceleration, yaw rate, and roll angle, are chosen. Unfavorable combinations of these factors result in vehicle deviation from their intended trajectory and dramatically increase the likelihood of sideslip and rollover incidents. In particular, road adhesion coefficients ranging from 0.10 to 0.20 lead first to sideslip, while coefficients of 0.21 to 0.35 lead straight to rollover. Additionally, in the initial segment of the curve, cars are more susceptible to lateral instability. Curve radius has the greatest influence on sideslip when the three influencing factors are combined, while speed is the key component affecting rollover incidents. Smaller curve radii and higher speeds are major factors in such incidents. The results are helpful for proper road alignment parameter selection and dynamic speed-limit measures. This can provide a theoretical basis for traffic management departments to take targeted measures, which is of great significance to improving road traffic safety in snowy and icy weather.

Lane numbers and their impact on commercial motor vehicle crash safety: An econometric perspective

This research investigates the intricate relationship between the number of lanes on highways and injury severities sustained by commercial motor vehicle (CMV) drivers. Many studies have addressed crash determinants, but the safety implications of differing numbers of lanes remain insufficiently examined, especially during the highway planning stages. Our study fills this knowledge gap by analyzing injury severity crash factors for a varied number of lane scenarios. Employing a random parameters logit modeling framework, we differentiated injury levels for 2–4 lanes and 6–10 lanes. Key factors were identified for each number of lanes, with older, loss of vehicle control, non-collision crashes, and crashes on locations where grade or hill existed being more perilous and increasing the risk of sustaining severe injuries on 2-lane highways. For 4-lane highways, factors such as non-Oregonian drivers, older drivers, crashes that occurred during the spring season, and crashes that occurred beyond shoulders were associated with an elevated probability of being involved in severe injury crashes. Regarding highways with 6 lanes and higher, driving too fast for conditions and driver error (drowsy, fatigued, inattentive, or reckless) increases the odds of being involved in higher levels of injury crashes. To enhance truck driver safety, we recommend the implementation of electronic stability control in CMVs, moderated speeds on graded sections, improved curve markers, and robust public safety campaigns.

ADDRESSING THE COMPLEX DRIVING NEEDS OF AN AGING POPULATION: THE RISE OF AUTONOMOUS VEHICLES

Abstract Eighty-eight million Americans will be age 65 or older by 2050 and 25% of all drivers will be ≥ 65 years old. Technology has the potential to address some issues such as the inability to drive in advanced age. We postulate that continuing to drive with the use of Advanced Driver Assistance Systems (ADAS) and/or maintaining social connectedness with the use of autonomous vehicles (AVs) have the potential to slow down or reduce the impact of age-related diseases and enhance social mobility. ADAS are electronic technologies that assist drivers in safely operating a vehicle by detecting crash risk stimuli and reducing driver error. Older drivers’ willingness to use ADAS depends on individual perceived usefulness, safety, ease-of-learning, and anxiety. Based on the SAE levels of automation (0-5) , level 3-5 AVs can detect, evaluate, and respond to the vehicle environment without human involvement. Still, ADAS and AV technology cannot completely supplant all human driving and it cannot independently determine crash risk and safety. Comprehensive studies are needed to prove the effectiveness of ADAS in reducing crash risk among older drivers, especially those who are most vulnerable. In addition, AV manufacturers need to take into account the requirements and needs of providing this type of mobility service to older adults, especially those with chronic neurodegenerative diseases like dementia. If these technologies are eventually shown to be safe and embraced by older adults, they have the potential to delay driving cessation, prolong quality of life and maintain social connectedness.

Optimization of foam-filled crash-box under axial loading for pure electric vehicle

To increase the energy-absorbing capability of frontal collision management systems and raise vehicle crash safety, foam-filled crash boxes should be optimized. On the basis of a double tubular construction, a novel foam-filled crash box with different design is developed. The energy absorption capacity, initial peak force and deformation modes of the original and improved crash boxes were examined using impact models. As opposed to the full-filling design, it is demonstrated that the filling design may utilize less foam while increasing specific energy absorption. The stability of the continuing deformation after the first buckling is determined by the foam filled crash-box combined. For the foam-filled crash box, a better optimized design technique is suggested using Radial Basic Function and Non-dominated Sorting GA II. Compression tests are used to validate the design concept. Therefore, the optimal design technique of the crash box is suitable and practical for the crashworthiness design of crash boxes, taking consideration the combined effect of significant indicators for electric vehicle.

TENSILE, IMPACT AND NON-DESTRUCTIVE ANALYSIS OF 316 L STAINLESS STEEL FOR MARINE APPLICATIONS

Welding is an age-old method used for joining the metals permanently. The marine industry is a major user of sheet metal components that benefit from custom welded blanks (TWBs). These advantages encompass weight, cost, and noise savings, as well as increases in crash safety, improved efficacy, and oxidation resistance. However, machinability issues are prevalent as a result of the welding process. These benefits and drawbacks are explored, as well as TWB forming techniques, welding procedures, and materials utilized in the marine sector. In this study two different thickness 316L stainless steel grade plate welded using manual and automatic TIG Welding process. The process parameters are chosen for this study is welding current, voltage, welding speed, shielding gas &amp; filler diameter. The manual TIG welded specimen was than compared with automatic TIG welded specimen in terms of Liquid Penetration test, tensile and impact test. The result and discussion are briefly explored.

Additive manufacture of ultrasoft bioinspired metamaterials

The dynamic loading behavior of materials plays a vital role in various engineering applications, such as aerospace protective components, armor, marine infrastructures, and automotive crash safety. The advent of additive manufacturing technologies has enabled the design of metamaterials that exhibit exceptional mechanical performance and artificially engineered properties not found in nature. However, fabricating ideal materials that resist dynamic loading is challenging because of the complexity of dynamic mechanical processes and varying requirements across different applications. In this study, a novel hierarchical design is proposed that combines natural fiber-inspired frameworks with graphene-inspired parent structures. This design aims to produce metamaterials, with characteristics such as reduced dynamic compressive strength, high energy absorption, and programmable dynamic loading, via advanced manufacturing technologies. An additive-manufacturing-oriented digital design approach and machine learning techniques were employed to engineer the dynamic loading performance of graphene-inspired metamaterials using the bonding principles inspired by natural fibers, to facilitate the design of next-generation metamaterial for advanced manufacturing. Experimental results illustrate the significant improvements achieved with our metamaterials compared to their existing counterparts. These improvements include a decrease in dynamic compressive strength of up to 86 %, while maintaining a remarkable 682 % enhancement in energy absorption during dynamic compressions, with a 42 % reduction in the energy decay rate. A compositional design strategy and programmable dynamic compression curve methodology is proposed that enable the tailored optimization of dynamic loading behaviors without modifying the base topology of metamaterials. This research offers a promising pathway for the development of next-generation materials, engineered to withstand dynamic loadings with intelligent and programmable performances suitable for aerospace, defense, and other high-value applications. By leveraging the advantages of natural fiber-inspired structures and graphene-inspired metamaterials, this work contributes to the advancement of materials with tailored resistance to dynamic loading and opens new possibilities for intelligent dynamic loading performance.

Exploring integrated seating structures in fully autonomous vehicles from an NVH perspective

An intrinsic need exists in the transport vehicle industry to reduce vehicle weight and enhance safety and comfort. This need extends to fully autonomous vehicles, which will gain commonality over time. Since fully autonomous vehicles remove the need for human driver input, a rethink is required to optimise safety, given that the seating designs and configurations in these vehicles could be different. One avenue to potentially increase fully autonomous vehicle safety is integrating the front-row seating into the main body structure. Since an increase in stiffness results in an increase in structural vibration modes and frequencies, it is possible to assess vehicle safety and structural performance through a vibration study. The proposed integrated seating concept has been compared with the existing seating models to assess design feasibility. Increasing safety for fully autonomous vehicles will improve their comfort perception and crash safety. By studying the vibration characteristics of an integrated seating structure, an initial understanding can be ascertained that points the successful design of one in the right direction.

Optimized Crash Safety through AI and Synthetic Data

Optimized Crash Safety through AI and Synthetic Data

Optimization Study of Driver Crash Injuries Considering the Body NVH Performance

Optimal body structure design is a central focus in the field of passive automotive safety. A well-designed body structure enhances the lower threshold for crash safety, serving as a basis for the deployment of other safety systems. Frontal crashes, particularly those with an overlap rate below 25%, are the most frequent types of vehicular accidents and pose elevated risks to occupants due to variable energy absorption and force transmission mechanisms. This study aims to identify an optimized, cost-effective, and lightweight solution that minimizes occupant injuries. Using a micro-vehicle as a case study and accounting for noise, vibration, and harshness (NVH) performance, this paper employs Elman neural networks to predict key variables such as the first-order modes of the body, the body’s mass, and the head injury values for the driver. Guided by these predictions and constrained by the first-order modes and body mass, a genetic algorithm was applied to explore optimal solutions within the solution space defined by the body panel thickness. The optimized design yielded a reduction of approximately 173.43 in the driver’s head injury value while also enhancing the noise, vibration, and harshness performance of the vehicle body. This approach offers a methodological framework for future research into the multidisciplinary optimization of automotive body structures.