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Inspired by the cross-section of a beetle’s elytron, hyperbolic lattice with double-layered feature has received increasing attention in recent years. This paper aims to investigate the compression behavior and energy absorption performance of a truss-based hyperbolic structure. The quasi-static compression simulation for the hyperbolic structure has been performed and validated with a compression test. Through compression simulation and test, the hyperbolic structure proved to show obvious twisting effect. To explore the influences of the geometric parameters in the mechanical properties of hyperbolic structure, this paper has designed 13 cells with varied rotation angle, height, and rod diameter, and investigated the mechanical properties of these configurations.

Due to the high center of gravity of medium-duty vehicles, rollover accidents can easily occur during high-speed cornering and lane changes. In order to prevent the deformation of the body structure, which would restrict the survival space and cause compression injuries to occupants, it is necessary to investigate methods for mitigating these incidents. This paper establishes a numerical model of right-side rollover for a commercial medium-duty vehicle in accordance with ECE R66 regulations, and the accuracy of the model is verified by experiment. According to the results, the material and size parameters of the key components of the right side pillar are selected as design variables. The response result matrix was constructed using the orthogonal design method for total mass, energy absorption, maximum collision acceleration, and minimum distance from the survival space.

Distributed drive electric vehicles (DDEVs), characterized by independently controllable torque at each wheel, redundant actuators, and highly integrated drive systems, are considered as the optimal platform for achieving intelligent driving with high safety and efficiency. This paper focuses on the trajectory tracking and lateral stability coordination control problems in high-speed emergency collision avoidance and autonomous lane change scenarios for DDEVs. A trajectory tracking control algorithm is proposed based on model predictive control (MPC) and coordinated optimization of distributed drive torques. The method adopts a hierarchical control architecture. Firstly, the upper-level trajectory planning layer calculates the lane change trajectory data. Based on the trajectory planning results, the middle-level controller designs a time-varying linear model predictive control method to solve the desired front wheel steering angle and additional yaw moment.

The sensitivity of the electric vehicle (EV) energy consumption characteristics has seriously hindered the promotion and development of vehicle electrification in the coastal area, as quantities of additional energy are caused by the complex and ever-changing coastal driving conditions. In that case, this paper aims to clarify the quantitative impacts of coastal driving conditions on EV energy consumption characteristics based on the coastal traffic data and the EV powertrain. Firstly, this paper analyzes the coastal driving conditions with the help of multisource traffic data. Secondly, the costal-adapted EV energy consumption model is constructed to reveal the relationship between powertrain performance and coastal driving conditions. Next, the EV coastal driving framework is proposed to identify the EV driving state in the coastal area. Finally, the EV energy consumption spatiotemporal characteristics in a typical coastal road are discussed with the help of simulations.

This paper proposes an intelligent car testing and evaluation method based on digital twins, which is crucial for ensuring the proper functioning of autonomous driving systems. This method utilizes digital twin testing technology to effectively map and integrate real vehicles in real-world testing scenarios with virtual test environments. By enriching the testing and validation environment for smart cars, this approach improves testing efficiency and reduces costs. This study connects real test vehicles with simulation software testing toolchains to build a digital twin autonomous driving testing platform. This platform facilitates the validation, testing, and evaluation of functional algorithms, and case study is conducted through testing and validation of an emergency collision avoidance system. By rapidly applying digital twin testing and evaluation techniques for intelligent cars, this approach accelerates the development and deployment of autonomous vehicles.

Traditional vector control needs the installation of mechanical sensors to gather rotor position and speed information in order to enhance the control performance and dynamic quality of electric vehicle wheel motors, which increases system cost and reduces system reliability and stability. On the basis of Popov's super-stability theory, an appropriate adjustable model and reference model are constructed, and the system's reference adaptive law is determined. Furthermore, to solve the problem of the standard PI regulator's poor anti-interference capabilities in speed controllers, the approach of utilizing a sliding-mode speed controller in the speed loop is presented. Finally, a MATLAB/SIMULINK simulation model is created to simulate the motor in three scenarios: no-load start, abrupt speed change, and sudden load change, and a permanent magnet synchronous motor experimental platform is created to validate the control approach.

At present, the remote monitoring cloud platform of many automobile companies only displays the collected data information, and it does not fully mine the deep-level information of the data. This paper uses data mining and machine learning methods to build a driver's driving style and driving condition prediction and recognition model based on the historical driving information generated by the vehicle, so as to improve the supervision and safety of the driver and the vehicle by automobile companies and other automobile-related industries. First, 36 standard driving cycles are utilized to construct an initial operating condition block data set. Second, we obtain the feature variables of driving style and driving conditions through feature engineering, and two recognition model data sets use the principal component analysis (PCA) and clustering algorithm for data dimensionality reduction and cluster analysis.

In this paper, for the front wall of a certain automobile, the defects of drawing splits, excessive thinning and excessive springback in the sheet metal forming process are analyzed and predicted. The stamping process has been simulated. The influence of different technical parameters (blank holder force, stamping speed, die gap and friction coefficient) on the forming results was further investigated using the center composite experiment. Through preliminary finite element simulation, the main drawing defects and trimming springback were analyzed. The second-order response surface model was established to perform the multi-objective optimization design of the stamping process with a NGSA-II genetic algorithm. Based on the relevant simulation data, multiple springback compensations are performed on the die surface to reduce the final springback of the part to meet the requirements.

Thin-walled tubes have been mostly used in passive vehicle safety systems due to high crash energy absorption. The structures with negative Poisson’s ratio (NPR) property will contract to increase its stiffness. In this paper, a double-arrowed NPR structure is designed as a new energy-absorption filler for thin-walled tubes to apply as a novel crash energy absorber. Different beam thicknesses, angles and half cellular width are taken into account in the double-arrowed NPR filling tubes (DAFT) designing and the crashworthiness of the structures are analyzed by using validated nonlinear finite element method. The crashworthiness performances of DAFT are also compared with the singular NPR and hollow tube with the same outer dimension to show the efficiency of DAFT.

Regenerative braking is identified as an essential step toward extending cruising mileage for electric vehicle (EV). Braking energy recovery strategies usually focus on passenger EV and commercial EV is ignored. In this paper, an energy-efficient braking torque distribution strategy is proposed for a rear-axle drive commercial EV to improve braking energy recovery and safety. Firstly, the braking force distribution curve is determined referring to the EU braking law for commercial vehicle and the ideal braking distribution curve. Secondly, a novel braking torque distribution strategy is established adopting fuzzy control algorithm, where the ratio between hydraulic braking torque and regenerative braking torque is updated instantaneously according to vehicle velocity, braking strength and state of charge of battery. Then, the corresponding controller is synthesized on ideal braking condition and several classic cycles.

The safety design of batteries, an important part in passive safety development of electric vehicle, is difficult in practical project application because of complex structure inside and Multi-physics reactions coupled mechanics-electrics-thermodynamics under mechanical abuse. An efficient computational model of batteries that can be attached to model of vehicle used for collision simulation is needed. In this work, four types of Multi-physics battery models (detailed computational model, simplified representative-sandwich model, composite layered model and simplified layered model) of pouch cell with LiFePO4 system are established in a commercial finite element software LS-DYNA (usually used for vehicle collision simulation). And the difficulties of modeling, resource demanded for calculation, accuracy of results (in mechanics, electrics and thermodynamics) in the four models are compared.

When automobile is at the threat of collisions, steering usually needs a shorter longitudinal distance than braking to avoid collision, especially at a high speed. In emergency steering evasion, the vehicle may be out of the road or colliding with obstacles ahead when the driver’s steering torque is excessive or insufficient. In view of the above problems, this paper presents an emergency steering evasion torque assistance system based on optimized trajectory. First, a feasible steering evasion area is established which treats the paths of excessive and insufficient steering as boundary conditions in this paper. An optimized trajectory is derived from the lateral acceleration of the vehicle and the time to the adjacent lane as optimization conditions. Second, a two degree of freedom vehicle model is used to represent dynamics of the vehicle.

With the rapid development of modern economy and society, traffic congestion has become an increasingly serious problem. Vehicle cooperative driving can alleviate traffic congestion and improve road traffic capacity. Compare with vehicle separate control, cooperative driving combines various vehicle systems, and highly integrates information on obstacle location, vehicle status and driving intention. Then the controller uniformly issues instructions to ensure the orderly driving of the platoon. In the cooperative driving platoon, the displacement difference and the speed difference between vehicles have a certain relationship, which reduces the possibility of traffic accidents and then improves the safety of driving. In the process of cooperative driving, if there are multiple vehicles whose speeds don’t meet the current lane requirements, or if there are obstacles ahead, multi-vehicle lane change measures must be taken.

Minimum energy consumption with maximum comfort driving experience define the ideal human mobility. Recent technological advances in most Advanced Driver Assistance Systems (ADAS) on electric vehicles not only present a significant opportunity for automated eco-driving but also enhance the safety and comfort level. Understanding driving styles that make the systems more human-like or personalized for ADAS is the key to improve the system comfort. This research focuses on the personalized and green adaptive cruise control for intelligent electric vehicle, which is also known to be MyEco-ACC. MyEco-ACC is based on the optimization of regenerative braking and typical driving styles. Firstly, a driving style model is abstracted as a Hammerstein model and its key parameters vary with different driving styles. Secondly, the regenerative braking system characteristics for the electric vehicle equipped with 4-wheel hub motors are analyzed and braking force distribution strategy is designed.

The paper analyzes the mechanism of automobile millimeter wave radar noise, this paper does not study radar noise from the angle of signal processing, but from the level of false detection and missed detection, at the same time, the noise mechanism is modeled and verified. Firstly, the purpose and significance of the research of radar vehicle noise are described, and then, we summarize and outline the macro phenomenon and the specific characteristics of the automobile millimeter wave radar noise.

With rapid increase of vehicles on the road, safety concerns have become increasingly prominent. Since the leading cause of many traffic accidents is known to be by human drivers, developing autonomous vehicles is considered to be an effective approach to solve the problems above. Although trajectory tracking plays one of the most important roles on autonomous driving, handling the coupling between trajectory-tracking control and ESP under certain driving scenarios remains to be challenging. This paper focuses on trajectory-tracking control considering the role of ESP. A vehicle model is developed with two degrees of freedom, including vehicle lateral, and yaw motions. Based on the proposed model, the vehicle trajectory is separated into both longitudinal and lateral motion. The coupling effect of the vehicle and ESP is analyzed in the paper. The lateral trajectory-tracking algorithm is developed based on the preview follower theory.

Performance testing and evaluation always plays an important role in the developmental process of a vehicle, which also applies to autonomous vehicles. The complex nature of an autonomous vehicle from architecture to functionality demands even more quality-and-quantity controlled testing and evaluation than ever before. Most of the existing testing methodologies are task-or-scenario based and can only support single or partial functional testing. These approaches may be helpful at the initial stage of autonomous vehicle development. However, as the integrated autonomous system gets mature, these approaches fall short of supporting comprehensive performance evaluation. This paper proposes a novel hierarchical and systematic testing and evaluation approach to bridge the above-mentioned gap.

As one of advanced driver assistance systems (ADAS), automatic parking system has great market prospect and application value. In this paper, based on an intelligent vehicle platform, an automatic parking system is designed by using multi-point preview theory. The vehicle kinematics model was established, based on Ackermann steering principle. By analyzing working conditions of parallel parking, complex constraint condition of parking trajectory is established and reference trajectory based on sine wave is proposed. In addition, combined with multi-point preview theory, the design of trajectory following controller for automatic parking is completed. The cost function is designed, which consider the trajectory following effect and the degree of easy handling. The optimization of trajectory following control is completed by using the cost function.

Advanced driver assistance systems (ADAS) are being developed for more and more complicated application scenarios, which often require more predictive strategies with better understanding of driving environment. Taking traffic vehicles’ maneuvers into account can greatly expand the beforehand time span for danger awareness. This paper presents a maneuver-based strategy to vehicle collision threat assessment. First, a maneuver-based trajectory prediction model (MTPM) is built, in which near-future trajectories of ego vehicle and traffic vehicles are estimated with the combination of vehicle’s maneuvers and kinematic models that correspond to every maneuver. The most probable maneuvers of ego vehicle and each traffic vehicles are modeled and inferred via Hidden Markov Models with mixture of Gaussians outputs (GMHMM). Based on the inferred maneuvers, trajectory sets consisting of vehicles’ position and motion states are predicted by kinematic models.

Based on the research and analysis of the current brake systems, this paper presents a novel electro-hydraulic brake system, which can better meet the functional requirements. The system mainly contains a master cylinder, two brake hydraulic cylinders and drive motors, two transmission mechanisms, thirteen solenoid valves, pedal force simulator, etc. Since the proposed brake system uses a dual motor along with two brake hydraulic cylinders, it has advantages in providing fast pressure response, flexible working modes, high precision and strong fault tolerance. In order to facilitate the study of pressure control algorithm for the proposed brake system, a mathematical model of the brake system is firstly established, then a multiplexed time-division pressure control algorithm is proposed to realize the simultaneous or partially simultaneous pressure control, which ensures the high precision and short response time.