Automotive Innovation最新文献

Adhesive bonding is a promising joining technology for joining lightweight aluminum structures, offering advantages such as the absence of additional heat input, connection damage, and environmental pollution. To further enhance the strength of aluminum adhesive joints, this study investigates the influence of laser surface treatment on their mechanical properties. Specifically, the effect of laser processing patterns and their geometric parameters on aluminum alloy adhesive joints is examined. A fiber laser is used to process crater array and multi-groove pattern on A6061 aluminum surface. The impact of crater overlap ratio and groove distance on various aspects, including aluminum surface morphology, roughness (S_a), adhesive joints shear, tensile strength, and failure modes is discussed. Laser confocal microscope tests, water contact angle tests, lap shear tests, and cross tensile tests are employed to analyze these parameters. The results indicate that as the crater overlap ratio increases, the S_a value of the aluminum surface increases. Moreover, the shear strength of adhesive joints initially increases and then decreases, while the tensile strength consistently increases. On the other hand, an increase in groove distance leads to a decrease in S_a, as well as a reduction in both shear and tensile strength of adhesive joints. For shear loading conditions, mechanical interlocking is identified as one of the bonding mechanisms in aluminum adhesive joints featuring crater array and multi-groove patterns. The formation of interlocking structures is found to be influenced by the aluminum surface pattern and its associated parameters, as revealed through failure surface analysis. Specifically, adhesive and crater or groove interactions contribute to the formation of interlocking structures in specimens with a crater overlap ratio of − 60% or groove distances of 120, 180, 300, and 400 μm. Conversely, specimens with overlap ratios of 0%, 40%, and 60% exhibit interlocking structures formed by the adhesive and crater edge.

This study focuses on enhancing the agility and path tracking capabilities of autonomous trucks equipped with dual 4WIS-4WID modular chassis. To address the challenges associated with these versatile vehicles, a comprehensive approach is presented. Firstly, a communication framework is devised, utilizing a hierarchical combination of two fieldbus systems. This framework facilitates adaptive marshalling, allowing effective communication and coordination among the various modular components of the autonomous truck. Secondly, a reference path generation strategy is proposed. This strategy relates the motion paths of the truck's body to its modular chassis. Reference paths for the modular chassis are derived based on the center of mass, effectively resolving the issue of differing motion paths. To tackle the path tracking problem for dual modular chassis, a cooperative path tracking controller is developed. This controller is designed using the kinematic model of the autonomous truck, enabling adaptive control through online adjustments of controller parameters based on measured input–output data. Simulation and real vehicle testing validate the proposed path tracking controller. In the dual modular chassis path tracking simulation, the maximum lateral position error and the maximum yaw angle error of truck body at different speeds are 0.082 m and 0.007 rad, respectively. In the real vehicle test, the maximum lateral position error is 0.194 m, and the maximum yaw angle error is 0.071 rad. These results demonstrate the practicality and effectiveness of the controller in real-world applications.

The Safety of The Intended Functionality (SOTIF) challenge represents the triggering condition by elements of a specific scenario and exposes the function limitation of an autonomous vehicle (AV), which leads to hazards. As for operation-content-related features, the scenario is similar to AVs’ SOTIF research and development. Therefore, scenario generation is a significant topic for SOTIF verification and validation procedure, especially in the simulation testing of AVs. Thus, in this paper, a well-designed scenario architecture is first defined, with comprehensive scenario elements, to present SOTIF trigger conditions. Then, considering complex traffic disturbance as trigger conditions, a novel SOTIF scenario generation method is developed. An indicator, also known as Scenario Potential Risk, is defined as the combination of the safety control intensity and the prior collision probability. This indicator helps identify critical scenarios in the proposed method. In addition, the corresponding vehicle motion models are established for general straight roads, curved roads, and safety assessment areas. As for the traffic participants’ motion model, it is designed to construct the key dynamic events. To efficiently search for critical scenarios with the trigger of complex traffic flow, this scenario is encoded as genes and it is regenerated through selection, mutation, and crossover iteration processes, known as the Genetic Algorithm (GA). Experimental results show that the GA-based method could efficiently construct diverse and critical traffic scenarios, contributing to the construction of the SOTIF scenario library.

Most real-world situations involve unavoidable measurement noises or perception errors which result in unsafe decision making or even casualty in autonomous driving. To address these issues and further improve safety, automated driving is required to be capable of handling perception uncertainties. Here, this paper presents an observation-robust reinforcement learning against observational uncertainties to realize safe decision making for autonomous vehicles. Specifically, an adversarial agent is trained online to generate optimal adversarial attacks on observations, which attempts to amplify the average variation distance on perturbed policies. In addition, an observation-robust actor-critic approach is developed to enable the agent to learn the optimal policies and ensure that the changes of the policies perturbed by optimal adversarial attacks remain within a certain bound. Lastly, the safe decision making scheme is evaluated on a lane change task under complex highway traffic scenarios. The results show that the developed approach can ensure autonomous driving performance, as well as the policy robustness against adversarial attacks on observations.

Secret key generation from wireless channel is an emerging technology for communication network security, which exploits the reciprocity and time variability of wireless channels to generate symmetrical keys between the communication parties. Compared to the existing asymmetric key distribution methods, secret key generation from wireless channel has low complexity and high security, making it especially suitable for distributed networks. In vehicular communications, the reciprocity of wireless channel is degraded due to the movement of vehicular. This paper proposes a high consistency wireless key generation scheme for vehicular communication, especially applied to LTE-V2X (LTE vehicle to everything) systems. A channel reciprocity enhancement method is designed based on Wiener filter extrapolation, which can efficiently reduce the mismatch between the channels at the receiver and significantly reduce key disagreement rate. A real experimental system based on vehicle and universal software radio peripheral (USRP) platform is setup to verify the secret key generation in LTE-V2X systems. The effectiveness of the proposed method is verified in simulations and extensive practical tests.

Speed advisory systems have been used for connected vehicles to optimize energy consumption. However, for their practical utilization, presence of preceding vehicles and signals must be taken into account. Moreover, for Battery Electric Vehicles (BEVs), factors that deteriorate battery’s life cycle and discharging time must also be considered. This paper proposes an eco-driving control for connected BEV with traffic signals and other safety constraints. Traffic signals are considered as interior point constraints, while inter-vehicle distance with preceding vehicles, vehicle speed and battery charging/discharging limits, are considered as state safety constraints. Backward-forward simulator based Speed Guidance Model is applied to follow the optimized velocity under powertrain safety limitations. Effectiveness of the proposed methodology is tested on a 5.3-km route in Islamabad, Pakistan. Real traffic data using Simulation of Urban Mobility under different driving scenarios is considered. Using the proposed method, around 21% energy can be saved compared to the preceding vehicles that followed their random velocities under the same traffic and route conditions. This means the EV controlled by the proposed method can have longer driving range. Furthermore, the host BEV has crossed signals during their green time without collision with preceding vehicles. Low charging rates and terminal Depth of Discharge indicate less number of charging cycles, thus proving the usefulness of the proposed solution as battery’s lifesaving strategy.

In recent years, vehicle state estimation methods incorporating different vehicle models have received extensive attention. When the vehicle is disturbed by external forces not considered in traditional vehicle models (for example, a certain slope, or wind resistance different from theoretical calculation), the problem of model mismatch will occur, which leads to the inaccurate estimation of the vehicle states. To solve this problem, an Unscented Kalman Filter (UKF) algorithm is used to fuse inertial navigation data with the vehicle hybrid model in this paper. The hybrid model introduces a switching strategy that fuses the vehicle kinematics and the dynamics models while augmenting biases that need to be estimated in the vehicle states. The switching strategy resolves the integration divergence problem of vehicle dynamics models at low speeds and the inaccurate estimation of vehicle kinematics models at high speeds. Simulation experiments demonstrate that the proposed method can accurately estimate biases induced by external forces, enhancing the accuracy and confidence of states by eliminating errors caused by these biases. The robustness of the method is validated in vehicle verification platform experiments, where errors in vehicle lateral speed and yaw rate are reduced by 9.7 cm/s and 0.012 °/s, respectively, under large curvature maneuvers, and 9.6 cm/s and 0.004 °/s under quarter-turn maneuvers. The proposed method significantly improves lateral speed and vehicle position accuracies.

Accurate estimation of the state-of-energy (SOE) in lithium-ion batteries is critical for optimal energy management and energy optimization in electric vehicles. However, the conventional recursive least squares (RLS) algorithm struggle to track changes in battery model parameters under dynamic conditions. To address this, a multi-timescale estimator is proposed. A variable forgetting factor RLS approach is used to determine the model parameters at a macro timescale, and the H infinity filter is utilized to estimate the SOE at a micro timescale. The proposed algorithm is verified and analyzed and shown to have accurate and robust identification of battery model parameters. Finally, experiments under dynamic cycles demonstrate that the proposed algorithm has a high level of accuracy for SOE estimation.

Electrochemical impedance spectroscopy (EIS) contributes to developing the fault diagnosis tools for fuel cells, which is of great significance in improving service life. The conventional impedance measurement techniques are limited to linear responses, failing to capture high-order harmonic responses. However, nonlinear electrochemical impedance analysis incorporates additional nonlinear information, enabling the resolution of such responses. This study proposes a novel multi-stage fault diagnosis method based on the nonlinear electrochemical impedance spectrum (NEIS). First, the impact of alternating current excitation amplitude on NEIS is analyzed. Then, a series of experiments are conducted to obtain NEIS data under various fault conditions, encompassing recoverable faults like flooding, drying, starvation, and their mixed faults, spanning different degrees of fault severity. Based on these experiments, both EIS and NEIS datasets are established, and principal component analysis is utilized to extract the main features, thereby reducing the dimensionality of the original data. Finally, a fault diagnosis model is constructed with the support vector machine (SVM) and random forest algorithms, with model hyperparameters optimized by a hybrid genetic particle swarm optimization (HGAPSO) algorithm. The results show that the diagnostic accuracy of NEIS is higher than that of traditional EIS, with the HGAPSO-SVM model achieving a 100% accurate diagnosis under the NEIS dateset and self-defined fault labels.

Automated driving is poised to become a pivotal technology in the future automotive transportation. However, it is evident that the implementation of automated driving presents significant technical challenges. To accelerate the development and deployment of automated driving the European Commission initiated the research project L3Pilot in 2017. With a budget of 65 million Euros and the involvement of 13 car manufacturers, L3Pilot stands as the largest European project on automated driving (AD). This paper serves as a comprehensive account of BMW’s main activities in the L3Pilot project that ended in 2021. The research questions addressed in this project are related to the following topics: what are the guidelines for the development of AD? How do potential customers interact with AD? And what is the safety impact assessment of AD? The paper presents the findings related to all three research questions to contribute to the further development of automated driving. For this purpose together with other partners the Code of Practice of AD was defined as a guideline for the development of future AD systems. Related to the second question, BMW conducted tests with AD systems on motorways and in parking scenarios, with over 100 test subjects experiencing AD. The studies provide input and considerations for future AD systems. Finally, in the safety impact assessment, BMW investigated with other project partners the potential safety benefits of AD through simulation. The results show a potential to improve road safety. In conclusion, the exploration of all three research questions has led to a deeper understanding of SAE Level 3 AD.