Search Results

Fuel cell vehicles have always garnered a lot of attention in terms of energy utilization and environmental protection. In the analysis of fuel cell performance, there are usually some outliers present in the raw experimental data that can significantly affect the data analysis results. Therefore, data cleaning work is necessary to remove these outliers. The polarization curve is a crucial tool for describing the basic characteristics of fuel cells, typically described by semi-empirical formulas. The parameters in these semi-empirical formulas are fitted using the raw experimental data, so how to quickly and effectively automatically identify and remove data outliers is a crucial step in the process of fitting polarization curve parameters. This article explores data-cleaning methods based on the Local Outlier Factor (LOF) algorithm and the Isolation Forest algorithm to remove data outliers.

Brake judder affects vehicle safety and comfort, making it a key area of research in brake NVH. Transfer path analysis is effective for analyzing and reducing brake judder. However, current studies mainly focus on passenger cars, with limited investigation into commercial vehicles. The complex chassis structures of commercial vehicles involve multiple transfer paths, resulting in extensive data and testing challenges. This hinders the analysis and suppression of brake judder using transfer path analysis. In this study, we propose a simulation-based method to investigate brake judder transfer paths in commercial vehicles. Firstly, road tests were conducted to investigate the brake judder of commercial vehicles. Time-domain analysis, order characteristics analysis, and transfer function analysis between components were performed.

In recent years, with the development of computing infrastructure and methods, the potential of numerical methods to reasonably predict aerodynamic noise in turbocharger compressors of heavy-duty diesel engines has increased. However, aerodynamic acoustic modeling of complex geometries and flow systems is currently immature, mainly due to the greater challenges in accurately characterizing turbulent viscous flows. Therefore, recent advances in aerodynamic noise calculations for automotive turbocharger compressors were reviewed and a quantitative study of the effects for turbulence models (Shear-Stress Transport (SST) and Detached Eddy Simulation (DES)) and time-steps (2° and 4°) in numerical simulations on the performance and acoustic prediction of a compressor under various conditions were investigated.

In recent years, the burgeoning applications of hydrogen fuel cells have ignited a growing trend in their integration within the transportation sector, with a particular focus on their potential use in multi-rotor drones. The heightened mass-based energy density of fuel cells positions them as promising alternatives to current lithium battery-powered drones, especially as the demand for extended flight durations increases. This article undertakes a comprehensive exploration, comparing the performance of lithium batteries against air-cooled fuel cells, specifically within the context of multi-rotor drones with a 3.5kW power requirement. The study reveals that, for the specified power demand, air-cooled fuel cells outperform lithium batteries, establishing them as a more efficient solution.

Water content estimation is a key problem for studying the PEM fuel cell. When several hundred fuel cells are connected in serial and their active surface area is enlarged for sufficient power, the difference between cells becomes significant with respect to voltage and water content. The voltage of each cell is measurable by the cell voltage monitor (CVM) while it is difficult to estimate water content of the individual. Resistance of the polymer electrolyte membrane is monotonically related to its water content, so that the new online high frequency resistance (HFR) measurement technique is investigated to identify the uniformity of water content between cells and analyze its sensitivity to operating conditions in this paper. Firstly, the accuracy of the proposed technique is experimentally validated to be comparable to that of a commercialized electrochemical impedance spectroscopy (EIS) measurement equipment.

Lightweight design is a key factor in general engineering design practice, however, it often conflicts with fatigue durability. This paper presents a way for improving the effectiveness of fatigue performance dominated optimization, demonstrated through a case study on suspension brackets for heavy-duty vehicles. This case study is based on random load data collected from fatigue durability tests in proving grounds, and fatigue failures of the heavy-duty vehicle suspension brackets were observed and recorded during the tests. Multi-objective fatigue optimization was introduced by employing multiaxial time-domain fatigue analysis under random loads combined with the non-dominated sorting genetic algorithm II with archives.

The passive pre-chamber (PC) is valued for its jet ignition (JI) and is suitable for wide use in the field of gasoline direct injection (GDI) for small passenger cars, which can improve the performance of lean combustion. However, the intake, exhaust, and ignition combustion stability of the engine at low speed is a shortcoming that has not been overcome. Changing the structural design to increase the fluidity of the main chamber (MC) and pre-chamber (PC) may reduce jet ignition performance, affecting engine dynamics. This investigation is based on non-uniformly nozzles distributed passive pre-chamber, which is adjusted according to the working medium exchange between PC and MC. The advantages and disadvantages of the ignition mode of PC and SI in the target engine speed range are compared through optical experiments on a small single-cylinder GDI engine.

This paper presents an integrated modeling approach for real-time discretionary lane-changing decisions by autonomous vehicles, aiming to achieve human-like behavior. The approach incorporates a two-player normal-form game and a novel risk field method. The normal-form game represents the strategic interactions among traffic participants. It captures the trade-offs between lane-changing benefits and risks based on vehicle motion states during a lane change. By continuously determining the Nash equilibrium of the game at each time step, the model decides when it is appropriate to change the lane. A novel risk field method is integrated with the game to model risks in the game pay-offs. The risk field introduces regions along the desired target lane with different time headway ranges and risk weights, capturing traffic participants' complex risk perceptions and considerations in lane-changing scenarios.

Lane change obstacle avoidance is a common driving scenario for autonomous vehicles. However, existing methods for lane change obstacle avoidance in vehicles decouple path and velocity planning, neglecting the coupling relationship between the path and velocity. Additionally, these methods often do not sufficiently consider the lane change behaviors characteristic of human drivers. In response to these challenges, this paper innovatively applies the Dynamic Movement Primitives (DMPs) algorithm to vehicle trajectory planning and proposes a real-time trajectory planning method that integrates DMPs and Artificial Potential Fields (APFs) algorithm (DMP-Fs) for lane change obstacle avoidance, enabling rapid coordinated planning of both path and velocity. The DMPs algorithm is based on the lane change trajectories of human drivers. Therefore, this paper first collected lane change trajectory samples from on-road vehicle experiments.

Recently, several datasets have become available for occupant monitoring algorithm development, including real and synthetic datasets. However, real data acquisition is expensive and labeling is complex, while virtual data may not accurately reflect actual human physiology. To address these issues and obtain high-fidelity data for training intelligent driving monitoring systems, we have constructed a hybrid dataset that combines real driving image data with corresponding virtual data generated from 3D driving scenarios. We have also taken into account individual anthropometric measures and driving postures. Our approach not only greatly enriches the dataset by using virtual data to augment the sample size, but it also saves the need for extensive annotation efforts. Besides, we can enhance the authenticity of the virtual data by applying ergonomics techniques based on RAMSIS, which is crucial in dataset construction.

The software installed in Electronic Control Units (ECUs) has witnessed a significant scale expansion as the functionality of Intelligent Connected Vehicles (ICVs) has become more sophisticated. To seek convenient long-term functional maintenance, stakeholders want to access ECUs data or update software from anywhere via diagnostic. Accordingly, as one of the external interfaces, Diagnostics over Internet Protocol (DoIP) is inevitably prone to malicious attacks. It is essential to note that cybersecurity threats not only arise from inherent protocol defects but also consider software implementation vulnerabilities. When implementing a specification, developers have considerable freedom to decide how to proceed. Differences between protocol specifications and implementations are often unavoidable, which can result in security vulnerabilities and potential attacks exploiting them.

In the process of automobile industrialization, integrated electric drive systems turn to be the mainstream transmission system of electric vehicles gradually. The main sources of noise and vibration in the chassis are from the gear reducer and motor system, as a replacement of engine. For improving the electric vehicles NVH performance, effective identification and quantitative analysis of the main noise sources are a significant basis. Based on the rotating hub test platform in the semi-anechoic chamber, in this experiment, an electric vehicle equipped with a three-in-one electric drive system is taken as the research object. As well the noise and vibration signals in the interior vehicle and the near field of the electric drive system are collected under the operating conditions of uniform speed, acceleration speed, and coasting with gears under different loads, and the test results are processed and analyzed by using the spectral analysis and order analysis theories.

In order to improve the efficiency of safety performance test for intelligent vehicles and construct the test case set quickly, critical scenarios based on graded hazard disposal model of human drivers are proposed, which can be used for extraction of test cases for safety performance. Based on the natural driving data in China Field Operational Test (China-FOT), the four-stage collision avoidance process of human drivers is obtained, including steady driving stage, risk judgment stage, collision reaction stage and collision avoidance stage. And there are two human driver states: general state and alert state. Then the graded hazard disposal model of human drivers is constructed.

Hydrogen energy is a kind of secondary energy with an abundant source, wide application, green, and is low-carbon, which is important for building a clean, low-carbon, safe, and efficient energy system and achieving the goal of carbon peaking and being carbon neutral. In this paper, the effect of nozzle position, hydrogen injection timing, and ignition timing on the in-cylinder combustion characteristics is investigated separately with the 13E hydrogen engine as the simulation object. The test results show that when the nozzle position is set in the middle of the intake and exhaust tracts (L2 and L3), the peak in-cylinder pressure is slightly higher than that of L1, but when the nozzle position is L2, the cylinder pressure curve is the smoothest, the peak exothermic rate is the lowest, and the peak cylinder temperature is the lowest.

Fuel cells’ soft output characteristics and mismatched voltage levels with subordinate electrical devices necessitate the use of DC/DC converters, which are an important part of the power electronic subsystem of the fuel cell system. The staggered parallel Boost topology is commonly employed in fuel cell DC/DC converters. This paper focuses on the control characteristics of the two-phase interleaved parallel Boost topology in the context of a fuel cell system. Specifically, we derive the small-signal model and output-control transfer function of the topology, and design a controller based on frequency characteristic analysis. Our proposed controller uses a cascaded double-ring structure and supports both constant current and constant voltage switching modes. To evaluate the effectiveness of our proposed control strategy, we conduct simulation and prototype testing.

The design method for the powertrain mounting system in internal combustion engine vehicles is well-established. Electric vehicles experience higher vibration frequencies and more significant transient responses when accelerating or braking than fuel vehicles due to their high speed and fast response. Therefore, the design of the electric drive assembly mounting system requires further development. The modeling of electric drive assembly mounting systems often neglects the mounting bracket’s influence, which significantly affects the center of mass and rotational inertia of the electric drive assembly. This paper examines the effect of the mounting bracket in the electric drive assembly mounting system. It establishes a mathematical model with six degrees of freedom for the mounting system, considering the mounting bracket. By comparing the natural characteristics and the transient response, it is discussed whether the mass of the mounting bracket greatly influences the system.

To study the state of health (SOH) of the proton exchange membrane fuel cell (PEMFC), a novel hybrid method combining the advantages of both the model-based and data-driven methods is proposed. Firstly, the model-based method is proposed based on the voltage degradation model to estimate the variation trend, and three parameters reflecting the performance degradation are selected. Secondly, the data-driven (long short-term memory (LSTM)) method is presented to estimate the variation fluctuation. Moreover, the core step of the hybrid method is returning the results of the LSTM method to the power degradation model as the “observation” and modifying related parameters to improve the estimation accuracy. Finally, the sliding window method is applied to solve the problem of the data increase with the increase of the operating time. The results show that the power estimation is better than the current estimation for the SOH estimation.

To improve the prediction accuracy of the remaining useful life (RUL) of the proton exchange membrane fuel cell (PEMFC), an integrated health index (IHI) including electrical and non-electrical parameters of PEMFC is established, and the RUL prediction is conducted based on the above index. Firstly, several operating conditions including the PEMFC degradation information are selected according to the information theory method. Moreover, the IHI is established by the sequential quadratic programming method. Secondly, RUL predictions based on the power and IHI are conducted by the adaptive neuro fuzzy inference system (ANFIS), respectively. Finally, different results comparisons including power and IHI differences, differences between experimental and training/predicting results, amounts of different differences in training and predicting phases, and RUL prediction results are presented in detail.

With the increase of motor speed and the deterioration of operating environment, it is more difficult to predict the transient temperature field (TTF). Meanwhile, it is difficult to obtain the temperature test dataset of key nodes under various complete road conditions, so the cost of bench test or real vehicle test is high. Therefore, it is of great significance to establish a high fidelity, lightweight temperature prediction model which can be applied to real vehicle thermal management for ensuring the safe and stable operation of motor. In this paper, a physical model simulating electromagnetic-heat-flow multi-physical coupling of permanent magnet synchronous motor (PMSM) in electric drive gearbox (EDG) is established, and the correctness of the model is verified by the actual EDG bench test.

The acoustic muffler is one of the practical solutions to reduce the noise in ducts. The acoustic and aerodynamic performances are two critical indices of one muffler for the air intake system of a hydrogen fuel cell electric vehicle (FCEV). In this study, the concept of phononic crystal is applied to design the muffler to obtain superior acoustic performance. One duct with periodic and compact resonator-type mufflers is designed for broadband noise attenuation. The two-dimensional (2D) transfer matrix method and bandgap theory are employed to calculate the transmission loss (TL) and acoustic bandgap. It is numerically and theoretically demonstrated that broadband noise attenuation could be acquired from 500Hz to 3500Hz. Afterwards, the three-dimensional (3D) computational fluid dynamics (CFD) approach is applied to predict the pressure distribution. The results indicate that the proposed hybrid muffler and the phononic crystal duct possess low pressure loss values.