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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.

SLAM (Simultaneous Localization and Mapping) plays a key role in autonomous driving. Recently, 4D Radar has attracted widespread attention because it breaks through the limitations of 3D millimeter wave radar and can simultaneously detect the distance, velocity, horizontal azimuth and elevation azimuth of the target with high resolution. However, there are few studies on 4D Radar in SLAM. In this paper, RI-FGO, a 4D Radar-Inertial SLAM method based on Factor Graph Optimization, is proposed. The RANSAC (Random Sample Consensus) method is used to eliminate the dynamic obstacle points from a single scan, and the ego-motion velocity is estimated from the static point cloud. A 4D Radar velocity factor is constructed in GTSAM to receive the estimated velocity in a single scan as a measurement and directly integrated into the factor graph. The 4D Radar point clouds of consecutive frames are matched as the odometry factor.

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.

Ultra-lean combustion of GDI engine could achieve higher thermal efficiency and lower NOx emissions, but it also faces challenges such as ignition difficulties and low-speed flame propagation. In this paper, the sparked-spray is proposed as a novel ignition method, which employs the spark to ignite the fuel spray by the cooperative timing control of in-cylinder fuel injection and spark ignition and form a jet flame. Then the jet flame fronts propagate in the ultra-lean premixed mixture in the cylinder. This combustion mode is named Sparked-Spray Induced Combustion (SSIC) in this paper. Based on a 3-cylinder 1.0L GDI engine, a 3D simulation model is established in the CONVERGE to study the effects of ignition strategy, compression ratio, and injection timing on SSIC with a global equivalence ratio of 0.50. The results show it is easier to form the jet flame when sparking at the spray front because the fuel has better atomization and lower turbulent kinetic energy at the spray front.

Exhaust gas recirculation technology is one of the main methods to reduce engine emissions. The pressure of the intake pipe of turbocharged direct-injection diesel engine is high, and it is difficult to realize EGR technology. The application of Venturi tube can easily solve this problem. In this paper, the working principle of guide-injection Venturi tube is introduced, the EGR system and structure of a turbocharged diesel engine using the guide-injection Venturi tube are studied. According to the working principle of EGR system of turbocharged diesel engine, the model of guide-injection Venturi tube is established, the calculation grid is divided, and it is carried out by using Computational Fluid Dynamics method that the three-dimensional numerical simulation of the internal flow of Venturi tube under different EGR rates injection.

As one of the pollutants that cannot be ignored, soot has a great impact on human health, environment, and energy conversion. In this investigation, the effect of residence time (25ms, 35ms, and 45ms) and ammonia on morphology and nanostructure of soot in laminar ethylene flames has been studied under atmospheric conditions and different flame heights (15 mm and 30 mm). The transmission electron microscopy (TEM) and high-resolution transmission electron microscope (HRTEM) are used to obtain morphology of aggregates and nanostructure of primary particles, respectively. In addition, to analyze the nanostructure of the particles, an analysis program is built based on MATLAB software, which is able to obtain the fringe separation distance, fringe length, and fringe tortuosity parameters of primary particles, and has been verified by the multilayer graphene interlayer distance.

The on-ramp merging driving scenario is challenging for achieving the highest-level autonomous driving. Current research using reinforcement learning methods to address the on-ramp merging problem of automated vehicles (AVs) is mainly designed for a single AV, treating other vehicles as part of the environment. This paper proposes a control framework for cooperative on-ramp merging of multiple AVs based on multi-agent deep reinforcement learning (MADRL). This framework facilitates AVs on the ramp and adjacent mainline to learn a coordinate control policy for their longitudinal and lateral motions based on the environment observations. Unlike the hierarchical architecture, this paper integrates decision and control into a unified optimal control problem to solve an on-ramp merging strategy through MADRL.

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.

Argon power cycle hydrogen engine is an internal combustion engine that employs argon instead of nitrogen of air as the working fluid, oxygen as the oxidizer, and hydrogen as the fuel. Since argon has a higher specific heat ratio than air, argon power cycle hydrogen engines have theoretically higher indicated thermal efficiencies according to the Otto cycle efficiency formula. However, argon makes the end mixture more susceptible to spontaneous combustion and thus is accompanied by a stronger knock at a lower compression ratio, thus limiting the improvement of thermal efficiency in engine operation. In order to suppress the limitation of knock on the thermal efficiency, this paper adopts a combination of experimental and simulation methods to investigate the effects of port water injection on the knock suppression and combustion characteristics of an argon power cycle hydrogen engine.

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.

Accurate and reliable localization in GNSS-denied environments is critical for autonomous driving. Nevertheless, LiDAR-based and camera-based methods are easily affected by adverse weather conditions such as rain, snow, and fog. The 4D Radar with all-weather performance and high resolution has attracted more interest. Currently, there are few localization algorithms based on 4D Radar, so there is an urgent need to develop reliable and accurate positioning solutions. This paper introduces RIO-Vehicle, a novel tightly coupled 4D Radar/IMU/vehicle dynamics within the factor graph framework. RIO-Vehicle aims to achieve reliable and accurate vehicle state estimation, encompassing position, velocity, and attitude. To enhance the accuracy of relative constraints, we introduce a new integrated IMU/Dynamics pre-integration model that combines a 2D vehicle dynamics model with a 3D kinematics model.

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 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.

In order to scrutinize the timing variables impacting the combustion performance and emissions of the Port Fuel Injection hydrogen engine (PFI-H2ICE), a model of a four-cylinder hydrogen engine is meticulously built utilizing the 1D software GT-POWER. The effect of excess air coefficients and timing strategies (including the intake valve opening timing (IVO), the start of injection timing (SOI), and ignition timing) is analyzed in this study. The main conclusions are as follows: The hydrogen engine remold from the Isuzu JE4N28 nature gas engine manifests a lean combustion threshold ranging between 2.0 and 2.5. Notably, advancing intake valve opening timing by 20°CA has proven beneficial to the brake thermal efficiency (BTE) of the hydrogen engine while reducing the NOx emissions by a substantial margin, and advancing intake valve opening timing bears the virtue of strengthen the positive influence of the start of injection timing upon the engine's combustion performance.

A DMS500 engine exhaust particle size spectrometer was employed to characterize the effects of injection strategies on particulate emissions from a turbocharged gasoline direct injection (GDI) engine. The effects of operating parameters (injection pressure, secondary injection ratio and secondary injection end time) on particle diameter distribution and particle number density of emission were investigated. The experimental result indicates that the split injection can suppress the knocking tendency at higher engine loads. The combustion is improved, and the fuel consumption is significantly reduced, avoiding the increase in fuel pump energy consumption caused by the 50 MPa fuel injection system, but the delayed injection increases particulate matter emissions.

This paper focuses on lane-changing trajectory planning and trajectory tracking control in autonomous vehicle technology. Aiming at the lane-changing behavior of autonomous vehicles, this paper proposes a new lane-changing trajectory planning method based on particle swarm optimization (PSO) improved third-order Bezier curve path planning and polynomial curve speed planning. The position of Bezier curve control points is optimized by the particle swarm optimization algorithm, and the lane-changing trajectory is optimized to improve the comfort of lane changing process. Under the constraints of no-collision and vehicle dynamics, the proposed method can ensure that the optimal lane-changing trajectory can be found in different lane-changing scenarios. To verify the feasibility of the above planning algorithm, this paper designs the lateral and longitudinal controllers for trajectory tracking control based on the vehicle dynamic tracking error model.

LiDAR and camera fusion have emerged as a promising approach for improving place recognition in robotics and autonomous vehicles. However, most existing approaches often treat sensors separately, overlooking the potential benefits of correlation between them. In this paper, we propose a Cross- Modality Module (CMM) to leverage the potential correlation of LiDAR and camera features for place recognition. Besides, to fully exploit potential of each modality, we propose a Local-Global Fusion Module to supplement global coarse-grained features with local fine-grained features. The experiment results on public datasets demonstrate that our approach effectively improves the average recall by 2.3%, reaching 98.7%, compared with simply stacking of LiDAR and camera.