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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, 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.

It is recognized that the heavier vehicles, the more emissions, thus the more imperative to electrify. In this study, long haul heavy-duty trucks are referred as HDTs, which are recognized as one of the hard-to-electrify vehicle segments, though the automotive industry has gained trending advantages of electrifying both light-duty cars and SUVs. Since big rigs such as Class 8 HDTs have significant road-block challenges for electrification due to the demanding long-hour work cycles in all weathers, this study focuses on quantifying those electrification challenges by taking advantage of the public data of Class 8 tractors & trailers. Tesla Semi is the research target though its vehicle spec data is sorted out with fragmentary information in the public domain. The key task is to analyze the battery capacity requirements due to environmental temperature and inherent aging over the lifespan.

As a novel ignition technology, pre-chamber ignition can enhance ignition energy, promote flame propagation, and augment turbulence. However, this technology undoubtedly faces challenges, particularly in the context of emission regulations. Of this study, the transient characteristics of combustion and emissions in a hybrid electric vehicle (HEV) gasoline engine with active pre-chamber ignition (PCI) under the first combustion cycle of quick start are focused. The results demonstrate that the PCI engine is available on the first cycle for lean combustion, such as lambda 1.6 to 2.0, and exhibit particle number (PN) below 7×107 N/mL at the first cycle. These particles are predominantly composed of nucleation mode (NM, <50 nm) particles, with minimal accumulation mode (AM, >50 nm) particles.

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 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.

With the development of brake-by-wire technology, electro-hydraulic composite braking technology came into being. This technology distributes the total braking force demand into motor regenerative braking force and hydraulic braking force, and can achieve a high energy recovery rate. The existing composite braking control belongs to single-channel control, i.e., the four wheel braking pressures are always the same, so the hydraulic braking force distribution relationship of the front and rear wheels does not change. For single-axle-driven electric vehicles, the additional regenerative braking force on the driven wheels will destroy the original braking force distribution relationship, resulting in reduced braking efficiency of the driven wheels, which are much easier to lock under poor road adhesion conditions.

Argon Power Cycle (APC) is an innovative future potential power system for high efficiency and zero emissions, which employs an Ar-O2 mixture rather than air as the working substance. However, APC hydrogen engines face the challenge of knock suppression. Compared to hydrogen, methane has a better anti-knock capacity and thus is an excellent potential fuel for APC engines. In previous studies, the methane is injected into the intake port. Nevertheless, for lean combustion, the stratified in-cylinder mixture formed by methane direct injection has superior combustion performances. Therefore, based on a methane direct injection engine at compression ratio = 9.6 and 1000 r/min, this study experimentally investigates the effects of replacing air by an Ar-O2 mixture (79%Ar+21%O2) on thermal efficiencies, loads, and other combustion characteristics under different excess oxygen ratios. Meanwhile, the influences of varying the methane injection timing are studied.

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 Chinese government and industries have proposed strategic plans and policies for automotive renewable-energy transformation in response to China’s commitments to peak the national carbon emissions before 2030 and to achieve carbon neutrality by 2060. We thus analyze the evolution of carbon emissions from the vehicle fleet in China with our data-driven models based on these plans. Our results indicate that the vehicle life-cycle carbon emissions are appreciable, accounting for 8.9% of the national total and 11.3% of energy combustion in 2020. Commercial vehicles are the primary source of automotive carbon emissions, accounting for about 60% of the vehicle energy cycle. Among these, heavy-duty trucks are the most important, producing 38.99% of the total carbon emissions in the vehicle operation stage in 2020 and 52.18% in 2035.

Reinforcement learning is a promising approach to solve the energy management for hybrid electric vehicles. In this paper, based on the DQN (Deep Q-Network) reinforcement learning algorithm which is widely used at present, double DQN, dueling DQN and learning from demonstration are integrated; states, actions, rewards and the experience pool based on the characteristics of series-parallel multi-speed hybrid powertrain are designed; the hybrid energy management strategy based on D4QN (Double Dueling Deep Q-Network with Demonstrations) algorithm is established. Based on the training results of D4QN algorithm, multi-parameter analysis under state and action space, HCU (Hybrid control unit) application and MIL (Model in-loop) test research are conducted.

The displacement of the shaft head fails to be accurately measured while the three-axle heavy-duty truck is driving on the reinforced pavement. In order to obtain accurate fatigue load spectrum of the suspension bracket, the acceleration signals of the shaft heads of the suspension obtained by the reinforced pavement test measurement are virtually iterated as responses. A more accurate model of the rigid-flexible coupled multi-body dynamics (MBD) of the whole vehicle is established by introducing a flexible frame based on the comprehensive modal theory. Furthermore, the vertical displacements of the shaft heads are obtained by the reverse solution of the virtual iterative method with well-pleasing precision. The accuracy of the virtual iteration is verified by comparing the simulation results with the vertical acceleration of the shaft head under the reinforced pavement in the time domain and damage domain.

To improve the maneuverability and energy consumption of an electrical vehicle, a two-level speed control method based on model predictive control (MPC) is proposed for accurate control of the vehicle during downhill coasting. The targeted acceleration is planned using the anti-interference speed filter and MPC algorithm in the upper-level controller and executed using the integrated algorithm with the inverse vehicle dynamics and proportional-integral-derivative control model (PID) in the lower-level controller, improving the algorithm’s anti-interference performance and road adaptability. Simulations and vehicle road tests showed that the proposed method could realize accurate real-time speed control of the vehicle during downhill coasting. It can also achieve a smaller derivation between the actual and targeted speeds, as well as more stable speeds when the road resistance changes abruptly, compared with the conventional PID method.

With the growing prosperity of the long-distance freight and urban logistics industry, the demand for cargo trucks is gradually increasing. The connecting bracket is the critical connecting part of the truck chassis, which bears the load transmitted by the road excitation and reduces the damage to the frame caused by the load. However, the occurrence of rough road conditions is inevitable in heavy-duty transportation. In this paper, road durability tests and fatigue life analysis are carried out on the original structure to ensure the safety of the vehicle. Based on the known boundary and load constraints, a lightweight and high-performance structure is obtained through size optimization, as the original structure cannot meet the performance requirements. Firstly, the road test was conducted on the truck where the original bracket structure is located.

The fuel-saving potential of a series hybrid electric vehicle (SHEV) was investigated in this work based on the future goals and technical roadmaps proposed by China's automobile and internal combustion engine (ICE) industry. The genetic algorithm optimization method and dynamic programming energy management strategy are used to optimize the key component parameters of a typical SHEV SUV to improve the fuel economy of the vehicle. Results showed that the fuel consumption of the vehicle would be 3.24 L / 100km in 2035, which is 37.21% less than 5.16 L / 100km in 2020, following the industries’ roadmaps. The results also indicated that the improvement of the ICE’s thermal efficiency is the main reason for the decrease of the vehicle’s fuel consumption. In addition, the improvement of working points and the reduction of energy losses of the key components also contribute to the improvement of the fuel economy.

SEAT Department of SAIC Motor Vehicle Company starts innovatively applying the single motor and P2.5 configuration scheme from EDU G2(Electric Drive Unit Generation 2), which consists of six engine gears and four motor gears. EDU G2 is very compact and adaptable through the coupling design. Gear coupling make the engine and motor coordination limited, so as to the high efficiency zone of the engine and the high efficiency zone of the motor cannot match in some working conditions, which affect the performance of the vehicle. Therefore, SEAT developed the second generation of single-motor plug-in hybrid system EDU G2 Plus EDU G2(Electric Drive Unit Generation 2 Plus), which realized the decoupling design of 5 engine gears and 2 motor gears, so that the power output of engine and motor is freely. With excellent power and economic performance, the vehicle has been well received by customers.

Combustion diagnostics of highly diluted mixtures are essential for the estimation of the combustion quality, and control of combustion timing in advanced combustion systems. In this paper, a novel fast response flame detection technique based on active plasma is introduced and investigated. Different from the conventional ion current sensing used in internal combustion engines, a separate electrode gap is used in the detecting probing. Further, the detecting voltage across the electrode gap is modulated actively using a multi-coil system to be slightly below the breakdown threshold before flame arrival. Once the flame front arrives at the probe, the ions on the flame front tend to decrease the breakdown voltage threshold and trigger a breakdown event. Simultaneous electrical and optical measurements are employed to investigate the flame detecting efficacy via active plasma probing under both quiescent and flow conditions.

Multiple object detection and tracking are central aspects of modeling the environment of autonomous vehicles. Lidar is a necessary component in the autonomous driving system. Without Lidar sensors, we will most probably not see fully self-driving cars become a reality. Lidar sensing gives us high-resolution data by sending out thousands of laser signals. In advanced driver assistance systems or automated driving systems, 3-D point clouds from lidar scans are typically used to measure physical surfaces. Lidar is a powerful sensor that you can use in challenging environments where other sensors might prove inadequate. Lidar can provide a complete 360-degree view of a scene. This paper designs Lidar based multi-target detection and tracking system based on the traditional point cloud processing method including down-sampling, denoising, segmentation, and clustering objects.

As a prerequisite for energy management of hybrid vehicles, the results of speed prediction can optimize the performance of vehicles and improve fuel efficiency. Energy management strategies are usually developed based on standard driving cycles, which are too generalized to show the variability of driving conditions in different time and locations. Therefore, this paper constructs a representative driving cycle based on driving data of the corresponding time and location, used as historical information for prediction. We propose a method to construct the driving cycle based on Markov chain theory before constructing the prediction model. In this paper, multiple prediction methods are compared with traditional parametric methods. The difference in prediction accuracy between multiple prediction methods under the single time scale and multiple time scale were compared, which further verified the advantages of the speed prediction method based on Markov chain theory.