Jiangling Motors Corporation (China)

Braking drag is a typical fault of brake systems, and clarifying the correlation mechanism between vehicular working conditions and braking drag is critical for brake design improvement. Based on fluid mechanics and contact mechanics, this paper establishes a dynamic model for braking drag mechanism analysis, combined with the return mechanism and force-bearing state of brake pistons. Firstly, a commercial vehicle brake system dynamic model is built via Amesim, and piston sliding resistance is identified as the key factor leading to insufficient piston retraction through user operational data analysis. Subsequently, a fluid-structure interaction-based dynamic coupling model of drag mechanism is established, typical braking conditions are extracted via K-means clustering, and piston friction, displacement and drag torque are solved with the system model outputs as inputs. Finally, drag-prone working conditions are determined, and the disc brake drag mechanism is revealed. The results show that piston sliding resistance is the primary factor in braking drag; medium-low speed prolonged braking has high drag susceptibility; and the seal contact area is in mixed lubrication, with contact pressure and friction dominated by asperity shear stress. This work enables accurate identification of drag-prone conditions, providing guidance for brake system optimization.

CO2 methanation offers a promising technology to convert CO2 into methane, a valuable fuel that can be integrated into existing gas infrastructure. However, developing cost-effective, highly active, and stable catalysts remains a key challenge. In this paper, a series of La/NiAl-LDO catalysts were synthesized via a coprecipitation–impregnation method for catalytic CO2 hydrogenation. Among the prepared catalysts, 6La/NiAl-LDO exhibited the highest CO2 conversion (85.6%) with nearly 100% CH4 selectivity at 300 °C and 2 MPa. The catalyst also demonstrated excellent stability over a 100 h durability test. Moreover, the kinetics of CO2 hydrogenation over a 6La/NiAl-LDO catalyst were studied in a fixed-bed reactor at a catalyst particle size of 20–40 mesh, space velocity of 8000 mL/(g·h)), and temperatures ranging from 260 to 300 °C. The overall positive reaction followed approximately first-order kinetics, with an apparent activation energy of 89.4 kJ/mol. This work contributes to broader efforts in CO2 capture and conversion to synthetic natural gas.

The current general A* algorithm has problems such as excessive path points, long search time, and ignoring the vehicle's motion characteristics. To tackle these challenges, an enhanced A* algorithm is developed for path planning of autonomous vehicles in 5G network environments. Initially, the search neighborhood of the conventional A* algorithm is broadened to expand the set of available search directions and eliminate redundant waypoints. Secondly, considering the visibility of the target, the algorithm can actively terminate the A* search process under certain conditions, saving search time. Finally, combined with the driving requirements of unmanned vehicles under actual conditions, a path optimization method is adopted to optimize the path, improving the robustness of the planned path and generating a smooth path suitable for unmanned vehicle driving. Simulation experiments were conducted in a grid map environment. The experimental results show that compared with the traditional A* algorithm, the proposed algorithm reduces the number of path points by approximately 63%, eliminates all turning points, and reduces collision events by 100%. In complex obstacle environments, the number of waypoints is reduced by approximately 65%, and turning points and collision events are eliminated.

<div class="section abstract"><div class="htmlview paragraph">In order to make lateral motion robust and stable for smart chassis, a lateral motion controller is proposed in this paper taking structural uncertainty into account. Firstly, a new lateral error model is developed to describe the lateral motion problem. Secondly, the kernel of the lateral motion controller is active disturbance rejection control method, that is, a second order linear tracking differentiator is embedded between lateral error model and first order linear active disturbance rejection control (FO-LADRC). And every module of the lateral motion controller has been designed in detail, which contains first-order linear tracking differentiator (LTD1), second-order linear tracking differentiator (LTD2), linear extended state observer (LESO), and linear state error feedback (LSEF). Finally, six typical scenarios with two conditions are designed to validate the controller referencing to the test standard, considering the structural uncertainty, including wheelbase length and steering ratio. The results illustrate that the developed lateral motion controller has excellent performance in both robustness and stability compared with pure pursuit and FO-LADRC when there are structural uncertainty in wheelbase length or in steer ratio.</div></div>

Abstract. Urban traffic congestion, obstacle avoidance, and driving efficiency are the challenges faced by autonomous-vehicle path-planning technology in cities. The traditional artificial potential field (APF) algorithm is insufficient to meet the requirements of efficiency and safety in path planning, as it easily gets trapped in local optima when dealing with complex environments. Therefore, this paper proposes a novel AS-IAPF path-planning algorithm to more efficiently enhance the target reachability of autonomous vehicles in complex traffic environments. Firstly, this paper analyzes and elucidates the macroscopic traffic model, achieving effective modeling of dynamic traffic flow stability based on Lyapunov stability theorem and a classical 1D flow model. Thus, the threshold discriminant formula for traffic element stability is obtained. Secondly, based on the aforementioned threshold discriminant formula, a new AS-IAPF algorithm is proposed. The algorithm mainly includes two aspects: firstly, by pre-generating initial paths and introducing a Gaussian oscillation coefficient of force fields, it avoids the algorithm falling into local optima; secondly, by using the aforementioned driving stability threshold discriminant formula as a dimensional adjustment for adaptively improving and adjusting the strength coefficient of the AS-APF repulsive field, the algorithm further improves the efficiency of path planning. Finally, the algorithm is subjected to joint simulations of 2D and 3D scenarios of different types. The research results show that the AS-IAPF algorithm outperforms other algorithms of the same type with respect to comprehensive performance based on multiple 2D scenario simulation experiments. In the 3D simulation experiments of three different typical traffic scenarios, the proposed algorithm can drive autonomous vehicles to effectively perform corresponding obstacle avoidance actions based on the actual traffic scenarios ahead, ultimately achieving safe obstacle avoidance. The path-planning method proposed in this paper can enhance driving efficiency while considering the safety and stability of vehicles, providing a promising approach and reference for the path planning of autonomous vehicles.

This paper proposes a mixed-mode (combining shear and squeeze working modes) vibration isolator using magnetorheological elastomer (MRE), which enables the isolator to have a larger working area and better isolation performance by combining the working modes of the MRE. Firstly, based on the magnetorheological effect working principle of the MRE, the material selection and dimensional parameters of each component are determined through structural design and magnetic circuit calculation. On this basis, magnetic field simulation is conducted using Maxwell 16.0 software to analyze the distribution of magnetic field lines and magnetic induction in the working area. Simultaneously, equivalent stiffness and equivalent damping models are established to explore the variation of vibration response with external current and excitation frequency conditions. Finally, a vibration isolation experimental platform is built to test the mixed-mode MRE isolator. The experimental results are basically consistent with the simulation modeling results. The experimental results showed that when the external excitation is in the frequency range of 16 Hz, effective semi-active vibration isolation control could be achieved by applying different current inputs. The isolation effect of the system is difficult to effectively control using current input when the external excitation is at high frequency. These results validate the rationality and feasibility of the mixed-mode MRE isolator structure, which provides a good reference for the design of MRE isolators.