Pneumatic ABS Modeling and Failure Mode Analysis of Electromagnetic and Control Valves for Commercial Vehicles

Li, Xiaohan; Zhao, Leilei; Zhou, Changcheng; Li, Xue; Li, Hongyan

doi:10.3390/electronics9020318

Cited by 14 publications

(6 citation statements)

References 19 publications

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“…In order to improve the control effect of the ACC system in actual driving scenarios, it is necessary to consider the influence of road surface adhesion changes on the performance of the braking system when designing the control method at the execution layer [ 28 , 29 ]. Therefore, a rule-based anti-lock braking system (ABS) control method based on the execution layer is proposed.…”

Section: Acc System Control Strategy Developmentmentioning

confidence: 99%

Adaptive Cruise System Based on Fuzzy MPC and Machine Learning State Observer

Guo

Wang

Liang

et al. 2023

Sensors

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Under the trend of vehicle intelligentization, many electrical control functions and control methods have been proposed to improve vehicle comfort and safety, among which the Adaptive Cruise Control (ACC) system is a typical example. However, the tracking performance, comfort and control robustness of the ACC system need more attention under uncertain environments and changing motion states. Therefore, this paper proposes a hierarchical control strategy, including a dynamic normal wheel load observer, a Fuzzy Model Predictive Controller and an integral-separate PID executive layer controller. Firstly, a deep learning-based dynamic normal wheel load observer is added to the perception layer of the conventional ACC system and the observer output is used as a prerequisite for brake torque allocation. Secondly, a Fuzzy Model Predictive Control (fuzzy-MPC) method is adopted in the ACC system controller design, which establishes performance indicators, including tracking performance and comfort, as objective functions, dynamically adjusts their weights and determines constraint conditions based on safety indicators to adapt to continuously changing driving scenarios. Finally, the executive controller adopts the integral-separate PID method to follow the vehicle’s longitudinal motion commands, thus improving the system’s response speed and execution accuracy. A rule-based ABS control method was also developed to further improve the driving safety of vehicles under different road conditions. The proposed strategy has been simulated and validated in different typical driving scenarios and the results show that the proposed method provides better tracking accuracy and stability than traditional techniques.

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Section: Acc System Control Strategy Developmentmentioning

confidence: 99%

Adaptive Cruise System Based on Fuzzy MPC and Machine Learning State Observer

Guo

Wang

Liang

et al. 2023

Sensors

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show abstract

“…In order to construct the state expression in the risk point of the braking system and judge the working state of the risk point, this paper consults the relevant literature on the risk point mentioned in this paper. some characteristics and indexes of related components are introduced in the braking system [26][27][28][29][30][31][32][33][34]. Based on these indicators, this paper constructs the risk point state expression.…”

Section: State Expression Of Key Risk Point State Of the Braking Systemmentioning

confidence: 99%

“…If the pressure difference is within 5 kPa, the EP valve is healthy, and if it exceeds 5 kPa, the EP valve is faulty. The state of the EP valve is represented by the mathematical expression of the In emergency braking conditions, P1 = 1, under the condition of t > 1, p ≥ 150 kPa 0, under the condition of t > 1, p < 150 kPa (27) Under non-emergency braking conditions and applying common, rapid braking: P1 = 1, under the condition of t > 1, p ≤ 50 kPa 0, under the condition of t > 1, p > 50 kPa (28) In the formula, p: the pressure of the volume chamber of the relay valve.…”

Section: Ep Valvementioning

confidence: 99%

Failure Propagation Prediction of Complex Electromechanical Systems Based on Interdependence

et al. 2023

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Interdependence is an inherent feature of the cyber-physical system. Small damage to one component in the system may affect several other components, leading to a series of failures, thus collapsing the entire system. Therefore, the system failure is often caused by the failure of one or more components. In order to solve this problem, this paper focuses on a failure propagation probability prediction method for complex electromechanical systems, considering component states and dependencies between components. Firstly, the key component set in the system is determined based on the reliability measure. Considering the three coupling mechanisms of mechanical, electrical, and information, a topology network model of the system is constructed. Secondly, based on the topology network model and fault data, the calculation method of influence degree between components is proposed. Three state parameters are used to express the risk point state of each component in the system through mathematical representation, and the correlation coefficient between the risk point state parameters is calculated and measured based on the uncertainty evaluation. Then, the influence matrix between the system risk points is constructed, and the fault sequence is predicted by using the prediction function of an Artificial Neural Network (ANN) to obtain the fault propagation probability. Finally, the method is applied to the rail train braking system, which verifies that the proposed method is feasible and effective.

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“…In active vehicle safety systems region for multi-wheeled vehicles, Mokarramet al, 35 designed an active yaw controller integrated with ABS and TCS, which improved vehicle the directional stability and traction performance and pointed out the influence of road friction coefficient on the efficiency of the controller, which limited the available braking torque. In order to study the braking performance and the rule of vehicles under the failure modes of the pneumatic ABS, Ragheb and Gindy 36 established the co-simulation model of the pneumatic ABS of the commercial vehicle based on AMESim and Simulink softwares, but did not achieve the optimization control of the ABS system. Li et al 37 designed a digital sliding mode based minimum variance control, which improved the robustness of ABS, and verified it with a single round experiment.…”

Section: Abs Control System Designmentioning

confidence: 99%

Multi-body modeling and dynamic analysis of the heavy-load multi-axle vehicle Based on 6S/6M ABS control

Gao

Cheng

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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The study investigates the braking characteristics of heavy-load five-axle special vehicle to improve the braking safety of it. The mechanical system with strong coupling is studied by means of multi-body dynamics analysis method, and the refined vehicle dynamics model including braking system, suspension system, steering system, and other components is established via platform of Adams/Car. Through road test experiments, the accuracy and reliability of the model under conditions of 80 m constant turning radius circle and emergency braking of 60 km/h initial speed are verified. The classical ABS (Anti-lock Braking System) control strategy with logic threshold including a road recognition module and a slip ratio calculation module is established in Simulink, which matches the ABS control system of 6 S/6 M for the five-axle special vehicle well. By changing adhesion conditions of a single road and the control channel modes of the split-mu road, the control effect of the ABS control system on the braking performance of the five-axle vehicle is determined by the co-simulation. The experimental and simulation results indicate the following: (1) The vehicle dynamics model can effectively simulate the structure of the actual vehicle mechanical system and mechanical constraints, and has good braking performance and steering performance; (2) ABS external control system and heavy-load five-axle vehicle model can be matched, and during the control process, logic thresholds need to be repeatedly adjusted to adapt to different types of roads. (3) The change of road adhesion coefficient will affect the control effect of ABS. The baking performance can be improved more in low adhesion road than in high adhesion road. (4) Split-mu road is more special and has a great impact on the lateral stability of braking. It can be seen through comparative analysis of high-selection and low-selection control that braking safety is better under low selection control mode.

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Pneumatic ABS Modeling and Failure Mode Analysis of Electromagnetic and Control Valves for Commercial Vehicles

Cited by 14 publications

References 19 publications

Adaptive Cruise System Based on Fuzzy MPC and Machine Learning State Observer

Adaptive Cruise System Based on Fuzzy MPC and Machine Learning State Observer

Failure Propagation Prediction of Complex Electromechanical Systems Based on Interdependence

Multi-body modeling and dynamic analysis of the heavy-load multi-axle vehicle Based on 6S/6M ABS control

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