Current Issue : July-September Volume : 2024 Issue Number : 3 Articles : 5 Articles
When traversing soft and rugged terrain, a planetary rover is susceptible to slipping and sinking, which impedes its movement. The real-time detection of wheel sinkage in the planetary wheel-on-limb system is crucial for enhancing motion safety and passability on such terrain. Initially, this study establishes a measurement of wheel sinkage under complex terrain conditions. Subsequently, a monocular vision-based wheel sinkage detection method is presented by combining the wheel–terrain boundary with the wheel center position (WTB-WCP). The method enables the efficient and accurate detection of wheel sinkage through two-stage parallel computation of the wheel–terrain boundary fitting and wheel center localization. Finally, this study establishes an experimental platform based on a monocular camera and the planetary rover wheel-on-limb system to experimentally validate and comparatively analyze the proposed method. The experimental results demonstrate that the method effectively provides information on the wheel sinkage of the planetary rover wheel-on-limb system, and the relative errors of the method do not exceed 4%. The method has high accuracy and reliability and is greatly significant for the safety and passability of planetary rovers in soft and rugged terrain....
EVs suffer from short driving range because of limited capacity of the battery. An advantage of EVs over internal-combustion vehicles is the ability of regenerative braking (RB). By this advantage, EVs can develop energy by RB which can be stored in the battery for later use to increase the driving range of EVs. There are different motors that can be used in EVs, and the control during RB mode is dedicated for certain motor types. However, the previous studies for EV-based IM drives consider the motor-speed control without considering its RB. This paper proposes a robust control of induction motor (IM) during RB mode of EVs. The proposed control system is simple and depends only on mathematical calculations. The obtained results confirm the effectiveness and accuracy of the suggested control strategy with a good dynamic behavior under different operating conditions. Also, the results assure the robustness of control capabilities under parameters uncertainties during the RB mode of EV-based IM drives....
Studies from around the world show that engines using biofuel, LPG, and CNG emit fewer pollutants than those using conventional fuels. Experimental research has focused on a rapid compression and expansion machine (RCEM) that resembles a compression ignition (CI) engine. It uses dual direct injection fuel, diesel and propane (DP), with propane injection timing varying from 0 to 40 before top dead center (BTDC) and diesel injection timing remaining at 10 BTDC. The compression ratio was changed at points 17 and 19 by adjusting the RCEM connecting rod. A converge simulation program was used to run the simulation model, which was used to examine how the fire and inflow inside the chamber developed. The ANN method was used to predict pressure, temperature, power, TKE, and ITE data output based on propane energy fraction, compression ratio, and SOI of propane as input data parameters. It was noticed that the ANN prediction on experimental data has a higher accuracy compared to the simulation prediction. The R and MSE values were used to identify the accuracy of the prediction on output parameter data. ANN generalization capability is comparatively high when trained with large enough databases. The highest accuracy of prediction was produced on TKE, which had an MSE of 0.003715 and R value of 0.99981 from 287900 sample data. This shows that the ANN model is quite accurate in forecasting output experimental data....
An identification technique is proposed to create a relation between the accelerator pedal position and the corresponding driving moment. This step is beneficial to replace the complex physical model of the vehicle control unit, especially when the sufficient information needed to model certain functionalities of the vehicle control unit are unavailable. We utilized the nonlinear autoregressive exogenous model to regenerate the electric motor torque demand, given the accelerator pedal position, the motor’s angular speed, and the vehicle’s speed. This model proved to be extremely efficient in representing this highly complex relationship. The data employed for the identification process were chosen from an actual three-dimensional route with sudden changes of a dynamic nature in the driving mode, different speed limits, and elevations, as an attempt to thoroughly cover the driving moment scope based on the alternation of the given inputs. Analyzing the selected route data points showed the widespread coverage of the motor’s operational scope compared to a standard driving cycle. The training outcome revealed that linear modeling is inadequate for identifying the targeted system, and has a substantial estimation error. Adding the nonlinearity feature to the model led to an exceptionally high accuracy for the estimation and validation datasets. The main finding of this work is that the combined model from the nonlinear autoregressive exogenous and the sigmoid network enables the accurate modeling of highly nonlinear dynamic systems. Accordingly, the maximum absolute estimation error for the motor’s moment was less than 10 Nm during the real-world driving maneuver. The highest errors are found around the maximum motor’s moment. Finally, the model is validated with measurements from an actual field test maneuver. The identified model predicted the driving moment with a correlation of 0.994....
The widespread use of fossil fuels in automobiles has become a concern, particularly in light of recent frequent natural disasters, prompting a shift towards eco-friendly vehicles to mitigate greenhouse gas emissions. This shift is evident in the rapidly increasing registration rates of hydrogen vehicles. However, with the growing presence of hydrogen vehicles on roads, a corresponding rise in related accidents is anticipated, posing new challenges for first responders. In this study, computational fluid dynamics analysis was performed to develop effective response strategies for first responders dealing with high-pressure hydrogen gas leaks in vehicle accidents. The analysis revealed that in the absence of blower intervention, a vapor cloud explosion from leaked hydrogen gas could generate overpressure exceeding 13.8 kPa, potentially causing direct harm to first responders. In the event of a hydrogen vehicle accident requiring urgent rescue activities, the appropriate response strategy must be selected. The use of blowers can aid in developing a variety of strategies by reducing the risk of a vapor cloud explosion. Consequently, this study offers a tailored response strategy for first responders in hydrogen vehicle leak scenarios, emphasizing the importance of situational assessment at the incident site....
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