The optimal initial pre-conditioning parameter is essential to properly adjust the temperature within the cabin in an effective and accurate way, especially while passengers' thermal comfort and ...energy-saving properties are both considered. Under the various environmental thermal loads, the pre-conditioning solutions resulting from those pre-fixed cooling parameters are unfeasible for achieving accurately passengers' comfort temperature. In addition, it is also difficult in such a narrow car space to identify a lot of local attributes due to the different material properties and sizes of a variety of structural parts that have various thermal responses to environmental conditions. This paper presents a data-driven decision model to numerically identify the degrees of the cabin thermal characteristic to determine satisfactory pre-conditioning parameter schemes. Initially, based on the thermal data within a vehicle recorded through the whole year at a selected hot climate region of the Middle East, the study levels multiple climate scenes corresponding to change in the cabin air temperature. Then three classification algorithms (Support Vector Machines, Decision Tree, and K-nearest neighbor model) are used to comparatively identify climate levels according to the input conditions. Based on the identified climate level, an appropriate parameters scheme for this level is applied. A comprehensive evaluation index (CEI) is proposed to characterize the passengers' satisfaction in numerical computation, on considering multi-satisfaction objectives including Predicted Mean Vote (PMV), local temperature, air quality, and energy efficiency; and it formulates the pre-conditioning parameter scheme for each climate scene with CEI. Several scene cases are carried out to verify the effectiveness of the proposed models. The result shows that the pre-conditioning schemes of the model can effectively satisfy passengers in multi-satisfaction objectives.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
It is a challenging task to segment drivable area of road in automatic driving system. Convolutional neural network has excellent performance in road segmentation. However, the existing segmentation ...methods only focus on improving the performance of road segmentation under good road conditions, but pay little attention to the performance of road segmentation under severe weather conditions. In this paper, an image enhancement network (IEC-Net) based on CycleGAN is proposed to enhance the diversified features of input images. Firstly, an unsupervised CycleGAN network is trained to feature enhance road images under severe weather conditions, so as to obtain an enhanced image with rich feature information. Secondly, the enhanced image is input into the most advanced semantic segmentation network, so as to realize the segmentation of the drivable area of the road. The experimental results show that the IEC-Net based on CycleGAN can be directly combined with any advanced semantic segmentation network and can not only realize end-to-end training, but also greatly improve the performance of the original semantic segmentation network for road segmentation under severe weather conditions.
The external climate consistently maintains thermal transfer with the vehicle cabin. The complexity and variability of the urban microclimate cause dynamic changes in the thermal characteristics of ...the cabin. Combining the differences in spatial structure and environmental characteristics of urban roads, this study proposed a cabin thermal management method based on matching cooling schemes for different urban microclimates in road space, aiming to achieve thermal comfort and low energy consumption. Environmental load spectrum tests were conducted in three types of urban road spaces during the summer in southern China, including open roads, boulevards, and underground parking lots. The primary environmental loads experienced by the vehicle in service are solar radiation, high temperature, and high humidity. However, these loads vary significantly due to factors such as cloud cover, building and tree shading, and tree transpiration. Combined with the cabin thermal model, the study analyzed the effect of environmental differences in road scenes on the dynamic thermal characteristics of the vehicle cabin, and explored the variability of passengers’ cooling requirements. The results indicated that the HVAC air supply temperatures of 10.5°C, 15.8°C and 20.5°C should be respectively adopted on the open road, boulevard and in the underground parking lot.
Internal short circuits and thermal runaway in lithium-ion batteries (LIBs) are mainly caused by deformation-induced failures in their internal components. Understanding the mechanisms of mechanical ...failure in the internal materials is of much importance for the design of LIB pack safety. In this work, the constitutive behaviors and deformation-induced failures of these component materials were tested and simulated. The stress-strain constitutive models of the anode/cathode and the separator under uniaxial tensile and compressive loads were proposed, and maximum tensile strain failure criteria were used to simulate the failure behaviors on these materials under the biaxial indentations. In order to understand the deformation failure mechanisms of ultrathin and multilayer materials within the prismatic cell, a mesoscale layer element model (LEM) with a separator-cathode-separator-anode structure was constructed. The deformation failure of LEM under spherical punches of different sizes was analyzed in detail, and the results were experimentally verified. Furthermore, the n-layer LEM stacked structure numerical model was constructed to calculate the progressive failure mechanisms of cathodes and anodes under punches. The results of test and simulation show the fracture failure of the cathodes under local indentation will trigger the failure of adjacent layers successively, and the internal short circuits are ultimately caused by separator failure owing to fractures and slips in the electrodes. The results improve the understanding of the failure behavior of the component materials in prismatic lithium-ion batteries, and provide some safety suggestions for the battery structure design in the future.
In this paper, a novel motion control scheme with guaranteed prescribed performance is proposed for autonomous vehicles with consideration of couplings between lateral and longitudinal motions. ...Firstly, a prescribed performance transformation function is constructed to equivalently release the output error constraints. Then, a coordinated controller is designed to complete lateral and longitudinal motion control tasks simultaneously based on sliding-mode control. The designed coordinated controller can guarantee predefined trajectory tracking performance (e.g., minimum speed of convergence, maximum steady state error and overshoot), in presence of strong-coupled characteristics, model uncertainties and external disturbance. Finally, simulation results under different boundary constraints further validate the feasibility and good robustness of the controller.
With the different physiological properties and thermal conditions, different body parts of passengers have inconsistent thermal sensations and thermal requirements in a highly non-uniform and ...transient vehicle cabin thermal environment. Determining the thermal comfort requirements for different body parts of a passenger is essential for effectively supplying warm air to the right human part especially for electric vehicles with energy-saving attributes. In this paper, a comprehensive numerical model that integrates human thermal regulation mechanism and dynamic environmental characteristics is established to calculate the thermal comfort for passengers via thermal responses to a dynamic environment. The numerical computation sets up such a model structure, firstly considering human thermal regulation functions into the thermal response to the in-cabin dynamic thermal distributions, then combining Berkeley thermal comfort model to identify the thermal comfort level at different body parts, that would implement total numerical simulations to get thermal comfort evaluation, independent of human subjective feedbacks. The model is validated by experiments with an acceptable error and implemented for a cabin heating case study. The models can effectively predict the thermal comfort and thermal requirements of various body parts in a dynamic environment with human thermoregulation, as an important tool for designing a non-uniform environment.
High-tilt reclined seats are one of the most popular configurations in highly automated vehicles; however, current restraint systems cannot protect out-of-position occupants in this type of seat. To ...reduce the risk of injury to reclined occupants, this study proposes a swiveling seat driven by occupant inertia and rotated in the sagittal plane during impact. The effectiveness of the swiveling seat was evaluated based on kinematics and injury to a human biomechanical model in a frontal sled test. A simulation matrix was constructed to design and optimize various safety devices, including the belt, pre-tensioner, knee constraint, and rotation stiffness for the swiveling seat. The results showed that (1) submarining occurred when the reclined occupant was on a fixed seat with a normal three-point belt during impact; (2) a fixed seat with a dynamic locking tongue and passenger lap pretension prevented the submarining, but produced a high lumbar force of 5359 N, which was higher than the spine injury criterion; and (3) the proposed swiveling seat with a matched restraint system could prevent submarining and produce lumbar force of 1787 N. The results demonstrated that the swiveling seat has high potential for occupant protection in intelligent driving scenarios.
Among the shortcomings of the A* algorithm, for example, there are many search nodes in path planning, and the calculation time is long. This article proposes a three-neighbor search A* algorithm ...combined with artificial potential fields to optimize the path planning problem of mobile robots. The algorithm integrates and improves the partial artificial potential field and the A* algorithm to address irregular obstacles in the forward direction. The artificial potential field guides the mobile robot to move forward quickly. The A* algorithm of the three-neighbor search method performs accurate obstacle avoidance. The current pose vector of the mobile robot is constructed during obstacle avoidance, the search range is narrowed to less than three neighbors, and repeated searches are avoided. In the matrix laboratory environment, grid maps with different obstacle ratios are compared with the A* algorithm. The experimental results show that the proposed improved algorithm avoids concave obstacle traps and shortens the path length, thus reducing the search time and the number of search nodes. The average path length is shortened by 5.58%, the path search time is shortened by 77.05%, and the number of path nodes is reduced by 88.85%. The experimental results fully show that the improved A* algorithm is effective and feasible and can provide optimal results.
Picking robots have become an important development direction of smart agriculture, and the position detection of fruit is the key to realizing robot picking. However, the existing detection models ...have the shortcomings of missing detection and slow detection speed when detecting dense and occluded grape targets. Meanwhile, the parameters of the existing model are too large, which makes it difficult to deploy to the mobile terminal. In this paper, a lightweight GA-YOLO model is proposed. Firstly, a new backbone network SE-CSPGhostnet is designed, which greatly reduces the parameters of the model. Secondly, an adaptively spatial feature fusion mechanism is used to address the issues of difficult detection of dense and occluded grapes. Finally, a new loss function is constructed to improve detection efficiency. In 2022, a detection experiment was carried out on the image data collected in the Bagui rural area of Guangxi Zhuang Autonomous Region, the results demonstrate that the GA-YOLO model has an mAP of 96.87%, detection speed of 55.867 FPS and parameters of 11.003 M. In comparison to the model before improvement, the GA-YOLO model has improved mAP by 3.69% and detection speed by 20.245 FPS. Additionally, the GA-YOLO model has reduced parameters by 82.79%. GA-YOLO model not only improves the detection accuracy of dense and occluded targets but also lessens model parameters and accelerates detection speed.
It is difficult to comprehensively master and precisely regulate the external factors distribution of automobile weathering in non-uniform thermal environment as well as the consequent disequilibrium ...weathering problem, while exploring weather-resistant materials in uniform thermal environment. Thus, a numerical calculation method for the weathering external factors is proposed, on the basis of annual experimental study on the outdoor weathering inconsistencies of auto-parts. The time–space distribution characteristics and day–night variation rules of the external factors are studied, and the disequilibrium weathering mechanism among parts is revealed from the perspective of non-uniform distribution of external factors. The laws of automotive physical parameters, orientations and locations, as well as their influences on external factors distribution are analyzed in detail, and hereby the targeted schemes to effectively reduce the local external factor intensity and the thermal gradient between parts are investigated. The method can be used to rapidly predict weathering external factors distribution of vehicle exposed to outdoor in any direction during day and night, so as to provide auto-parts with differentiated test schemes in accelerated tests and IP/DP box tests, and it also contributes to present some pertinence guidance for the accurate regulation of automobile disequilibrium weathering on regions at different levels.
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