International Journal of Automotive Technology最新文献

Uneven heating of the human body in the cabin is one of the main reasons for poor thermal comfort. In this study, five small thermoelectric cooling devices were used to build the automobile localized air conditioning system to improve body temperature uniformity by the method of multi-point air supply. The cooling capacity of each thermoelectric cooling device can be changed independently so the localized air conditioning could work with a non-isothermal jet air supply method to optimize each thermoelectric cooling device outlet temperature based on thermal simulation analysis results aimed at better body heat flux balance and lower power consumption. The air temperature and skin temperature test were done to verify the simulation as well. The maximum deviation of the predicted stable air temperature was 0.82 ℃. The maximum deviation of the predicted skin temperature was 1.83 ℃. The subjective evaluation experiment of human thermal comfort was carried out, and the average overall thermal comfort vote of the volunteers was changed from 1.02 to − 0.44 after the localized air condition turning on, which showed that the temperature adjustment had a good effect on improving the heat balance and the thermal comfort of the human body.

Pure pursuit tracking algorithms are a popular control method in the field of autonomous navigation, where the selection of a look-ahead point plays a crucial role in tracking performance. However, the computation of the look-ahead point involves issues that are challenging to describe precisely using mathematics. To enhance the tracking precision of vehicles on curved trajectories, we propose an improved optimal look-ahead point path tracking algorithm. This algorithm primarily seeks the optimal look-ahead point by considering both longitudinal look-ahead distance and lateral position offset. To begin, we employ the Deep Deterministic Policy Gradient (DDPG) algorithm to train vehicles to determine the optimal longitudinal look-ahead distance under various constant curvature and velocity conditions. Subsequently, by utilizing the optimal longitudinal look-ahead distance and the front-wheel steering angle, we construct a lateral deviation search region. Finally, we use an evaluation function to search for the optimal look-ahead point within this region. Simulation tests demonstrate that the proposed algorithm significantly improves tracking accuracy under varying curvature trajectory conditions.

To create advanced lithium-ion battery packs (BP) that are both lightweight and durable in crashes, an innovative honeycomb BP design has been developed. This design involves inserting cylindrical lithium-ion battery cells into a honeycomb cell core, eliminating the need for traditional modules. To reduce the weight of BP, collision analyses using the finite element method (FEM) are conducted with various thickness-to-length ratios for the honeycomb cell structures. A new mathematical formula is developed to calculate the energy absorption rate per unit volume and compared with the FEM results. Based on the formula, the optimal thickness-to-length ratio is determined. Furthermore, a new method to capture effective mechanical properties for the integrated battery cells with honeycomb cells is developed using the optimal thickness ratios and a modified rule of mixture. To enhance the collision safety of the honeycomb BP, its dimensions have been optimized by performing transient FE analyses while colliding with a rigid pillar on its one edge. A weight reduction of approximately 23.7% has been achieved.

The effects of compression ratio and injection timing on a direct injection spark ignition hydrogen engine under various excessive air ratios were analyzed using a 0.4-L single-cylinder engine in this study. The engine speed was set to 1500 rpm, and the excessive air ratio was changed by controlling the amount of injected hydrogen under wide-open throttle conditions. The compression ratio was changed from 10, 12, and 14 and the injection timing was varied from BTDC 200, 160, 120°CA. The results revealed that for a compression ratio 14 at a rich limit, late injection timing reduced knocking incidence by taking advantage of stratified mixtures combustion and increased indicated thermal efficiency by reducing combustion loss while producing lower NOx emissions. For compression ratio 14 at an excessive air ratio of 2.2, late injection timing increased indicated thermal efficiency by reducing both combustion and heat losses, achieving the higher indicated thermal efficiency of 42.3%. Although NOx emissions increased with the injection timing retardation, NOx emissions decreased to under 1 g/kWh under excessive air ratios above 2.5 conditions at all injection timings.

This article presents a cooperative controller that is specifically designed to enhance the stability of a distributed-drive vehicle during steering. The controller focuses on improving lateral stability during steering and achieving optimal torque allocation to meet numerous objectives. The article proposes a novel approach to improve the performance of the sliding mode controller for transverse stability control during steering. This is achieved by designing a fractional-order non-singular fast terminal sliding mode surface function, a fractional-order double-power exponential convergence law, and introducing a weighted integration term. Furthermore, the vehicle’s torque was fine-tuned by employing an ant colony optimization (ACO) technique within the acceptable range defined by the lateral and longitudinal control requirements. To prevent the ACO algorithm from being stuck in local optima, a pseudo-random rule was implemented based on the original state transfer probability. This rule helps accelerate the convergence of the algorithm. Additionally, an elite approach and a dynamic change strategy for pheromone concentration were devised. Ultimately, the performance of the co-controller that was built is evaluated by simulation experiments conducted under both accelerated and decelerated driving situations. The test findings indicate that the technique effectively improves the lateral stability, tracking control, and energy economy of electric cars, with promising potential for practical use.

A second-order RC equivalent circuit model was established to accurately estimate the state of charge (SOC) of power lithium battery. The model parameters were identified online using the recursive gradient correction (RGC) algorithm, enhancing the real-time performance of parameter identification. Building on the unscented Kalman filter (UKF) algorithm, a multi-innovation unscented Kalman filter (MIUKF) algorithm was proposed by incorporating the multi-innovation identification theory. This approach overcomes the impact of ignoring historical errors in traditional Kalman filter algorithms on estimation accuracy, thereby accelerating the algorithm’s convergence to the true value and improving its accuracy and stability. The algorithm was validated under various operating conditions. The results indicate that, compared to the UKF algorithm, the MIUKF algorithm exhibits superior performance in estimation accuracy and anti-interference capability, enabling precise SOC estimation for lithium batteries in vehicles.

In the field of autonomous driving, the perception of the environment plays a crucial role, serving as a fundamental component. Accurate and precise environmental detection is vital in providing detailed information about obstacles for the control module of autonomous vehicles. MEMS LiDAR, as a prevalent sensor for acquiring obstacle positions, offers high accuracy in data acquisition by leveraging its dense point cloud information. However, a characteristic of MEMS LiDAR is the decrease in cloud density as the distance increases. Failure to consider this issue can lead to problems such as merging or splitting of obstacles during the clustering process. Furthermore, relying solely on a two-dimensional grid-based approach poses challenges when it comes to detecting overhanging obstacles. To overcome these challenges, we propose a method that tackles the problems of undistinguishable adjacent obstacles, splitting of distant obstacles, and the detection of overhanging structures. First, we apply ground segmentation techniques to remove ground-based points from the point cloud data. This step helps in isolating the obstacles of interest and improving the accuracy of subsequent analysis. Next, we create a three-dimensional grid map and determine the occupancy of each grid cell. To optimize the problem of distant obstacle splitting, we employ a dilation algorithm to expand the occupancy of the grid cells. Subsequently, we convert the three-dimensional grid into a two-dimensional representation and evaluate the occupancy of each cell in the resulting grid based on the height direction occupancy. Furthermore, we employ noise removal techniques to enhance the quality of the data. Finally, we utilize the DBSCAN algorithm, which incorporates an adaptive radius and eight-neighbor cells clustering algorithm, to perform obstacle clustering operations. Comparing our proposed method with the traditional DBSCAN algorithm, we observed that our method achieved a 7.6% increase in detection accuracy, while reducing calculation time by 16.2%.

Vision-based driver monitoring, a non-invasive method designed to identify potentially dangerous operations, has attracted increasing attention in recent years. In this study, a head pitch angle detection method was established to evaluate the driver’s drowsiness. Rather than employing the front facial landmarks to estimate head pitch angle, the proposed method measure this angel directly from driver’s profile face. To meet the requirement of real-time detection, the method applies the YOLOv8 network of single-stage detection and utilizes MobileNetV3 and FasterNet for lightweight improvement. The detector is trained with re-labeled CFP datasets, and real-time speed tests have been performed. Results demonstrate that the non-improved detector can achieve an mAP50 of 97.3% of the keypoints in a single frame, meanwhile realizing the frame rate of 30.41 FPS. After improvement, parameters of the model have been reduced by 21.3% and 40.9% respectively, while the frame rate can be increased to 37.13 FPS and 52.70 FPS, and the mAP50 of keypoints is increased by 0.41% and 0.51%. The results during the in-car experiment have proved that the developed detection method can effectively evaluate the head pitch angle, thus detect the driver’s drowsiness. We provide open-access to the annotated data and pre-trained models in this study.

Driving under conditions of cognitive overload or drowsiness poses serious safety risks and is recognized as a major cause of vehicle collisions. Thus, timely detection of the driver’s state is crucial for preventing accidents. This study proposed the utilization of electrocardiography (ECG) data in conjunction with multi-layered neural network (MNN) models to determine the driver’s state. ECG signals were obtained from 67 participants during simulated driving scenarios that induced either cognitive load or drowsiness. The study considered five driver states: drowsiness, fighting-off drowsiness, normal, medium cognitive load, and high cognitive load. Statistical analysis revealed significant changes in ECG measurements as the driver’s attentiveness levels varied from low (drowsiness) to high (cognitive overload). Multiple MNN models were developed to address individual variations in heart response and achieved classification accuracies exceeding 95%. These findings demonstrated the potential of ECG signal utilization for driver’s state detection to prevent vehicle accidents.

The formation transformation in intelligent connected autonomous vehicles (CAVs) enhances platoon versatility and significantly improves traffic efficiency. Current formation control strategies for CAV platoons often focus on fixed formation scenarios. This paper proposes a three-layer architecture for platoon reconfiguration, encompassing discrete, offline, and online layers. CAV platoons utilize this architecture to transform their existing formation into a specified target formation from the Intelligent Transportation System (ITS). In the discrete layer, we propose a formation representation scheme and design A* and cooperative sorting algorithms to achieve the optimal intermediate formation sequence. Moving to the offline layer, we design a Signal Temporal Logic-based model predictive control algorithm (MPC). This algorithm plans continuous, dynamically feasible, and collision-free safe trajectories, which are stored in an offline trajectory database. In the online layer, we design a successive linearization-based MPC to track the offline trajectories in real-time traffic environments and accomplish the platoon reconfiguration task. We implement single-lane and multi-lane platoon reconfiguration tasks in the MATLAB platform, comparing them with two advanced platoon reconfiguration algorithms. The experimental results, demonstrating the effectiveness of the proposed approach, are presented and discussed.