International Journal of Automotive Technology最新文献

This study aims to provide a detailed evaluation and comparison of the performance of forward collision warning (FCW) and automatic emergency braking (AEB) systems in lane-changing scenarios, focusing on their detection range and detection angles. Real-world tests were conducted with a Tesla Model 3 and a KIA K8 to assess their detection capabilities. The experiments simulated common highway lane-changing scenarios, referencing Euro NCAP standards. Testing environments included a full-size target robot and a guided vehicle target to ensure accuracy. Preliminary tests established the test speed range and relative distances, while main tests focused on three key variables: time-to-collision (TTC) for FCW activation, TTC for AEB activation, and relative lateral positions of the target and test vehicles. The study also analyzed collisions despite FCW and AEB activation, identifying system limitations by examining deviations in TTC values and their correlation with collisions. These findings provide insights into the effectiveness and reliability of FCW and AEB systems under various conditions, aiding the advancement of ADAS technologies.

Vehicle thermal comfort has received more attention due to advancements in autonomous driving and intelligent cabin technology. Prediction of thermal comfort is challenging due to the passenger compartment's complex transient non-uniform thermal environment. Many thermal comfort models are primarily based on environmental or human thermal physiology factors, but too many temperature measurements may affect driving behavior. This study analyzed the correlations between local thermal sensation (LTS), local thermal comfort (LTC), the thermal environment in an automobile's cabin, and skin temperature. The optimal combination of influencing factors was established in the prediction model of overall thermal sensation (OTS) and overall thermal comfort (OTC) in the vehicle cabin. The results indicated that breathing air and chest skin surface temperature had the best correlation with subjective human evaluation. The prediction models of OTS and OTC have good prediction performance, and their R² values are 0.77 and 0.51, respectively. Accurately predicting the thermal comfort in the vehicle provides a valuable reference for intelligent cabin thermal environment control and automobile energy savings.

An automated valet parking system, a critical element of autonomous driving technology, greatly enhances parking efficiency and reduces driver stress. In the realm of path-planning in tight spaces, traditional Hybrid A* algorithms often produce numerous invalid expansion nodes, leading to increased computational load and decreased spatial utilization efficiency. This study introduces a new path-planning approach that combines the Hybrid A* algorithm with geometric curves to overcome these challenges. Initially, the Hybrid A* algorithm conducts global path-planning to ensure the viability of the route from the parking entrance to the desired spot. Subsequently, a genetic algorithm optimizes the driving path locally, improving safety and efficiency. Finally, Bézier and clothoid curves are utilized to smooth the path, further enhancing planning efficiency and driving safety. Experimental results show that the proposed approach surpasses existing methods in terms of planning time efficiency and path quality.

To further improve the accuracy of the complex multi-physics coupling system of flat wire motors, this paper presents an improved multi-physics field modeling method, which conducts a relatively comprehensive analysis of electromagnetic(EM), temperature, flow and stress field. And the multi-field coupling global model is simplified based on the analysis of two-field coupling relationships. First, each two-field coupling sub-model of four key physical fields is analyzed by bidirectional coupling and the weak coupling way is ignored. Secondly, based on the analysis results of the two-field coupling sub-model, the coupling relationship between electromagnetic field, temperature field and flow field is simplified and the global coupling model of electromagnetic field, temperature field and flow field inside the motor is established. Finally, because the stress field of the motor rotor is unidirectional influced by the temperature field, the rotor strength of the high-speed motor is analyzed based on temperature field to stress field coupling. Compared with the calculation results of EM and temperature two-field coupling, the accuracy of electromagnetic torque under multi-field coupling is increased by 4.4%, the calculation accuracy of electromagnetic loss is also increased by 2.1%. And the calculation accuracy of the motor temperature field is increased by 4.5%.

To provide the driver with a more realistic and comfortable driving experience, a novel road feel torque planning method based on rack force estimation and an active return control method for the steering wheel with disturbance observation are proposed for intelligent vehicle. First, for road feel feedback during steering, an improved reduced order extended state observer is designed to estimate the rack force, a secondary filter filters the rack force, obtaining the alignment torque, and superimposing the assist, inertia, damping, friction, and limiting torques to replicate the road feel of the electric power steering system. Second, a proportional-integral observer is designed to observe the lumped uncertainties in the steering wheel system and introduce the observation value into the backstepping controller for active return control of the steering wheel. Finally, an integral sliding mode controller is designed to control the road feel motor to achieve accurate feedback of road feel torque. The virtual simulation results show that the observation effect of the proposed observer is better, the designed road feel torque meets the requirements better; the proposed active return controller can achieve accurate return of the steering wheel, and the sliding mode controller achieves more accurate tracking of the road feel torque.

This study investigates the effects of various bore–stroke (S/B) ratios on the combustion characteristics, energy fractions, and performance of a hydrogen direct injection spark ignition engine equipped with a variable valve timing (VVT) system under low-load conditions. The experiments were conducted at S/B ratios of 1.0, 1.2, and 1.47 while maintaining a fixed displacement volume and compression ratio. The energy budget analysis focused on heat transfer loss, combustion loss, and exhaust loss to determine their effects on gross work. The results showed that as the S/B ratio increased, heat transfer loss increased due to enhanced piston speed and in-cylinder mixing, resulting in faster combustion. Combustion loss was highest at an S/B ratio 1.0 due to longer combustion duration. In contrast, exhaust loss did not show a clear trend with varying S/B ratios. The effects of fuel injection timing and excess air ratio on engine performance and emissions were investigated. The findings of this study suggest that optimizing the S/B ratio, fuel injection timing, and excess air ratio can significantly improve the thermal efficiency and emission characteristics of hydrogen engines, providing practical insights for the design and development of future hydrogen engine technologies.

High-voltage batteries used in electric vehicles use hundreds or thousands of battery cells. Because a large number of battery cells are used, installing each one into a battery pack causes many difficulties in production. Therefore, traditionally, multiple battery cells are composed of several battery modules and then assembled into a battery pack. However, recently, Cell-to-Pack (CTP) technology that configures battery cells directly into a battery pack is being developed to increase energy density of a battery pack. This is because parts needed for battery modules can be removed, which can have various advantages. Because modules are eliminated in CTP technology, the method of installing battery cells in a battery pack will also be modified and the effect battery cells have on the stiffness of a battery pack will also change. In this study, the differences in stiffness of battery packs based on CTP technology developed for various battery cell types are analyzed. In particular, battery packs with CTP technology are generated based on pouch-type battery cells and prismatic battery cells and how each type of battery cell changes the stiffness of a battery pack is analyzed.

Natural gas is an emerging alternative fuel for internal combustion engines in the transportation sector. However, the performance of natural gas engines can be significantly affected by changes in atmospheric pressure and temperature at high altitudes. To address this issue and enhance the performance of natural gas engines in plateau environments, a study focused on a two-stage turbocharged heavy-duty spark-ignition natural gas engine and its performance improvement is conducted targeting at operating altitude of 4000 m. A one-dimensional model of the engine is firstly developed and validated against experimental data at varying altitudes. The experimental and simulated data suggest engine power loss of 3% and 18% at 2500 m and 4000 m altitudes, respectively. Then, a response surface model of the engine is constructed employing the Box–Behnken experimental design method, considering optimization factors such as the compression ratio (CR), spark timing (ST), and bypass valve equivalent diameter (BVED). The objectives of the optimization are to enhance power, reduce brake specific fuel consumption (BSFC) and minimize nitrogen oxide (NOx) emissions. Finally, while adhering to engine durability constraints, the NSGA-II optimization algorithm is utilized for the multi-objective optimization. The optimization results demonstrate that at an altitude of 4000 m, the engine power recovers to approximately 86% of that at sea level, with a slight increase in BSFC and a decrease in NOx emissions. Therefore, this proposed engine optimization method effectively restores the performance of natural gas engines at high altitudes.

This study explores the thermal efficiency of high compression ratio Miller cycle engines and the impact of methanol and methanol/gasoline blends on combustion and emissions. Comparative experiments were conducted to investigate the thermal efficiencies of the Miller cycle compared to the conventional Otto cycle at different compression ratios and how methanol affects combustion and emissions. The results show that under high-speed and high-load conditions, the Miller cycle offers higher thermal efficiency and better tolerance to high compression ratios than the Otto cycle. In experiments conducted at 2000 rpm and 0.66 MPa GIMEP, using the Miller cycle with compression ratios of 11.5 and 14.5 increased thermal efficiency by about 0.6 and 0.8 percentage points compared to the Otto cycle. Using methanol/gasoline blends can advance the combustion phase without changing the load, further improving the engine’s thermal efficiency. Burning pure methanol under heavy load significantly improves combustion; it increases in-cylinder pressure by about 30%, thermal efficiency by 7.2 percentage points, and NOx emissions by 80% compared to gasoline. Furthermore, using methanol fuel significantly increases nucleation mode particles and decreases accumulation mode particles, with peak values shifting to smaller diameters.

Effective driving decision-making significantly enhances the safety of automated commercial vehicles. Different from small passenger vehicles mainly focusing on anti-collision, the inducements of collision and rollover for commercial vehicles are coupled with each other. However, these factors are not considered together which results in a limitation in the safety performance. This paper proposes a novel comprehensive driving decision-making methodology based on deep reinforcement learning (CDDM-DRL) for automated commercial vehicles in expressway scenarios. The CDDM-DRL consists of two parts. First, a feature encoding network is designed to encode hierarchical features from traffic situations and driving conditions, which can provide more useful feature information. Then an actor–critic network incorporating ensemble methods is developed to learn and provide effective driving actions, such as whether to turn and when to turn. Finally, extensive simulations in common and challenging scenarios with different traffic densities were performed. Experimental results show that our proposed method is better than some classical DRL methods in terms of time headway, backward clearance, lateral acceleration, etc. Moreover, it can prevent collision and rollover simultaneously, and realize safe driving decision-making for automated commercial vehicles.