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International Journal of Automotive Technology
Journal Prestige (SJR): 0.676  Citation Impact (citeScore): 2 Number of Followers: 4  Hybrid journal (It can contain Open Access articles)ISSN (Print) 1976-3832 - ISSN (Online) 1229-9138 Published by Springer-Verlag  [2468 journals]
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- Erratum to: Novel Test Scenario Generation Technology for Performance
Evaluation of Automated Vehicle-
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Abstract: This is the Editorial Board of the International Journal of Automotive Technology. In 11th Feb, 2022 we accepted a paper entitled “Novel Test Scenario Generation Technology for Performance Evaluation of Automated Vehicle”. According to the request from the authors of this article, we make a correction announcement of the grant number. Please refer to Table 1 for details.
PubDate: 2023-12-01
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- Dual Clutch Transmission Performance Analysis Using the MGMS-GA Friction
Model: Effect of the Piston Seal-
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Abstract: The increasing demand for spontaneity, comfort, and efficiency leads to the increasing complexity of dual-clutch transmissions and their shift controls. Friction modeling of piston seals plays an essential role in achieving a better understanding of the determinant factors of energy losses and, consequently, the realization of more efficient transmission hydraulic actuators. This paper studies the performance of a dual-clutch transmission during the gear-shifting process of a vehicle power train model. The Modified Generalized Maxwell-Slip friction model with Genetic Algorithm (MGMS-GA) parameters identification is used to include the effect of seal types. The gear shift comfort analysis and evaluation, with four different seal types, has been performed based on objectification. Simulations with O-Ring, D-Ring, Bonded, and Total Control System - Polytetrafluoroethylene piston seals were performed to show the validity of the model in practical scenarios. The study shows the superiority of the MGMS-GA in representing the experimental data and enhancing gearshift control and comfort. Moreover, it showed that the Total Control System-Polytetrafluoroethylene piston seal presented the highest performance among all seal types.
PubDate: 2023-12-01
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- Improved Predictive Model of Drivers’ Subjective Perception of Vehicle
Reaction under Aerodynamic Excitations-
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Abstract: In vehicle development, rating vehicle reactions to external disturbances such as aerodynamic excitations are important for improving safety and comfort of passengers. Vehicle motion reactions under such conditions are dependent on both disturbance and drivers’ steering actions. A predictive model has been developed to correctly anticipate the drivers’ ability to identify unexpected external disturbances for straight-line, high-speed driving in a driving simulator. The measured variables were band-pass filtered to desired frequency ranges. Excess yaw and roll velocities, defined as the difference between actual rotations and rotations predicted with a dynamic model from the cause of actual steering, were calculated. The standard deviations of the measured variables in a time window around disturbances were used in a regression model to predict the driver responses. Replacing actual rotations with excess rotations reduced the importance of steering input as a predictor by approximately 2/3, thus improving the accuracy of the predictive model. The model showed the change in driver sensitivity to rotations at different frequency intervals. It also showed that only driver input in around 1 ∼ 2 Hz reduces driver sensitivity and that drivers are not necessarily sensitive to high rotational accelerations, but rather to large differences between actual vehicle yaw and roll and expected vehicle yaw and roll responses from the steering input The result from this study were later compared to succeeding on-road tests which confirmed that the predictive model was improved with the use of excess motion variables.
PubDate: 2023-12-01
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- RSM Approach to Optimize an Engine Performance Combustion and Emissions at
Part Load through Cylinder Deactivation-
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Abstract: This article compares engine performance, combustion, and emissions at part-load in cylinder deactivation (CD) mode versus the traditional spark ignition mode. This comparison demonstrates the applicability of cylinder deactivation for a compact 3-cylinder engine. Experiments have been performed on a multi-point fuel injection engine having a 1000 cc displacement and equipped with an open engine control unit. A response surface approach has been applied to design experiments and parametric engine optimization. The analysis of variance test indicates that the load is the input factor that impacts the deactivation mode most. At 3500 RPM and 45 N·m load, the engine performs best in CD mode, with BSFC, BTE, CP, HRR and UHC values of 332.805 g/kW-hr, 25.26 %, 52.51 bar, 43.24 J, and 10.38 ppm, respectively. The validation test results for CD mode show that the percentage error for the BSFC, BTE, CP, HRR, and UHC responses was found to be 2.86, 2.91, 3.01, 3.47, and 3.97, respectively, within the acceptable range. As compared to SI mode, the current analysis has discovered a considerable decrease in BSFC (11.47 %), an improvement in BTE (12.25 %), a higher CP (80.65 %), a greater HRR (91.74 %), and a decrease in UHC (95.48 %).
PubDate: 2023-12-01
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- Exhaust Emissions and Aftertreatments of Hydrogen Internal Combustion
Engines: A Review-
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Abstract: Adopting carbon-neutral fuels is paramount to aligning internal combustion engines with global efforts to mitigate the impact of global warming. Hydrogen offers distinct advantages over other renewable fuels owing to its superior combustion properties. This comprehensive review explores three fuel injection systems suitable for using hydrogen as a fuel: mixers, port injections, and direct injections. Subsequently, we examined the emission characteristics of hydrogen internal combustion engines (HICE). Although nitric oxides (NOx) is the major emission from HICE, small quantities of hydrocarbon, CO, and CO2 should be expected due to engine oil burn. Moreover, we provide a concise overview of aftertreatment options, including urea-based selective catalytic reduction (SCR) and hydrogen-assisted SCR (H2-SCR).
PubDate: 2023-12-01
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- Energy-saving Optimization of Frequency-variable Heat Pump Air
Conditioning System for Electric Vehicles Based on a Genetic Algorithm-
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Abstract: To alleviate the impact of air conditioning (AC) systems on the overall performance of electric vehicles (EVs) and realize optimal control over the AC system, an optimal energy-saving operating control strategy for AC systems was proposed in this work. The main goal of our approach was to minimize the power consumption under the premise of meeting load demands. The energy-saving optimization problem of AC systems was solved using the improved genetic algorithm (IGA) by establishing a steady-state model of a frequency-variable heat pump AC system. In the proposed IGA, sequential quadratic programming (SQP) was embedded as a local search scheme to enable high convergence rate and solving accuracy, as well as effectively solve the optimization problem of frequency-variable heat pump AC systems. According to optimization results, the energy-saving operation of an AC system can be realized by dynamically adjusting the compressor frequency, indoor-fans frequency, and outdoor-fans frequency according to the actual working conditions of the system. The proposed optimal control method for the frequency-variable heat pump AC system based on the IGA also provided a robust basis for the intelligent control of AC systems. Meanwhile, it would be a potential solution to extend the driving ranges of EVs.
PubDate: 2023-12-01
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- Development of Sensor for the Real-time Monitoring of Brake Pad Wear and
Brake Disc Temperature in High Temperature-
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Abstract: As the technology for high-performance automobiles is developing rapidly together with the significant growth of the automobile industry in modern society, customer demand for automobile safety is increasing accordingly, and thus the development of safety products is gaining momentum. The brake system is indispensable for safe driving of the automobile, and malfunction or disorder of the brake system can lead to serious accident, resulting in injury or death. Meanwhile, if the brake system can be monitored on a real-time basis, such monitoring system can notify drivers of dangerous circumstance, like excessive wear of brake pad, or overheating of brake disc. However, in the case of high-performance/oversize automobiles, frequent or sudden braking during summer causes disc temperature to rise above 600 °C, which affects the useful life of the sensor. In contrast, there has not yet been a breakthrough for the sensor that can be used in a condition that exceeds 600 °C. Based on the heat transfer theory, we have developed the sensor in this study that normally works under the temperature exceeding 600 °C and verified that the sensor works normally under the temperature of ≥825 °C.
PubDate: 2023-12-01
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- Multi-objective Velocity Trajectory Optimization Method for Autonomous
Mining Vehicles-
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Abstract: Autonomous mining transportation is an intelligent traffic control system that can provide better economics than traditional transportation systems. The velocity trajectory of a manned vehicle depends on the driver’s driving style. Still, it can be optimized utilizing mathematical methods under autonomous driving conditions. This paper takes fuel and electric mining vehicles with a load capacity of 50 tons as the subject. It contributes a multi-objective optimization approach considering time, energy consumption, and battery lifetime. The dynamic programming (DP) algorithm is used to solve the optimal velocity trajectory with different optimization objectives under two types of mining condition simulation. The trajectories optimized by the single objective, energy consumption, usually adopt the pulse-and-gliding (PnG) approach frequently, which causes the battery capacity loss and increases the travel time. Hence, a multi-objective optimization approach is proposed. For electric vehicles, trajectories optimized by the multi-objective approach can decrease the battery capacity loss by 22.01 % and the time consumption by 41.28 %, leading to a 42.12 % increment in energy consumption. For fuel vehicles, it can decrease the time consumption by 32.54 %, leading to a 7.68 % increment in energy consumption. This velocity trajectory is smoother with less fluctuation. It can better meet the requirements of mining transportation and has a particular reference value for optimizing autonomous transportation costs in closed areas.
PubDate: 2023-12-01
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- Survey of Technology in Autonomous Valet Parking System
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Abstract: This paper introduces the definition of an autonomous valet parking system, standardization trends, processes designed to implement the system. Autonomous valet parking is a system in which a vehicle can be parked in a parking space without a user, and can also be moved to a specific location when the user wants to. The autonomous valet parking system is being developed by dividing it into search driving process, autonomous parking process, and return driving process. Currently, the autonomous valet parking system can demonstrate the entire parking process in a specific scenario. However, there are limitations, i.e., the system requires high costs, and some technologies do not show stable results. In this paper, we have highlighted the problems that should be solved to complete the autonomous valet parking system and the technologies for solving these problems. From this paper, researchers will be able to learn about the technical aspects and the developmental direction of the autonomous valet parking system.
PubDate: 2023-12-01
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- Nonstandard Backstepping Based Integral Sliding Mode Control of
Hydraulically Actuated Active Suspension System-
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Abstract: In this paper, the integral sliding mode control (ISMC) with non-standard backstepping is utilized for designing an automotive active suspension system hydraulic actuator. The main objective of this design is to make the suspension system’s ride more comfortable while keeping the road holding and rattling space within safe bounded limits. The controller design consists of applying the ISMC to perform a virtual control force, that meets all suspension requirements, besides utilizing a hydraulic model by a non-standard backstepping control algorithm taking into consideration the uncertainty and nonlinearity of the hydraulic system. The main advantage of ISMC is to have a robust controller, such that the stability of the system appears from starting its states at the switching surface where system nonlinearity, parameter changes, and road disturbances are rejected by a discontinuous control term present strongly in the suspension dynamics. This work demonstrates the effectiveness of the present controller design through the simulation of a 2-DOF quarter car system equipped with a passive suspension. The results vividly showcase how the current design enhances the overall performance of the system.
PubDate: 2023-12-01
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- Electric Vehicle Battery State of Charge Prediction Based on Graph
Convolutional Network-
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Abstract: The state of charge (SoC) of a vehicle battery can tend to vary depending on the driver’s driving patterns and circumstances. To accurately predict the SoC level, it is necessary to consider various circumstances. That is why traditional statistical models may not be sufficient. To address this issue, recurrent neural network (RNN) models have been proposed for time series prediction tasks due to their superior performance. In this paper, we propose a new approach using a graph convolutional network (GCN)-based model that shows better performance than RNN-based models. The GCN requires an adjacency matrix as input, which represents the relationships between variables. We set this matrix to be learnable during model training rather than predefined. We also use two different adjacency matrices: one with variables as nodes, and the other with timestamps as nodes, to enhance the interpretability of the data by considering different elements as nodes. This allows the model to interpret the data from different perspectives. The proposed GCN model was tested using real-world electric vehicle (EV) data and demonstrated improved performance compared to RNN-based baselines. In addition, the GCN model has advantage of being able to clearly express the relationships between variables in a graph, improving interpretabilty.
PubDate: 2023-12-01
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- Joint Estimation of Vehicle State and Parameter Based on Maximum
Correntropy Adaptive Unscented Kalman Filter-
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Abstract: To address the problem of poor robustness and accuracy of vehicle state and parameter estimation by conventional Kalman filter in the non-Gaussian environments, a three-degree-of-freedom vehicle model with an improved Dugoff tire model is established and a joint estimator of vehicle state and parameter is designed using the Maximum Correntropy (MC) adaptive unscented Kalman filter (AUKF) algorithm in order to simultaneously estimate and identify the yaw rate, longitudinal vehicle speed, lateral vehicle speed, vehicle mass and rotational inertia. The proposed joint estimator algorithm was validated by Simulink/CarSim simulation testbed under Double Lane Change and Sine Wave Steering Input conditions. The results show that MC combined with AUKF (MCAUKF) algorithm has higher estimation accuracy and better convergence compared to the unscented Kalman filter (UKF) and the MC combined with UKF (MCUKF) in non-Gaussian environments, and the MCAUKF estimator is more suitable for state estimation and parameter identification of real vehicles.
PubDate: 2023-12-01
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- Design of Permanent Magnet Solenoid Valve for Electric Vehicle AVH
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Abstract: The solenoid valve for traction control valve (TCV) responsible for the automatic vehicle hold (AVH) function of electric vehicles require a continuous supply of power to the magnetic coil in order to operate. When power is continuously supplied, heat is generated because of the resistance of the magnetic coil, which leads to deterioration of the periphery of the solenoid valve and deteriorates durability. To prevent this, when power is applied to the solenoid valve for TCV for more than a certain period of time, the power supply is automatically turned off and the AVH function is controlled to be performed by the electronic parking brake (EPB). In this study, we designed, manufactured, and verified the permanent magnet solenoid valve of TCV for AVH that can minimize unnecessary power consumption of electric vehicle batteries. For the design of the permanent magnet solenoid valve, the location, polarity direction and specifications of the permanent magnet within the solenoid valve were studied through finite element analysis. In order to check whether the braking function by the permanent magnet is maintained even when the current is cut off at AVH’s TCV solenoid valve, electronic control unit (ECU) and electronic stability control (ESC) were manufactured and evaluated for actual vehicle testing. Therefore, it was possible to manufacture a permanent magnet solenoid valve that minimizes unnecessary power consumption of the battery because it does not require power supply even when the car is stopped for a long time in the AVH function of the electric vehicle.
PubDate: 2023-12-01
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- Multi-objective Optimization Method with Multi Control Variables and Its
Application in Configuration Comparison of Combination HEV-
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Abstract: Combination hybrid electric vehicle (HEV) mainly includes two configurations: series-parallel and power-split. It is necessary to consider a variety of metrics to comprehensively evaluate the configuration performance of HEV and compare the two configurations. In order to fully evaluate the HEVs’ potential in energy management, a practical and effective multi-objective method that can solve the global optimization-based energy management problem is needed. Based on the idea of dynamic programming (DP) and non-dominated sorting method, this paper proposes a global multi-objective optimization method of non-dominated sorting dynamic programming (NSDP) with multi-control variables. This algorithm can calculate a set of uniformly distributed Pareto solutions for the conflicting or coupling optimization objectives, and the performance of the solution set is improved due to the increase in the dimension of the control variables, which increases the strategy search space. NSDP is applied to two different configurations to fully evaluate the performance of fuel consumption and battery lifespan. The parameters of the configurations are optimized and comprehensively compared based on the implementation of NSDP. The above process can provide theoretical analysis for hybrid power system developers.
PubDate: 2023-12-01
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- Decision-making for Connected and Automated Vehicles in Chanllenging
Traffic Conditions Using Imitation and Deep Reinforcement Learning-
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Abstract: Decision-making is the “brain” of connected and automated vehicles (CAVs) and is vitally critical to the safety of CAVs. The most of driving data used to train the decision-making algorithms is collected in general traffic conditions. Existing decision-making methods are difficult to guarantee safety in challenging traffic conditions, namely severe congestion and accident ahead. In this context, a semi-supervised decision-making algorithm is proposed to improve the safety of CAVs in challenging traffic conditions. To be specific, we proposed the expert-generative adversarial imitation learning (E-GAIL) that integrates imitation learning and deep reinforcement learning. The proposed E-GAIL is deployed in roadside unit (RSU). In the first stage, the decision-making knowledge of the expert is imitated using the real-world data collected in general traffic conditions. In the second stage, the generator of E-GAIL is further reinforced and achieves self-learn decision-making in the simulator with challenging traffic conditions. The E-GAIL is tested in general and challenging traffic conditions. By comparing the evaluation metrics of time to collision (TTC), deceleration to avoid a crash (DRAC), space gap (SGAP) and time gap (TGAP), the E-GAIL greatly outperforms the state-of-the-art decision-making algorithms. Experimental results show that the E-GAIL not only make-decision for CAVs in general traffic conditions but also successfully enhances the safety of CAVs in challenging traffic conditions.
PubDate: 2023-12-01
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- Analysis of the Parameters Affecting the Efficiency of the Wireless Power
Transmission System Designed for New Generation Electric Vehicles-
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Abstract: In magnetic resonance coupled wireless power transfer (WPT) systems, parameters were investigated in the WPT system to ensure maximum power transfer under the conditions of changing the distance between the receive coil and the transmit coil. When the distance between the transceiver coils is changed, the inductances of the system and the coupling coefficient for maximum power transfer were calculated with Maxwell-3D, which performed a solution based on the finite element method (FEM). In addition, the effect of the distance variation between the transmitter and receiver coils, the coupling coefficient (k) values, on the input inductance and power transmission was investigated. In the model developed in the ANSYS-Maxwell environment, it has been observed that the variation of the input inductance depending on the distance and therefore the common inductance between the transmitter and receiver coils can be analyzed. In addition, the effect of the coupling factor (k) on the WPT system has also been demonstrated. It has been shown that maximum power transfer can be sustained in WPT systems where the distance between the receive coil and the transmit coil varies within certain limits. Finally, the efficiency of the transformer for a close distance between the coils was also tested experimentally.
PubDate: 2023-12-01
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- Effects of Varying Excess Air Ratios on a Hydrogen-fueled Spark Ignition
Engine with PFI and DI Systems under Low-load Conditions-
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Abstract: In this study, the effects of varying excess air ratios on a 2.0 L naturally aspirated (NA) hydrogen-fueled spark ignition (SI) engine were evaluated under low-load conditions by using port fuel injection (PFI) and direct injection (DI) systems. The engine speeds chosen were 1,200 rpm and 2,000 rpm. The excess air ratio was varied between 1.0 and 2.7 by controlling the throttle and hydrogen fuel rate under a brake mean effective pressure of 0.4 MPa. The combustion mechanism, net indicated thermal efficiency (ITE), brake thermal efficiency (BTE), gas exchange efficiency, mechanical efficiency, and engine-out nitrogen oxide (NOx) emissions were mainly discussed. The results indicated that the main combustion duration of PFI was shorter than that of DI due to its homogeneity, and the ITE values of PFI were similar or slightly lower than those of DI. However, as the mechanical efficiency of DI was higher than that of PFI, the BTE values of DI were always higher than those of PFI (the maximum BTE was 39.7 %). The NOx reduction potential of DI was superior to that of PFI due to stratified combustion, and its lowest value was 0.03 g/kWh. In addition, as the CO2 concentration in the exhaust gas increased, the brake-specific CO2 reduced (< 5.71 g/kWh).
PubDate: 2023-12-01
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- Prediction of Nonlinear Stress-strain Behaviors with Artificial Neural
Networks and Its Application for Automotive Rubber Parts-
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Abstract: This study presents a new method to predict the stress-strain curves of rubber materials using artificial neural networks in order to reduce the numbers of tensile tests and shows its application. Various stress-strain curves used for the machine learning are obtained by uniaxial, biaxial, planar tension tests on the chloroprene rubber specimens. Tests are carried out at a rate of 0.01 strain/s at 23 °C, and the Mullins effect is reflected through five load-unload processes in the strain range of 0 ∼ 20 %, 0 ∼ 50 %, 0 ∼ 70 %, and 0 ∼ 100 %. After training, the stress-strain relationships in untrained ranges are predicted. The predictions are compared with the experimental data in the strain range of 0 ∼ 100 %, which was previously reserved to confirm the prediction performance. It was predicted with errors within 0.04, 0.08, and 0.01 MPa for the uniaxial, biaxial, and planar tests, respectively. These small errors indicate predictions are reliable. For optimization of rubber parts, material constants of Ogden model are obtained using the predicted data in the strain of 0 ∼ 60 % and 0 ∼ 80 %. Dust covers are optimized to reduce stresses by the Taguchi method. The maximum von Mises stresses in the optimal designs are reduced by approximately 8 % and 14 %, compared to the initial ones.
PubDate: 2023-12-01
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- Development of Real Operating Test Cycle for Tier-IV Construction
Machineries with Emissions Characteristics-
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Abstract: Recently, the clean air support system (CAPSS) pronounced that construction machinery is the second-largest source of pollutants in Non-road mobile machinery (NRMM) after ship. Three types of vehicles, forklifts, excavators, and loaders, account for 80 % of pollutants emitted by all construction machinery. In addition, domestic construction emission regulation has been reinforced by adopting the EU Stage V regulations. Therefore, it is necessary to analyze the domestic and overseas construction machinery regulations and perform emission test under real operation condition. In this study, three construction machineries (1 unit each of forklift, excavator, and loader) were selected as test vehicles. The emissions were measured using a portable emissions measurement system (PEMS) installed on the test vehicles. The real operation test was conducted in accordance with EU Stage V ISM policy emission measurement procedure. The real operation test cycles were developed for each vehicle type to meet the minimum test duration requirement. Additionally, the test data were evaluated to distinguish valid working events and analyze emission characteristics using the moving average window (MAW) analysis method.
PubDate: 2023-12-01
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- Equivalent Stiffness Modeling Method of a Battery System for Evaluating
Vehicle Rear-End Collision Performance-
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Abstract: The battery system of a vehicle is critical for its good operation, and in the event of vehicle collision, damage to the parts or displacement of the battery can significantly affect performance. Nonlinear characteristics must be considered when designing elastic parts for vehicle’s collision analysis, and nonlinear dynamic finite element analysis simulations should be used to derive dynamic responses through collision simulations. Existing models examine the overall behavior without specifically configuring each part, and therefore, we need a model that can derive dynamic responses through collision simulations at the part level. Thus, we propose an equivalent stiffness model for a battery system to evaluate the vehicle’s rear-end collision performance in the early design stage. The model showed errors of 7.54 % and 6.12 % in the peak-to-peak values for the x-axis and z-axis acceleration responses, respectively, on the upper part of the battery; the root mean square value was within the margin of error. Simulation results confirmed that the proposed model is as accurate as the real vehicle test and nonlinear dynamic finite element analysis model. Thus, it can be used in the early design stage to predict system performance for collision test in a system whose geometry has not been determined.
PubDate: 2023-10-01
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