Vehicle Performance Research Articles

Study on the Impact of Tail Wing Profiles on the Resistance Characteristics of Amphibious Vehicles

The resistance performance of amphibious vehicles can be improved by installing underwater tail hydrofoils. The research on the impact of different hydrofoil profiles on the resistance characteristics of amphibious vehicles can provide a reference for the vehicle’s design. For an amphibious vehicle model, five shapes of symmetrical hydrofoils, NACA0012, NACA0015, NACA0016, and asymmetric hydrofoils NACA23012, NACA66-209, were selected as the underwater tail wing of the vehicle body, respectively. Based on the RANS method and overset grid technology, the resistance performance of the vehicle body was numerically calculated, and the resistance variation in the amphibious vehicle equipped with different tail hydrofoils at 0.43 < Fr∇ < 1.3 speed was obtained. The basic shape of amphibious vehicle tail wings can be determined by comparing the effects of symmetrical hydrofoils and asymmetric hydrofoils on body resistance. The results show that the asymmetric hydrofoils have a better resistance reduction effect on amphibious vehicles than the symmetrical ones. Among them, an amphibious vehicle installing the asymmetric hydrofoil NACA66-209 as an underwater tail wing can reduce resistance by 44.3%. Chord length is an important factor affecting the resistance reduction performance of tail wings. When Fr∇ = 1.3, the asymmetric hydrofoil optimized based on chord length has a 21.2% higher resistance reduction effect on amphibious vehicles.

Numerical prediction for vertical dynamic anti-sinking performance of underwater vehicle

Computational fluid dynamics (CFD) simulations of an underwater vehicle (UV) during self-propulsion and dynamic anti-sinking maneuvers are presented. A generic submarine, Joubert BB2, was selected as the test model, which was modified by the Maritime Research Institute, Netherlands (MARIN). Different recovery maneuvers for improving the anti-sinking ability were performed when the UV bow cabin was broken. The overset mesh method was used to simulate the deflection of the rudder and stern planes, and a body-force propeller represented by an actuator disk incorporating predetermined propulsion properties provided the thrust and torque for self-propulsion. Extra vector forces of inflow water forces and recovery buoyancy forces were proposed to simulate the flooding of a bow cabin and recover buoyancy by emptying the ballast. Numerical simulations were performed for 20 different combinations of vertical anti-sinking maneuvers, including stern control surfaces, increasing ship speed, and emptying the ballast water. The pitch angle and depth characteristics of UV self-propelled motion were analyzed based on the pre-set anti-sinking criterions. The results indicated stern control surfaces combined with accelerating could effectively maintain the UV's depth and pitch angle, and discharge ballast water could always make it surface quickly. We hope that this study will provide ideas for further research on anti-sinking abilities.

Modeling and simulation of a double DQN algorithm for dynamic obstacle avoidance in autonomous vehicle navigation

Developing an autonomous vehicle in safe navigated open environments through self-learning is a formidable challenge. So, in this paper, an autonomous navigation implementation for urban environments with dynamic obstacles using a double DQN algorithm is proposed. Furthermore, the vehicle is trained in varied training environments, explored neural network architectures, and fine-tuned action and reward functions to optimize its performance. In conclusion, the vehicle performance is steadily improved with each iteration of training, as evident from the upward trend in the average reward graph. These graphs depict the vehicle's learning progress in the scenario where it is trained to avoid dynamic obstacles. Finally, the proposed algorithm shows better performance relative to the conventional algorithms. The entire work is modelled and simulated in CARLA software.

Research on the Identification of Nonlinear Wheel–Rail Adhesion Characteristics Model Parameters in Electric Traction System Based on the Improved TLBO Algorithm

The wheel–rail adhesion is one of the key factors limiting the traction performance of railway vehicles. To meet the adhesion optimization needs and rapidly obtain wheel–rail creep characteristics under specific operating conditions, an engineering identification method for wheel–rail adhesion characteristics based on a nonlinear model is proposed. The proposed method, built upon the traditional Teaching-Learning-Based Optimization (TLBO) algorithm, has been adapted to the specific nature of nonlinear wheel–rail adhesion model parameters identification, enhancing both the search speed in the early stages and the search accuracy in the later stages of the algorithm. The proposed identification algorithm is validated using experimental data from the South African 22E dual-flow locomotive. The validation results demonstrate that the proposed identification algorithm can obtain a nonlinear wheel–rail adhesion characteristics model with an average adhesion coefficient error of around 0.01 within 50 iteration steps. These validation results indicate promising prospects for the engineering practice of the proposed algorithm.

A road surface reconstruction dataset for autonomous driving

Recent developments in intelligent robot systems, especially autonomous vehicles, put forward higher requirements for safety and comfort. Road conditions are crucial factors affecting the comprehensive performance of ground vehicles. Nonetheless, existing environment perception datasets for autonomous driving lack attention to road surface areas. In this paper, we introduce the road surface reconstruction dataset, providing multi-modal, high-resolution, and high-precision data collected by real-vehicle platform in diverse driving conditions. It covers common road types containing approximately 16,000 pairs of stereo images, point clouds, and ground-truth depth/disparity maps, with accurate data processing pipelines to ensure its quality. Preliminary evaluations reveal the effectiveness of our dataset and the challenge of the task, underscoring substantial opportunities of it as a valuable resource for advancing computer vision techniques. The reconstructed road structure and texture contribute to the analysis and prediction of vehicle responses for motion planning and control systems.

EDT-STACK: A stacking ensemble-based decision trees algorithm for tire tread depth condition classification

Tires serve as the critical interface between a vehicle and the road. Their condition significantly impacts road safety, vehicle performance, fuel efficiency, and operational costs. Developing a machine learning model for accurately and efficiently classifying tire conditions using tread depth measurements poses a critical challenge in enhancing automotive safety and maintenance. This research aims to address this challenge by leveraging machine learning techniques to categorize and assess tire conditions based on tread depth, thereby contributing to developing a robust and automated system for tire safety condition monitoring in diverse vehicle fleets. This article presents a novel ensemble method-based machine learning algorithm called EDT-STACK (Stacking Ensembles-Based Decision Tree) that combines several decision tree-based methods using a stacking ensemble technique. The model is trained and tested on a dataset of 500 tires (new and disposed) with different tread depths, The model achieves 99 % accuracy in all classes, demonstrating its efficacy in classifying tire conditions from tread depth data. The study also compares the performance of EDT-STACK with other algorithms. Its superior performance outshines other decision tree algorithms, such as XGBoost, AdaBoost, Random Forest, ID3, and Histogram-based Decision Tree, which achieve accuracies of 98 %, 78 %, 85 %, 98 %, and 98 %, respectively. This study concludes that EDT-STACK offers an innovative solution for tire condition assessment, which can enhance road safety, reduce environmental impacts, and optimize vehicle maintenance.

Experimental investigation on frosting performance of a microchannel heat exchanger in heat pump system for electric vehicles

Frosting of outdoor heat exchangers deteriorates the heat transfer performance of the electric vehicle (EV) heat pump system. The frosting performance of the outdoor heat exchanger in an EV heat pump air-conditioning system was investigated experimentally, considering different flow configurations, tube arrangements, and frontal air volume. The purpose of the study was to establish the optimal tube arrangements and flow configurations for minimizing frost formation and maximizing system performance. The results indicate that a three-flow configuration outperforms a two-flow setup under frosting conditions. Specifically, a flow sequence of 14-21-35 increases the heat capacity by 15.05 %–20.64 % compared to that of the reverse flow of 35-21-14. Additionally, the coefficient of performance (COP) increases by 1.19 %–3.55 %. Higher airflow reduces frost formation. A significant correlation exists between the refrigerant distribution within the heat exchanger and the corresponding frost distribution on the surface of the microchannel heat exchanger. The frost formation rate was slower for the flat tube vertical arrangement. Furthermore, horizontal tube arrangements were more conductive than vertical ones, providing a more uniform temperature distribution and higher heat transfer rates (an increase of 2.7% in heat transfer and 3.4% in heat capacity). The COP of the vertical arrangement system was consistently higher than that of the horizontal arrangement by 1.4 %–3 %.

Monitoring Fuel-Efficient Driving Patterns to Augment ADAS to regulate the fuel dynamically using Machine Learning

In the realm of enhancing Advanced Driver-Assistance Systems (ADAS) for optimal vehicle performance and sustainability, this paper introduces an innovative methodology that capitalizes on Machine Learning (ML) to scrutinize and learn from fuel-efficient driving patterns. Utilizing real-time driving cycle datasets collected through On-Board Diagnostics (OBD) tools, meticulously recorded the driving styles of 100 drivers on a predetermined route over duration of 35 minutes. The rich dataset includes an array of vehicle parameters and driving behavior, providing a comprehensive foundation for ML-based analysis. Proposed approach involves the application of state-of-the-art ML algorithms, with a specific focus on Machine learning and ensemble methods, to accurately model and predict fuel-efficient driving patterns. The algorithms XGB-regression, Random Forest regression and Gradient Boosting regressions were rigorously trained, validated, and tested on the collected data, ensuring robustness and reliability in their predictive capabilities. The scores and the Mean absolute Errors of these algorithms were estimated, most efficient algorithms are RF-regression and GB-regressions with a score of 99.93% and 99.93% with MAE 0.01219% and 0.01210%respectively. The integration of these predictive models into the ADAS framework mayshow promising results, significantly improving the system’s performance in real-time decision-making and driver assistance. The system demonstrates an enhanced capability to adapt to varying driving styles, offering personalized recommendations for fuel-efficient driving, and contributing to the reduction of fuel consumption and emissions.

Development of lightweight and high-strength glass fiber and natural fiber hybrid composites using VARTM technique

This research paper presents the synthesis of Glass Fiber Reinforced Plastic (GFRP) and Natural Fiber Hybrid Composites (NFHC) utilizing Vacuum-Assisted Resin Transfer Molding (VARTM) techniques. Jute and flax fibers were strategically selected for their unique properties and potential synergistic effects when combined with glass fibers. The study systematically evaluates the influence of natural fiber incorporation on the mechanical performance of the composites, focusing on critical factors such as fiber type, orientation, and resin selection. A series of meticulously crafted laminate configurations were employed. These included combinations of glass and jute fibers (GJG, GJGJG), glass and flax fibers (GFG, GFGFG), and a reference laminate composed solely of glass fibers (GGGGG). The mechanical characterization involved rigorous Izod impact testing, hardness assessments, and water absorption tests to provide a detailed understanding of behavior of composite material. The results reveal that the incorporation of jute fibers resulted in a significant enhancement of impact resistance of up to 59 % compared to GFG laminates and hardness. Water absorption varied across the laminates, with GGGGG exhibiting the lowest value of 0.054 %. Scanning Electron Microscopy (SEM) was employed to gain valuable insights into the critical fiber-matrix interaction and microstructure of the composites, further elucidating the observed mechanical properties. These findings pave the way for lightweight, high-strength composites ideal for engineering applications, particularly automotive mono leaf springs. The resulting weight reduction promises significant gains in fuel efficiency and vehicle performance, advancing sustainable materials with exceptional mechanics.

Aerodynamic optimization design of low reynolds number aerofoils based on induced laminar separation

At low Reynolds numbers, the design of aerofoils with a high lift-to-drag ratio (RLD) is crucial for enhancing the aerodynamic performance of Micro Air Vehicles (MAVs). However, the transition from laminar to turbulent flow plays a dominant role in the aerodynamic performance of aerofoils at Low-Reynolds-Numbers (LRN). The laminar separation bubbles formed in laminar flow alter the effective aerofoil shape, reducing aerodynamic efficiency. Additionally, predicting the shape and location of the laminar separation bubbles becomes challenging due to its sensitivity to the flight environment. To design aerofoils suitable for low Reynolds number MAV applications, we optimize a high RLD aerofoil by inducing transition to improve the effective aerofoil shape in the flow field. The study reveals that excessive curvature can suppress the formation of laminar separation bubbles at the leading edge, redirecting separation to the midsection. This results in reduced aerofoil drag and enables a high lift distribution at the leading edge.

The comparative research on the VPMM experiment and numerical simulation of the SUBOFF model

To ensure optimal maneuvering performance of underwater vehicles, it is essential to obtain highly precise hydrodynamic coefficients for constructing the equations of motion. The principal approach to acquiring these coefficients is through the Planar Motion Mechanism (PMM) test. The subject of this paper is the SUBOFF full appendage model. To obtain hydrodynamic coefficients related to maneuvering motion, a Vertical Planar Motion Mechanism (VPMM) test is conducted in the circulating water tank. In addition, a numerical simulation of the typical pure heaving and pure pitching maneuverability test is performed for the model by programming its motion UDF using Fluent’s dynamic mesh technology. The test results and calculated hydrodynamic coefficients are compared to the data obtained from the Key Laboratory of Underwater Robotics Technology of Harbin Engineering University. The calculation results show high credibility of the test results of the circulating tank VPMM test. The corresponding numerical simulation has a certain practical engineering accuracy, which can break through the limitations of the test and also provides valuable experience and technical reference for the hydrodynamic prediction of other underwater vehicles.

Investigation on steady state performance of hydrodynamic automatic transmission vehicle

The aim of this research is to glance the feasible scientific theory and performance investigations of steady-state hydraulic automatic transmission vehicle. Numerous studies that inform about new powertrain designs in steady-state conditions look at how well manual transmission vehicles operate. However, one of the most cutting-edge of conventional automotive vehicle power transmission for modern automobile designs is hydraulic automatic transmission. Consequently, in this research the realistic methodical scheme is achieved using classical optimization techniques, utilizing spatial case functions that signify engine and torque convertor performance curve in automatic transmission vehicle. The advantage is to test the vehicle performance that meets the experimental or numerical design enactment considering the exceptional correlation coefficient obtained at global extreme. Analysis comprises, engine's joint work with hydraulic torque converter examined in relation to vehicle speediness, observing the matching between engine and convertor based on the fundamental operational performances resembling, full use of engine power, economic fuel consumption, and the requirements of vehicle operation. Finally the vehicle powertrain feasibility is confirmed by, the maximum speed of the vehicle and fuel consumption in extra urban driving 274.5 km/hr and 5.5 L/100 km respectively. This shows, there is 4 % and 7 % deviation from vehicle evidence speed and fuel consumption respectively.

Distributed Finite-Time Prescribed Performance for Multiple Unmanned Aerial Vehicle With Time-Varying External Disturbance

Distributed Finite-Time Prescribed Performance for Multiple Unmanned Aerial Vehicle With Time-Varying External Disturbance

Enhancing airless tire performance for military vehicles: natural rubber compound with carbon black Fillers N220 and N550 with dynamic mechanical analysis approach

Enhancing airless tire performance for military vehicles: natural rubber compound with carbon black Fillers N220 and N550 with dynamic mechanical analysis approach

Development of a sensitivity analysis tool for the trajectory of multistage launch vehicles

The primary objective of this work is to create a highly accurate sensitivity analysis tool for multi-stage launch vehicle trajectories. This tool is designed to assess the impact of various parameters on the trajectory and performance of multi-stage launch vehicles. To achieve this, we have developed high-fidelity simulation software that considers all translational and angular movements by modelling the six-degrees-of-freedom (6DOF) equations of motion. The validation of this software is based on experimental data. An essential aspect of this work is the utilization of the developed sensitivity analysis tool to determine how different parameters affect the trajectory of multi-stage launch vehicles. Through the sensitivity analysis conducted using the developed tool, it is possible to identify which parameters are of critical importance during the design phase. We apply a generic mission profile for the Minotaur-I launcher to obtain parametric dependencies of the flight path. Through a comprehensive parametric study, we evaluate a range of critical parameters, including gross lift-off weight, a specific impulse of each stage, pitch-over manoeuvre initial time and angle, and ignition impulse of each stage. These parameters significantly influence the trajectory, performance, and reliability of the launch vehicle for mission design and success. The results of the sensitivity analysis underscore that even minor variations in these parameters can result in substantial deviations from the nominal insertion altitude. The acceptability of errors in specific impulse changes varies across stages, with maximum changes of 6.07%, and the fourth stage showing less sensitivity at 0.13%. However, it's important to note that variations in the parameters of the first stage tend to be challenging to rectify once they occur, with maximum changes in specific impulses reaching 75.57%. Another noteworthy discovery is that the acceptability of changes in pitch-over manoeuvre initiation times depends on the rate of change; they can be deemed either acceptable or unacceptable based on this factor, with changes ranging between 17.90% and 98.38%.

Design and Manufacturing of EV Go-Kart

Abstract: Go-karts are small-size and small-weight vehicles that were developed for racing. These are made with materials that are strong and durable. It consists of many parts which include a chassis, engine, steering and braking system, and electronic controls. The chassis is the main part that is responsible for the stability of the vehicle. Chassis is made with the material having greater endurance and rigidity. It was developed in the 1950s in the USA and now getting popular. These are now used in amusement parks as a recreational activity. Many researchers have done work on go-karts and improve their design. This project was intended to design and fabricate a reliable and durable go-kart. Its primary objective to build a go-kart using local resources and applying different techniques to limit the cost of vehicles. These objectives were achieved by going through a detailed literature review and studying different techniques which can be implemented. A reliable design was chosen which can be implemented and can be completed in our period. Critically evaluate the design of the vehicle and then different parts were designed. This paper presents an approach to a go-kart chassis design, vehicle dynamics calculation, Li-ion battery capacity analysis, and electric motor choice for optimized vehicle performance. The chassis analysis shown in this paper was performed using a CAD/FEA software package, SolidWorks Student Edition. Three highlights can be found in this paper: An original design was implemented; the basic analysis was composed of chassis optimization using beam elements and modelling such an optimized chassis “locally” with solid elements for sub-modelling purposes. The most stressed tube joint was sub-modelled to calculate the risk of tube wall stability. Vehicle dynamics were calculated for the case of braking on a curved path and the case of a collision with the front tire due to road imperfection. The authors intend to install a data acquisition system in the future to analyse the stress of local chassis tubes.

Estimating the Optimal State of Charge for Electric Car Batteries Using an Extended Kalman Filter

The efficient management of battery state of charge (SOC) is crucial for maximizing the performance, range, and longevity of electric vehicle (EV) batteries. This paper presents a novel approach for estimating the optimal state of charge of electric car batteries using an Extended Kalman Filter (EKF). The EKF is a recursive algorithm that combines measurements from various sensors with a dynamic battery model to estimate the current SOC and predict future SOC values with high accuracy. The paper provides a detailed explanation of the EKF algorithm and its application to battery SOC estimation, highlighting its ability to handle nonlinearities, uncertainties, and measurement noise inherent in battery systems. Furthermore, this research presents a simulation-based validation of the proposed EKF approach using real-world driving data from electric vehicles. The simulation results demonstrate the effectiveness of the EKF algorithm in accurately estimating the SOC of electric car batteries under various operating conditions, including different driving patterns, temperatures, and battery degradation scenarios.

Regulatory Insights for On-Board Monitoring of Vehicular NOx Emission Compliance.

Nitrogen oxide (NOx) emissions from heavy-duty diesel vehicles (HDDVs) have adverse effects on human health and the environment. On-board monitoring (OBM), which can continuously collect vehicle performance and NOx emissions throughout the operation lifespan, is recognized as the core technology for future vehicle in-use compliance, but its large-scale application has not been reported. Here, we utilized OBM data from 22,520 HDDVs in China to evaluate their real-world NOx emissions. Our findings showed that China VI HDDVs had a 73% NOx emission reduction compared with China V vehicles, but a considerable proportion still faced a significant risk of higher NOx emissions than the corresponding limits. The unsatisfactory efficiency of the emission treatment system under disadvantageous driving conditions (e.g., low speed or ambient temperature) resulted in the incompliance of NOx emissions, especially for utility vehicles (sanitation/garbage trucks). Furthermore, the observed intertrip and seasonal variability of NOx emissions demonstrated the need for a long-term continuous monitoring protocol instead of instantaneous evaluation for the OBM. With both functions of emission monitoring and malfunction diagnostics, OBM has the potential to accurately verify the in-use compliance status of large-scale HDDVs and discern the responsibility of high-emitting activities from manufacturers, vehicle operators, and driving conditions.

Enhancing Electric Vehicle Performance with a Hybrid PI-Sliding Mode Controller for Battery Supercapacitor Integration

Nowadays, most of the works are based on electric vehicle usage for sustainable transportation using traditional energy storage device, such as battery. Usage of batteries in electric vehicles is having several disadvantages, for example, life span, temperature, and charge estimation. In this paper, a novel control scheme for battery and supercapacitor- (SC-) based hybrid energy storage system (HESS) using hybrid proportional and integral- (PI-) sliding mode control (SMC) for electric vehicle (EV) applications is introduced and implemented. This HESS with hybrid controller proves the usage of batteries in EVs to its fullest potential. The conventional control strategy for HESS follows two-loop voltage and current PI controllers with low-pass filter (LPF) and involves tuning of multiple control parameters with variations of source and load disturbances. Performance of the system is affected by tuning PI controller constants. A slow response time with linear PI controllers is long which is not advisable for starting and sudden jerk conditions of EVs. Moreover, the PI controller performance is affected by the system parameter variations during load changes. And these parameters are dynamic in nature due to nonideal conditions. In this paper, a hybrid PI-sliding mode controller (SMC) scheme is designed to control the bidirectional DC-DC converters to overcome the drawbacks of aforementioned issues. The combined PI-SMC controller reduces the tuning effort and reduces the effect of shift in operating point in controller performance. Linear modeling is done using small signal analysis for each subsystems. Permanent magnet synchronous machine (PMSM) is used as electric vehicle. The entire system and its controllers are simulated using MATLAB-Simulink, and detailed comparison is carried between conventional PI and proposed hybrid PI-SMC scheme to regulate the DC link voltage. The results are tabulated and show that the hybrid PI-SMC scheme outperforms in transient and steady-state conditions than the traditional PI controller. A scaled hardware prototype of 48 W set-up is developed using dSPACE-1104, and the experimental results have been carried out to verify the proposed system’s feasibility.

Research on end-route planning for community group purchasing for vehicles with different energy sources

To address the issue of final delivery route planning in the community group purchase model, this study takes into full consideration logistics vehicles of different energy types. With the goal of minimizing the sum of vehicle operating costs, delivery timeliness costs, goods loss costs, and carbon emissions costs, a multi-objective optimization model for community group purchase final delivery route planning is constructed. An improved genetic algorithm with a hill-climbing algorithm is utilized to enhance adaptive genetic operators, preventing the algorithm from getting stuck in local optima and improving the solution efficiency. Finally, a case study simulation is conducted to validate the feasibility of the model and algorithm. Experimental results indicate that currently, among the three types of vehicles, fuel logistics vehicles still have an advantage in terms of vehicle usage cost. Electric logistics vehicles exhibit the poorest performance with the highest cost per hundred kilometers, but their sole advantage lies in their high energy release efficiency, enabling optimal low-carbon vehicle performance. Battery-swapping logistics vehicles perform the best in terms of carbon emissions, combining the advantages of both fuel-based and electric logistics vehicles. Therefore, battery-swapping logistics vehicles are a favorable choice for replacing fuel-based logistics vehicles in the future, offering promising prospects for future development.