Improve Vehicle Performance Research Articles

Morphing Spoiler for Adaptive Aerodynamics by Shape Memory Alloys

The automotive industry is continuously looking for innovative solutions to improve vehicle aerodynamics and efficiency. The research introduces a significant breakthrough in the field of automotive aerodynamics by employing shape memory alloys as bistable actuators for spoilers and moving flaps. The main novelty of this research lies in the development of a bistable actuator made of shape memory alloys as a precise and accurate control mechanism for spoilers and movable flaps. The shape memory alloys, with their unique ability to maintain two stable configurations and switch rapidly from one to the other in response to thermal or mechanical stimuli, allow precise and rapid adjustment of aerodynamic surfaces. The main advantage of this technology is its ability to improve vehicle aerodynamics by optimising both drag and downforce, thereby improving vehicle performance and fuel efficiency. This research shows the promising potential of a single composition of NiTi as a revolutionary technology in the automotive industry, revolutionising the way spoilers and moving flaps are used to achieve superior vehicle performance.

A Sustainable Free-Standing Triboelectric Nanogenerator Made of Flexible Composite Film forBrake Pattern Recognition in Automobiles.

In recent years, the automotive industry has made significant progress in integrating multifunctional sensors to improve vehicle performance, safety, and efficiency. As the number of integrated sensors keeps increasing, there is a growing interest in alternative energy sources. Specifically, self-powered sensor systems based on energy harvesting are drawing much attention, with a main focus on sustainability and reducing reliance on typical batteries. This paper demonstrates the use of triboelectric nanogenerators (TENGs) in a computer mouse for efficient energy harvesting and in automobile braking systems for safety applications using SrBi2Ta2O9 (SBTO) perovskite, blended PDMS composite operating in free-standing mode with an interdigitated patterned aluminum electrode. This self-powered sensor is capable of distinguishing between normal and abnormal braking patterns using digital signal processing techniques. It is noteworthy that the addition of 15%wt. of the SBTO in PDMS composite-based TENG delivered 13.5V, 45nA, and an output power of 0.98µW. This new combination of energy harvesting and safety applications enables real-time monitoring and predictive maintenance in the automotive industry.

Analisis Penambahan Bioethanol dan Oktan Booster pada Bahan Bakar Pertamax terhadap Daya dan Torsi Sepeda Motor 4-Langkah

There are various methods to improve vehicle performance, including by blending fuels. In addition to bioethanol that can increase octane rating, there is the use of additives in vehicle fuels that are known to increase octane rating. With the addition of additives such as octane boosters and bioethanol, it is expected to increase the degree of ignition, reduce knocking, achieve more complete combustion, and automatically increase vehicle power.This study aims to analyze the power and torque of a four-stroke motorcycle by considering the addition of bioethanol and octane boosters to Pertamax fuel. Experimental method was used with variation of bioethanol concentration and octane booster in fuel as test parameters. This study provides insight into the potential for power improvement in the use of Pertamax fuel on four-stroke motorcycles through the addition of bioethanol and octane booster. The implications of these findings can serve as a basis for the development of more efficient and sustainable fuels for four-stroke motorcycles.

Analisis Pengaruh Rasio Kompresi Dan Persentase Etanol Pada Bahan Bakar Terhadap Kinerja Mesin Otto 4 Langkah

The public demand for better vehicle performance is increasing. Increasing the compression ratio and fuel octane is one solution to improve vehicle performance. This study aims to determine the effect of increasing the compression ratio and ethanol mixture in fuel on the power and torque of the 125 cc Otto engine. The method used is an experiment using a dynotest. The compression ratio was varied to 8.9, 9.2, and 9.6, with ethanol mixtures of 5%, 10%, and 15%. The test data were analyzed using the two-way ANOVA method. The results showed that increasing the compression ratio and ethanol mixture can increase engine power and torque. The highest peak power and torque occurred at a compression ratio of 9.6 with 15% ethanol, which was 6.11 kW and 10.23 N.m. A compression ratio of 9.2 produces 6.07 kW of power and 10.07 N.m of torque at 15% ethanol. A compression ratio of 8.9 produces 5.62 kW of power at 15% ethanol and 9.73 N.m of torque at 10% ethanol.

Integration of a Belt Starter Generator in a Flex-Fuel Vehicle

<div>The concern with global warming has led to the creation of legislation aimed at minimizing this phenomenon. As a result, the development of technologies to minimize vehicle emissions and reduce fuel consumption has gained market share. A promising alternative is the use of a belt starter generator (BSG): an electric machine to replace the vehicle’s alternator. This research analyzes the effects of introducing a 12 V BSG into a flex-fuel vehicle, specifically examining its impact on fuel economy and CO<sub>2</sub> emissions when using both gasoline and ethanol. The utilization of a low-voltage BSG in a flex-fuel vehicle has not been previously studied. Numerical simulations and experimental fuel consumption and CO<sub>2</sub> emissions tests were performed for the normal production flex-fuel baseline configuration and the vehicle with the 12 V BSG, following the standards ABNT NBR 6601 and ABNT NBR 7024. The use of the BSG led to a 10.06% reduction in CO<sub>2</sub> emission in the urban cycle for the vehicle running on gasoline and a 6.28% reduction in energy consumption in the combined cycle. The results demonstrated that the low-voltage BSG is a promising solution for reducing fuel consumption and GHG emissions in flex-fuel vehicles. The electrical machine installation required minimal modifications to the vehicle and had a low adaptation cost. The BSG can also improve vehicle performance and drivability.</div>

Design and Implementation of a Car’s Black Box System using Arduino

A black box system (BBS) in a car is crucial for recording and analyzing critical data to enhance safety, investigate accidents, and improve vehicle performance. This research presents a BBS developed using Arduino for cars, aimed at using the power of modern technology for comprehensive data capture and analysis in vehicular contexts. The BBS, or Event Data Recorder (EDR), is an essential component for enhancing road safety, accident analysis, and overall vehicle performance evaluation. The proposed system uses Arduino, a versatile and cost-effective microcontroller platform, to create a robust and customizable solution. It integrates various sensors and data acquisition modules to collect critical data points, including speed, acceleration, GPS coordinates, engine performance, and vehicle diagnostics. The architecture of the system and its smooth integration into automobiles are described in this article through detailed hardware and software design. Data retrieval and analysis are made possible by the system's user-friendly interface, which helps with fleet management, driver behaviour analysis, and accident investigation. This paper addresses the importance of data privacy and security while highlighting technological improvements. It proposes measures to ensure that personal data is managed responsibly and in accordance with legal requirements. In conclusion, a major advancement in improving road safety and vehicle monitoring has been made with the integration of Arduino technology into the car's BBS. Considering data security and privacy, this system provides users with an extensive set of facts to enable them to make well-informed decisions.

Natural Fibre and Hybrid Composite Thin-Walled Structures for Automotive Crashworthiness: A Review.

Natural fibres, valued for their low density, cost-effectiveness, high strength-to-weight ratio, and efficient energy absorption, are increasingly emerging as alternatives to synthetic materials in green composites. Although they cannot fully replace synthetic counterparts, like carbon, in structural applications due to their inferior mechanical performance, combining them through hybridization presents a potential solution. This approach promotes a balance between environmental benefits and mechanical efficiency. Recently, the transportation sector has shifted its focus towards delivering lightweight and crashworthy composite structures to improve vehicle performance, address safety concerns, and minimise environmental impact through the use of eco-friendly materials. The crashworthiness of energy absorbers, typically thin-walled structures, is influenced by several factors, including their material and geometric design. This paper presents a comprehensive overview of recent studies focused on the crashworthiness of fibre-reinforced, thin-walled composites under axial crushing. It explores different aspects, such as their materials, cross-sections, stacking sequences, triggering or filling mechanisms, and the effect of loading rate speed. Emphasis is placed on natural-fibre-based materials, including a comparative analysis of synthetic ones and their hybridization. The primary objective is to review the progress of solutions using green composites as energy absorbers in the automotive industry, considering their lightweight design, crashworthiness, and environmental sustainability.

Active suspension for all-terrain vehicle with intelligent control using artificial neural networks

The automotive industry focuses on developing advanced protection and stability control systems, particularly for suspension and steering, to enhance vehicle comfort, luxury, and safety. This research presents an intelligent controller for all-terrain vehicle (ATV) suspension systems based on Artificial Neural Network (ANN) technology. The controller leverages ANN capabilities to optimize system performance. MATLAB simulations were conducted to evaluate its effectiveness under various disturbances. A comparative analysis compared the ANN regulator, classic ANFIS regulator, and passive performance in different disturbance scenarios. The simulation results demonstrate exceptional performance of the ANN-based controller in displacement reduction, speed, acceleration, and robustness. The controller effectively mitigates disturbances, enhancing overall suspension system performance. These findings highlight the advantages of employing ANN technology in ATV suspensions. This research contributes to intelligent control systems advancement in the automotive industry, specifically in ATV suspensions. The demonstrated improvements have the potential to enhance passenger comfort, vehicle stability, and safety across terrains. By implementing ANN-based controllers, automotive manufacturers can optimize suspension systems, leading to improved vehicle performance. Several indicators, including RMSE, MRE, and R2, were utilized to test and validate the models. The R2 values for the three quality parameters ranged from 0.989 to 0.999, indicating a high level of consistency in the predictions made by the ANN, a "5-12-1" structure is employed. The results of this study add to the expanding body of knowledge endorsing the efficacy of ANNs in simulating and optimizing quarter-vehicle dynamics.

Bi-LSTM predictive control-based efficient energy management system for a fuel cell hybrid electric vehicle

Energy management systems (EMS) in hybrid electric vehicles play a vital role in achieving optimal energy utilization, improved fuel efficiency, lower emissions, and improved vehicle performance. Thus, this paper proposes a Bidirectional Long-Term Memory (Bi-LSTM) model based on an efficient EMS for a hybrid electric vehicle that manages the powertrain elements of the IC engine, electric motor(s), fuel cell, and energy storage system. The proposed EMS also continuously monitors various parameters, including vehicle speed, driver inputs, battery state of charge, engine load, and environmental conditions, to make real-time decisions on power distribution and operation modes. Further, the proposed system leverages the capabilities of the Bi-LSTM model to capture intricate temporal dependencies and bidirectional context in driving patterns using the real-time dataset. Empirical studies are conducted to substantiate the efficacy of the proposed energy management strategy vis-à-vis diverse controllers, encompassing Model Predictive Control and Sliding Mode Control. Real-world driving data is employed as the basis for these investigations, aiming to provide a robust assessment of the proposed energy management approach in practical scenarios. Results demonstrate the efficacy of the suggested methodology compared to traditional EMS approaches, showcasing improvements in energy utilization and reduced environmental impact. Finally, this research emphasizes a comparative analysis of the proposed topology with the existing approaches in a real-world fuel cell hybrid electric vehicle system. The findings provide insights into the feasibility of deploying intelligent EMS strategies for improved sustainability and efficiency in a modern energy-efficient environment.

An Integrated Control Framework for Torque Vectoring and Active Suspension System

Four-wheel independently driven electric vehicles (FWID-EV) endow a flexible and scalable control framework to improve vehicle performance. This paper integrates the torque vectoring and active suspension system (ASS) to enhance the vehicle’s longitudinal and vertical motion control performance. While the nonlinear characteristic of the tire model leads to a relatively heavier computational burden. To facilitate the controller design and ease the load, a half-vehicle dynamics system is built and simplified to the linear-time-varying (LTV) model. Then a model predictive controller is developed by formulating the objective function by comprehensively considering the safety, energy-saving and comfort requirements. The in-wheel motor efficiency and the power loss of tire slip are treated as optimization indices in this work to reduce energy consumption. Finally, the effectiveness of the proposed controller is verified through the rapid-control-prototype (RCP) test. The results demonstrate the enhancement of the energy-saving as well as comfort on the basis of vehicle stability.

Design of lateral dynamic control objectives for multi-wheeled distributed drive electric vehicles

The lateral dynamic control system for electric vehicles aims to improve vehicle performance, making it critical to propose reasonable lateral dynamic control objectives. Therefore, in this study, a novel lateral dynamic control objectives is proposed for multi-wheeled distributed drive electric vehicles. Initially, utilizing the classical two-degrees-of-freedom (2DOF) vehicle model, two initial yaw rate references were established for handling characteristics of manoeuvrability and stability, respectively. The yaw rate references consider the vehicle velocity, steering wheel angle and road friction coefficient limitations. Considering the vital role of the sideslip angle in ensuring vehicle stability, a sophisticated weighting factor was developed based on a detailed analysis of the vehicle dynamics within the sideslip angle-sideslip angle rate phase plane. The weighting factor was determined based on the position of the vehicle state in the phase plane. This factor was adeptly applied to harmonise our dual control objectives, culminating in an integrated control aim that encapsulates the individual benefits of each. Finally, the lateral dynamic control objective was realised using a direct yaw moment control (DYC) system. The effectiveness of the proposed control objective was validated through hardware-in-the-loop (HIL) simulations.

Evolusi Dan Kemajuan Industri Otomotif Melalui Ajang Balapan Dan Pameran Di Era Teknologi Novel The Mechanics Karya Annisa Lim

The purpose of this journal's research is to discuss the development and progress of the automobile industry in the face of technological change, especially through the important role of racing events and exhibitions in Anisa Lim's novel The Mechanics. A positive path for racing events, arousing competitive spirit and promoting improved vehicle performance. We will promote the development of the latest technology. In addition, automobile trade fairs are considered an important platform for the presentation of consumers and industry representatives. A detailed analysis of the role of these events is expected to provide deeper insights into the dynamics of the automotive industry in the current technological era and provide positive prospects for future development directions. Masu. Quantitative methods are used in this study. The analysis process also considered variable determination and measurement. Identify the variables you want to investigate. B. Improving vehicle performance, sales, or adoption of automotive technology. Quantitative methods are about testing hypotheses against existing theory. Therefore, this research tends to be objective and not deep. Through this study, we will provide deep insights into technology, automobiles, evolution, and progress, in order to expand our experience and knowledge about the innovation process of new jobs, especially in the technology field

Optimizing V2X Communication: Spectrum Resource Allocation and Power Control Strategies for Next-Generation Wireless Technologies

The upcoming wireless technology developments in the next generations are expected to substantially transform the vehicle-to-everything (V2X) communication network. The challenge of limited spectrum resources in V2X communication, caused by the need for high data rates, necessitates a thorough analysis of spectrum resource allocation and power control. This complex problem falls under the domain of mixed-integer nonlinear programming; a strategic approach is implemented to overcome these issues, which divides the main challenge into two sub-problems. The issue of resource allocation is addressed by implementing a multiaccess spectrum allocation method, which is deliberately designed to optimize the utilization of the spectrum resources that are currently accessible. Concurrently, the power control issue is resolved by employing a continuous convex approximation technique, which effectively converts non-convex power-allocation issues into convex equivalents. This approach helps to alleviate interference between users. Finally, the simulation results prove that the proposed approaches can improve vehicle performance. The algorithms proposed in this article significantly improve the system throughput and access rate of vehicular user equipment (VUEs) while ensuring the data rate of cellular user equipment (CUEs).

Frequency Based Supervisory Power Management Approach for Battery-Supercapacitor Electric Vehicle Considering Road Slope

This article proposes a real time strategy based on frequency separation for energy management of battery/Supercapacitor (SC) based Hybrid Energy Storage System (HESS) in Electric Vehicle (EV). The performance and life cycle of battery are important parameters in determining the efficiency of EVs. With the aim of improving the effectiveness of battery in battery/SC based EVs, a supervisory Symlets wavelet based Energy Management System (EMS) using Fuzzy Controller (FC) is proposed which considers real time road slope and vehicle condition. Actual road grade values are computed from Contour Positioning System (CPS) using Google Earth software. Symlets wavelet control technique is utilised to separate the high frequency transients from the required load power. After separation of high frequency components, a FC is developed to provide an additional reference power for battery and SC. Then, the high frequency transients along with additional power obtained from FC are allocated to SC and the rest is handled by the battery. Three road slope conditions viz. city tour, downhill and uphill and two drive cycles viz. NYCC and UDDS are applied to examine the capability of proposed EMS. The proposed Fuzzy Symlets Wavelet based controller is observed to improve the battery Ampere hour (Ah) throughput and Root Mean Square (RMS) value of battery current in comparison to only Symlets wavelet based controller for city tour, uphill and downhill journeys. The results indicate the robustness and superiority of the designed Fuzzy Symlets Wavelet based control technique as compared to Symlets Wavelet technique in handling practical conditions and improving vehicle performance.

Parameter optimization of multi-suspended equipment to suppress carbody vibration of high-speed railway vehicles: a comparative study

ABSTRACTParameter optimization of equipment suspended under the carbody underframe is one of the important measures to suppress carbody vibration of high-speed trains. Previous work focused on using one equipment as a dynamic vibration absorber to control carbody vibration, overlooking the coupling effects of multiple equipments and their vibration tolerance. To address this, a comprehensive study involving multi-objective and multi-parameter optimization to enhance both carbody and equipment vibration performance is proposed. First, a theoretical model with one equipment is established to derive optimal parameters, and a simulation model with multi-suspended equipment is built to conduct optimize calculations with an improved niche genetic algorithm (INGA). Afterwards, a comparative study on carbody and equipment vibration performance is analysed based on the optimal parameters, proving that the optimal method with INGA is more effective in improving vehicle performance. Finally, the method is applied to a newly designed vehicle, and the performance is validated on a rig, providing technical support for the design of high-speed vehicles.

Evaluation Modeling of Electric Bus Interior Sound Quality Based on Two Improved XGBoost Algorithms Using GS and PSO

There is no doubt that traffic noise has become one of the main sources of urban noise, and the electric bus, as an important means of transport frequently used by people in daily life, has a direct impact on the psychological and auditory health of passengers due to its interior noise characteristics. Consequently, studying electric bus sound quality is an important way to improve vehicle performance and comfort. In this paper, eight electric buses were selected and 64 noise samples were measured. Acoustic comfort was taken as an evaluation index, professionals were organized to complete the subjective evaluation tests for all noise samples based on rank score comparison (RSC). And nine psycho-acoustic objective parameters such as loudness, sharpness and roughness were calculated using ArtemiS software to establish the sound quality database of electric buses. Aiming at the practical application requirements of high-precision modeling of acoustic comfort in vehicles, this paper presented two improved extreme gradient boosting (XGBoost) algorithms based on grid search (GS) method and particle swarm optimization (PSO), respectively, with objective parameters and acoustic comfort as input and output variables, and established three regression models of standard XGBoost, GS-XGBoost and PSO-XGBoost through data training. Finally, the calculation results of three indexes of average relative error, square root error and correlation coefficient indicate that the proposed PSO-XGBoost model is significantly better than GS-XGBoost and standard XGBoost, with its prediction accuracy as high as 97.6 %. This model is determined as the evaluation model of interior acoustic comfort for this case, providing a key technical support for future sound quality optimization of electric buses.

Physics-informed data-driven modeling approach for commuting-oriented hybrid powertrain optimization

The physics-informed data-driven approach is instructive in powertrain modeling with high-fidelity, which can be adopted for powertrain optimization to further improve vehicle performances. In this paper, a plug-in hybrid electric vehicle (PHEV) equipped with a novel powertrain is considered as the research object, and a series of vehicle bench experiments are carried out for operative data acquisition. With the collected data, the physics-informed data-driven models for engine fuel consumption and battery state of health (SOH) loss are proposed. The accuracy and superiority of the proposed model are demonstrated compared with other approaches. In order to realize the commuting-oriented powertrain optimization, the driving cycle representing the commuting scenario is reconstructed based on the collected traffic data in Beijing. The synthesized driving cycle generated by the proposed method can describe the practical application scenario in Beijing within the allowable error range. In addition, the non-dominated sorting genetic algorithm-II (NSGA-II) is adopted to further optimize the parameters for the hybrid powertrain according to vehicle performances. The multi-objective optimization is completed with the engine fuel consumption and battery state of health (SOH) loss as targets. The results indicate that the optimized configuration parameters can reduce fuel consumption by 4.85% and battery capacity loss by 3.7% compared with the original vehicle.

Carbon fiber comparison to traditional materials for the automotive industry

As automotive technology continues to advance; the industry is confronted with the ongoing challenge of improving car performance while maintaining safety and structural integrity. This long-standing challenge in the field of automotive technology may soon find a solution through the emergence of carbon fiber composite materials, potentially ushering in a new era of material dominance. Carbon fiber composites possess exceptional strength and extreme stiffness compared to metals, yet they exhibit a much lower density 80% less than steel. This unique combination of properties makes carbon fiber a possible alternative for automobile materials. Moreover, carbon fiber is a more sustainable material compared to traditional metals, offering environmental benefits. It also enables more efficient manufacturing techniques, contributing to reduced energy consumption and waste production. The present study investigates the feasibility of utilizing carbon fiber as the primary material for future automobiles, exploring the intricate aspects of its manufacturing process and examining the wide range of applications associated with this new material. By investigating carbon fiber's potential, the automotive industry could unlock many new opportunities for improved vehicle performance, fuel efficiency, and sustainability.

Deep learning-based Ipoh driving cycle prediction

<span>The driving cycle is a series of driving behaviours, such as acceleration, braking, and cruising, that occur over a set length of time. Predicting the driving cycle can help to improve vehicle performance or anticipate the range of an electric car. Based on prior data, long short-term memory (LSTM) networks can be used to forecast a vehicle's driving cycle. This paper studies a driving cycle prediction based on LSTM by recurrent neural network (RNN) using developed driving cycle data. The objectives of this paper are; to develop an Ipoh driving cycle (IDC), to develop a prediction of future IDC and to analyze the prediction of IDC. Firstly, the driving data is collected in three different routes in Ipoh city at back-from-work times. Then the data is divided into micro-trips and the driving features are extracted. The features are used to develop a driving cycle using k-means clustering approach. The prediction is developed after the training of neural networks by using LSTM network approach with root mean square error (RSME) of 6.2252%.</span>

AZ91 magnezyum alaşımlarının sürtünme karıstırma nokta kaynağı;eksenel kuvvet ve dönme hızının kaynak kalitesine etkisi ve muayene planlamasına ilişkin bir yaklaşım

In this study, the friction stir spot welding (FSSW) process for the magnesium alloy (AZ91) sheet materials are investigated. Friction stir spot welding (FSSW) is a solid-state welding process and one of the innovative methods to join specifically automotive components. Recently magnesium alloys are promising materials where combination of high strength and low density can be observed to improve vehicle performance and reduce emissions and fuel consumption in structural materials for automotive applications. A finite element model (FEM) established to understand the effect of axial force and rotational speed during the simulation in automotive applications. The FEM investigates the effect of axial force ranging from 1 kN to 8 kN and rotational speeds of 1000 rpm - 4000 rpm. The model analyses the heat flux and the temperature rise that leads defect formation after the process. 4.5 kN, 6 kN and 8 kN axial forces and 3000 rpm-4000 rpm rotational speeds are evaluated as the critical values in terms of defect and crack formation during the process under specified boundary conditions. Finally, several non-destructive inspection methods are suggested to secure structural integrity after the FSSW process to eliminate the harmful effects of surface and volumetric discontinuities.