Vehicle Manufacturers Research Articles

Process optimisation and robustness analysis for HFQ process

Lightweight materials and lightweight technologies have gained significant attention from all EV vehicle manufacturers. Aluminium has been commonly used as a lightweight replacement for steel in the automotive industry for many years. High-strength aluminium alloys have high specific strength, and their formability increases significantly at elevated temperatures. New manufacturing technologies incorporating elevated temperature forming have emerged recently, allowing forming of high and ultrahigh-strength aluminium alloys. One such technology is Hot Form Quench (HFQ)® which is a process that combines high formability with virtually no springback for (ultra)-high-strength aluminium alloys. The thermal expansion of aluminium alloys is approximately twice that of steel, leading to significant material contraction during in-die quenching. This is especially pronounced for large or long components. Therefore, robustness analysis of the process parameters can be used to optimise the process parameters and enable a capable production of parts meeting dimensional specification requirements. Such analysis can be performed using AutoForm-Sigma solver and this paper presents a simulation methodology developed in AutoForm and its use to verify the accuracy of the thermal distortion prediction for one of the safety cell structure components. A sensitivity analysis has been implemented to optimise the process parameters to improve manufacturing robustness.

IDADET: Iterative Double-Sided Auction-Based Data-Energy Transaction Ecosystem in Internet of Vehicles

In the era of big data, the unprecedented growth of data has been regarded as an important asset and the commercial application of data acquisition markets has emerged accordingly. With the advancement of vehicle manufacturing and sensor technologies, a large amount of data can be collected and stored in electric vehicles (EV), making the data acquisition scenario gradually extend to the Internet of Vehicles (IoV), and thus the corresponding operational rules and economic feasibility need to be fully investigated there. In this paper, we focus on a general IoV-oriented data acquisition market that consists of a data center, multiple EVs, multiple roadside units (RSUs), and a market operator (broker), with the objective of social welfare maximization (SWM) by identifying the optimal data task allocation. However, due to the inherent information asymmetry and fragmentation in such a market, it is not feasible to solve the SWM problem directly. To this end, we propose an iterative double-sided auction (IDA) mechanism, which leverages the self-interested feature of RSUs and EVs to decompose the SWM problem, enabling every participant to make decisions in a distributed manner under the broker’s coordination. A complete set of operational rules covering the data task allocation, bidding, payment, and reimbursement are elaborately designed to achieve SWM, and energy is adopted as the pricing “currency”, such that an IDA-based Data-Energy Transaction (IDADET) ecosystem is established in IoV. We verify the economic feasibility of the proposed IDADET ecosystem by showing its convergence and desirable properties of individual rationality, budget balance, incentive compatibility, and economic efficiency. In addition, considering the psychological effects of practical market participants, we make amendments to the operational rules of the IDADET ecosystem from the behavioral economics perspective, aiming to ensure its long-term well-functioning. Extensive numerical results are presented to show the performance of the IDADET ecosystem and demonstrate its advantages in terms of economic properties, operational feasibility, fast convergence, and market social welfare.

Used Car Price Prediction using Multiple Linear Regression

Abstract: Car price prediction is an active research topic that requires a lot of data and effort. The car market is a significant industry as a result of the tremendous expansion in vehicle production in recent years. The recent emergence of online marketplaces has strengthened the need for users and sellers to stay informed about the most recent market trends related to the subject. Our goal is to create a reliable and accurate model to forecast the cost of a used car based on a set of characteristics. To this end, we are utilizing the machine learning algorithm linear regression. When estimating the potential price of a car, we consider factors such as the company name, model name, year of purchase, fuel type, and number of miles driven.

Energy Policy - Ethanol Production in INDIA: The Roles of Policy, Price and Demand

Achieving energy security and the transitioning to a thriving low carbon economy is critical for a growing nation like India. Blending locally produced ethanol with petrol will help India strengthen its energy security, enable local enterprises and farmers to participate in the energy economy and reduce vehicular emissions, The Government of India notified the National Policy on Bio-fuels –2018 (NPB–2018) on 4.06.2018 wherein, under the Ethanol Blended Petrol (EBP) Program, an indicative target of 20% blending of ethanol in petrol by 2030 was laid out.The current ethanol production capacity in India of 426 crore litres derived from molasses-based distilleries, and 258 cr. litres from grain based distilleries is proposed to be expanded to 760 cr.litres and 740 cr. litres respectively. This would be sufficient to produce 1016 cr. litres of ethanol required for EBP and 334 cr litres for other uses. This will require 60 lakh MT of sugar and 165 lakh MT of grains per annum in ESY 2025 to be used for producing ethanol, which the country can support. To get the ball rolling, MoP&NG should proclaim and lay out the target for 10% ethanol blending of gasoline fuel all over the country by April, 2022. MoP&NG should further initiate phased roll-out of 20% ethanol blending from April, 2023 onwards to enable action by all stakeholders, namely Oil Marketing Companies, vehicle manufacturers, service stations, distilleries, and entrepreneurs as per a detailed roll-out plan suggested (Figure 9.1). This should be supported by a simpler and quicker regulatory regime, preferably single window clearance by the States, MoEF&CC, PESO,DFPD and MoP&NG and the launch of educational campaigns for consumers. This study accelerate the adoption and transition to ethanol blended fuels,price incentives through tax relief at the retail level on ethanol blended fuel and tax incentives for vehicles compatible with E20 are suggested. The government may also encourage use of lower water consuming food grain crops like maize, and 2G feed stock for production of ethanol.

Feasibility of Battery Electric Pickup Trucks in a State Department of Transportation Fleet

Vehicle manufacturers are beginning to introduce battery electric pickup trucks, with at least three models in production in the United States and at least six others announced. Unlike hybrid and plug-in hybrid vehicles, which use batteries to supplement an internal combustion engine, battery electric vehicles are fully reliant on their batteries and have significantly shorter ranges, fewer refueling/recharging options, and may experience shorter ranges under towing, mountain driving, and temperature extremes. This study investigated real-world pickup truck usage data from a large state department of transportation to determine whether battery electric trucks could effectively replace trucks based on manufacturer-stated range as well as early field tests of actual vehicle range under various speeds, idling scenarios, and weather conditions. The results indicated that 97% of pickup truck day trips could be completed on a standard range battery, and 99% on an extended range battery. Among tracked department of transportation fleet pickup trucks, 31% could be replaced by a standard range electric truck with no change in operation; and up to 64% of the fleet with an extended range could be replaced by an extended range electric truck. Dynamically assigning long trips to dedicated conventional engine pickup trucks could further reduce the number of nonelectric trucks required by half.

Faktor Analisis Penilaian Polisi Kenderaan Akhir Hayat

Vehicle manufacturers usually want their vehicles to last for 10 years, so vehicles that exceed this period are classified as End-of-Life Vehicles (ELV). Widespread use of ELVs can lead to environmental pollution. A framework for ELV management should be designed to address this problem. The aim of this research is to learn about ELV -related legislation and its implementation in foreign countries, as well as to measure public evaluation of the implementation of ELV recycling in Malaysia. The study includes a review of the literature that has been adopted in foreign countries, as well as a survey of 200 respondents. Data were analyzed descriptively using Cronbach’s Alpha reliability and factor exploratory analysis (EFA) using SPSS details. The results of the analysis found that the Cronbach’s Alpha value obtained was 0.925, which is more than 0.70. Results from the exploratory factor analysis showed eight factors with Eigen values exceeding 1.0, KMO (Kaiser-Meyer-Olkin) values were 0.792 and 0.881 exceeding 0.50 and Bartlett test showed significant values of p < 0.05, indicating items in the solid waste management knowledge section and ELV, community readiness and community attitudes were sufficient to implement the analytical factors.

Analysis of Implementation of Margins with Flat Installments in Murabahah Financing on Products iB PMG Motor Vehicles in the Bank of Sumut Sharia KCP HM Yamin

This study aims to determine the mechanism for implementing margins with flat installments in murabahah contracts and the factors that influence them on motor vehicles ib PMG products at Bank Sumut Syariah KCP HM Yamin. This study uses a descriptive qualitative approach with primary and secondary data sources. This study used data collection techniques by interviewing. The results of the research are that the mechanism for implementing margins on flat installments on murabahah contracts for iB PMG Motorized Vehicle products at Bank Sumut Syariah begins with the analysis of the treasure division by considering various costs and all that affect the financial value of Bank Sumut Syariah which is then discussed together with the DPS of the Bank. Sumut Syariah will then be presented at a joint meeting with Bank Sumut Syariah officials to agree on the provisions that have been analyzed and proposed by the treasure division. The size of Bank Sumut Syariah's margin is influenced by cost factors related to Overhead Costs, Profit Sharing of Third Party Funds (DPK), and Volume of Murabahah Financing. This study aims to determine the mechanism for implementing margins with flat installments in murabahah contracts and the factors that influence them on motor vehicles ib PMG products at Bank Sumut Syariah KCP HM Yamin. This study uses a descriptive qualitative approach with primary and secondary data sources. This study used data collection techniques by interviewing.

Vibration quality and ride comfort investigation with transient excitation in a ground vehicle simulator environment

Ground vehicle ride comfort is closely associated with vibration quality as perceived by the human occupants of a vehicle. While outstanding vibration quality is a desirable outcome of the vehicle design process, it is challenging to relate customer perceptions of vibration quality to customer-facing vehicle responses measured inside of a vehicle. Previous work with steady-state vibration has shown that human perception of vibration can be compared to how humans perceive sound, where both the amplitude and frequency of the excitation will alter its perception. Thus, a simple measurement of vibration level is insufficient to predict comfort. In this work, an analysis of human perception of transient vibration is undertaken using a ground vehicle simulator. Physical measurements from inside four different production vehicles are presented including accelerometer, microphone, and action camera recordings. The test event performed is travel over a cleat, with varying vehicle speeds and tire inflation pressures. The physical measurements are used as inputs to the simulator. A comparison is made between the perceived vibration quality of the vehicles as reported by an expert human jury and the physically measured vehicle responses. A ranking of 36 cleat test events for vibratory harshness from best to worst is presented.

Machine Learning and Artificial Intelligence Techniques for Detecting Driver Drowsiness

The number of vehicles on the road rises in tandem with the development of vehicle manufacture. With the rapid growth of automobiles, the number of road accidents appears to be steadily increasing. Accidents are a typical occurrence in our daily lives. Road accidents are the top cause of death from injuries worldwide in the top 10. It now forms an amazingly important part of the global burden of illness. An estimated 1.2 million people are killed in accidents every year, and an estimated 50 million are injured while receiving bone marrow transplants in leading countries. Drowsiness, along with fatigue of drivers, are some of the major causes of road accidents. Every year, we see an increase in death numbers worldwide. This study also looks into automatically detecting driver drowsiness with AI, visuals, and CNN. The Driver Drowsiness System works on the concept of Multi-Layer Feed Forward Network, especially on Convolutional Neural Networks (CNN). CNN was developed with the help of around 7000 images of eyes in both drowsiness and non-drowsiness states with different facial architectures. These images were split into training (80 percent of images) and testing (20 percent of images) datasets. The images under the training dataset are given as the input for the network for training purposes. Backpropagation algorithms and optimizers are used to reduce the loss as much as possible. We created an algorithm for detecting, tracking, and analysing motor and visual effects to measure ROI.

Evaluation of the combined driving cycle and its effect on fuel efficiency in a vehicle with an Otto cycle engine

The study proposed to analyze the combined driving cycles and their effect on fuel consumption within the Distrito Metropolitano de Quito. For this, a test protocol was established where the orography of the land, traffic density and road infrastructure of the District were studied. These factors allowed the evaluation of consumption through a global positioning system and a canister in order to systematically travel a set of roads in distance, time, and average speed in a vehicle with an Otto cycle engine. The data was processed through a statistical analysis of minimization of weighted averages. The findings revealed that the combined driving cycle obtained a high reliability (99.7%) in each procedure. The outcome of the study concluded that the combined cycle was differential to that proposed by the manufacturer, since there is a variation of 40% as proposed by the vehicle manufacturer.

Sourcing diversification: Strategy selection, sourcing allocation and brand power

The supply of power batteries is monopolised by a few suppliers, resulting in high purchasing costs for electric vehicle manufacturers (EVMs). Hence, some EVMs have implemented diversified sourcing strategies to seek purchase concessions and reduce their dependence on battery suppliers. Motivated by the sourcing strategies of EVMs, we develop a game-theoretic model to investigate the optimal procurement strategies of manufacturers with different brand power. The model consists of a primary supplier, an alternative supplier, and two competing manufacturers, wherein the focal manufacturer has the option of implementing dual sourcing or partial outsourcing through sourcing allocation, whereas the rival manufacturer only sources from the primary supplier. Specifically, we first study the focal manufacturer’s three procurement strategies under the primary supplier’s uniform pricing: single sourcing (the benchmark), dual sourcing and partial outsourcing. Then, we examine the primary supplier’s strategic response to the manufacturer’s sourcing diversification: maintaining uniform pricing or switching to differential pricing. By analysing the equilibrium profits, we find that compared with the situation under single sourcing, both the primary supplier and the focal manufacturer may obtain higher profits under dual sourcing or partial outsourcing. However, with an increase in the focal manufacturer’s order proportion, the primary supplier’s profit may decrease; and the focal manufacturer’s diversified sourcing strategy may hurt all the supply chain players. Furthermore, we show that when the primary supplier considers switching to differential pricing, the focal manufacturer tends to abandon the diversified sourcing and adopt single sourcing.

Vehicle-cycle and life-cycle analysis of medium-duty and heavy-duty trucks in the United States

Medium- and heavy-duty vehicles account for a substantial portion (25 %) of transport-related greenhouse gas (GHG) emissions in the United States. Efforts to reduce these emissions focus primarily on diesel hybrids, hydrogen fuel cells, and battery electric vehicles. However, these efforts ignore the high energy intensity of producing lithium (Li)-ion batteries and the carbon fiber used in fuel-cell vehicles. Here, we conduct a life-cycle analysis to compare the impacts of the vehicle manufacturing cycle for Class 6 (pickup-and-delivery, PnD) and Class 8 (day- and sleeper-cab) trucks with diesel, electric, fuel-cell, and hybrid powertrains. We assume that all trucks were manufactured in the US in 2020 and operated over 2021–2035, and we developed a comprehensive materials inventory for all trucks. Our analysis reveals that common systems (trailer/van/box, truck body, chassis, and lift-gates) dominate the vehicle-cycle GHG emissions (64–83 % share) of diesel, hybrid, and fuel-cell powertrains. Conversely, propulsion systems (lithium-ion batteries and fuel-cell systems) contribute substantially to these emissions for electric (43–77 %) and fuel-cell powertrains (16–27 %). These vehicle-cycle contributions arise from the extensive use of steel and aluminum, the high energy/GHG intensity of producing lithium-ion batteries and carbon fiber, and the assumed battery replacement schedule for Class 8 electric trucks. A switch from the conventional diesel powertrain to alternative electric and fuel-cell powertrains causes an increase in vehicle-cycle GHG emissions (by 60–287 % and 13–29 %, respectively) but leads to substantial GHG reductions when considering the combined vehicle- and fuel-cycles (Class 6: 33–61 %, Class 8: 2–32 %), highlighting the benefits of this shift in powertrains and energy supply chain. Finally, payload variation significantly influences the relative life-cycle performance of different powertrains, while LIB cathode chemistry has a negligible effect on BET life-cycle GHGs.

Channel leadership and performance for a closed-loop supply chain considering competition

This paper investigates channel leadership and performance in a closed-loop supply chain (CLSC) that involves the competition among an electric vehicle manufacturer (EVM), an electric vehicle recycler (EVR), and a third-party recycler (3PR). We develop a game-theoretic model to obtain the CLSC’s equilibrium outcomes for the EVM-led and EVR-led recycling channels and examine the impact of competition on these results. We then compare the equilibrium outcomes and discover that the EVM, EVR, and 3PR have difficulty aligning their preferences for recycling leadership mode, necessitating technological advancements in remanufacturing and governmental intervention. Our research highlights the significance of environmental responsibility awareness in the recycling process and discusses environmental damage and social welfare. Our findings indicate that in situations where the total recycling rate of the CLSC is low, the EVR may offer a higher price to prevent excessive new battery flow into the market. We suggest that the government should encourage more local EVRs to participate in the recycling system to reduce pollution. Finally, we extend our analysis to the cost-savings in remanufacturing and heterogeneous buyback prices.

Forecasting Carbon Dioxide Emissions of Light-Duty Vehicles with Different Machine Learning Algorithms

Accurate estimation of fuel consumption and emissions is crucial for assessing the impact of materials and stringent emission control techniques on climate change, particularly in the transportation industry, which accounts for a significant portion of global greenhouse gases and hazardous pollutants emissions. To address these concerns, the government of Canada has collected a large sensor-based dataset containing detailed information on 7384 light-duty vehicles from 2017 to 2021, with the goal of reducing CO2 emissions by 40–45% by 2030. To this end, various researchers worldwide have developed vehicle emissions and consumption models to comply with these targets and achieve the Canadian government’s ambitious objectives. In this work, we propose the development of boosting and other regression models to predict carbon dioxide emissions for light-duty vehicle designs, with the aim of creating ensemble learning models that leverage vehicle specifications to forecast emissions. Our proposed boosting model is capable of accurately predicting CO2 emissions, even with only one car attribute as input. Moreover, our regression models, in conjunction with the boosting algorithm, can effectively make predictions from various vehicle inputs. Our proposed technique, categorical boosting (Catboost), provides critical insights into transportation-generated air pollution, offering valuable recommendations for both vehicle users and manufacturers. Importantly, Catboost performs data processing in less time and with less memory than other algorithms proposed in the literature. Future research efforts should focus on developing higher performance models and expanding datasets to further improve the accuracy of predictions.

Promoting developments of hydrogen production from renewable energy and hydrogen energy vehicles in China analyzing a public-private partnership cooperation scheme based on evolutionary game theory

A tripartite evolutionary game model is put forward in this study, which represents the partnership among investment companies (IC), hybrid renewable energy generation system (HREGS) and hydrogen energy vehicle users (HEVU). Aims to determine whether the three parties are willing to participate in different scenarios and find out relevant optimal behaviors to further promote the development of renewable energy hydrogen production and hydrogen vehicles. HRES cooperation needs to meet the requirements that the direct normal irradiance (DNI) is greater than 1615 W/m2, the wind speed is greater than 4.5 m/s, the annual working time is greater than 1800 h, the government subsidy is greater than $152000, and the profit distribution ratio is greater than 0.34. IC cooperation needs to meet the requirements of DNI greater than 1425 W/m2, wind speed greater than 6 m/s, annual working time greater than 1950 h, government subsidy greater than $549000, and profit distribution ratio less than 0.48. HEU cooperation needs to meet the requirements that the government subsidy is more than $29300 and the hydrogenation operation risk cost is less than $2000. This study may provide a reference for renewable energy hydrogen production industry, hydrogen vehicle industry and governments making relevant policies.

Review of Just-in-Time Material Distribution Scheduling for Mixed Flow Assembly Lines Considering Transfer Vehicle Handling Energy Consumption

In recent years, the automotive industry, which uses component assembly methods for production, has grown rapidly as the demand for new energy vehicles has increased. This trend has also led to an increasing number of customers seeking individualized options, which places greater demands on traditional OEMs' vehicle production and assembly processes. In order to meet these challenges, OEMs need to improve their platform production lines by replacing traditional single-product lines with mixed-flow assembly lines. With the development of industrial standards and technology, automated production lines and unmanned vehicles are also being used in assembly line production. Decisions around the scheduling of material distribution on the side of assembly lines with material supermarkets need to be addressed in terms of the start of handling time of the AGV (Automated Guided Vehicle) trolley and the number of loaded bins. This paper presents a review of the research on issues such as distribution patterns under mixed flow assembly lines, combined with the new material supermarket distribution model, and considering the energy consumption of transfer vehicles for handling.

Study on the impact of government policies on power battery recycling under different recycling models

Whether the power battery can be recycled efficiently is related to the sustainable development of the electric vehicle industry. It is noteworthy that government intervention plays an essential role in promoting power battery recycling. However, there is no consensus on the impact of government policies to promote power battery recycling on social welfare. This paper constructs a closed-loop supply chain (CLSC) consisting of a single battery manufacturer, vehicle manufacturer, and third-party recycler under seven recycling models. The government intervenes in the CLSC through subsidy, deposit-refund, and reward-penalty policies to increase the recycling rate and supply chain profits. We compare these policies' social welfare including economic benefits, environmental benefits, consumer surplus, and government surplus. Results show that: (1) Subsidy policy has prominent performance in economic profits and consumer surplus. And the remaining capacity-based subsidy policy is better than the fixed subsidy policy. (2) The deposit-refund policy has a good performance in alleviating government financial pressure. (3) The performance of the reward-penalty mechanism depends on the reward-penalty intensity. Under certain reward-penalty intensity, the reward-penalty mechanism can increase the recycling rate significantly. (4) In terms of the influence of recycling models, alliance recycling models have the potential to achieve the highest social welfare.

Do Unprecedented Gasoline Prices Affect the Consumer Switching to New Energy Vehicles? An Integrated Social Cognitive Theory Model

From 2020 to 2022, the price of gasoline in China rose sharply, which may cause consumers to adopt new energy vehicles. However, the government subsidies in the yearly retreat for 2023 were completely abolished, which could hinder consumers’ switching behavior. The combination of these factors may affect consumer decision-making, making accurate analysis of consumer willingness to switch to new energy vehicles crucial for the development of this industry. The current study aims to investigate consumers’ vehicle switching intentions affected by multiple factors such as fuel prices, and we attempt to analyze these by combining personal and environmental factors from social cognitive theory. Data were collected through an online platform survey using developed reliable scales. The 464 responses were then synthesized using structural equation modeling and Bayesian networks, and the results showed that approximately 51% of consumers had high-level switching intentions to new energy vehicles; attitude, self-efficacy, environmental consciousness, and infrastructure barriers had the strongest effect on consumers’ vehicle switching intentions. This study assists in identifying the psychological demands of consumers switching to new energy vehicles and provides ideas for vehicle manufacturers and governments in terms of marketing strategies and policy formulation at crucial stages when new energy vehicles are in accelerated development.

The future of mobility-as-a-service: trust transfer across automated mobilities, from road to sidewalk.

While trust in different types of automated vehicles has been a major focus for researchers and vehicle manufacturers, few studies have explored how people trust automated vehicles that are not cars, nor how their trust may transfer across different mobilities enabled with automation. To address this objective, a dual mobility study was designed to measure how trust in an automated vehicle with a familiar form factor-a car-compares to, and influences, trust in a novel automated vehicle-termed sidewalk mobility. A mixed-method approach involving both surveys and a semi-structured interview was used to characterize trust in these automated mobilities. Results found that the type of mobility had little to no effect on the different dimensions of trust that were studied, suggesting that trust can grow and evolve across different mobilities when the user is unfamiliar with a novel automated driving-enabled (AD-enabled) mobility. These results have important implications for the design of novel mobilities.

Barriers in Replacement of Conventional Vehicles by Electric Vehicles in India

Electric vehicles are an emerging and evolving technology that brings in remarkable environmental gains over conventional vehicles, contributing significantly towards a decrease in fossil fuel dependence. However, infiltrating into the existing automobile market requires huge investment in charging facilities and intricate planning to make it more approachable to the consumers. Identifying the crucial challenges and finding a solution has been a major hurdle to the manufacturers. While various non-government agencies and government policies are urging both consumers and manufacturers to adopt electric mobility, many industries remain unguided. The paper aims to identify, study, and rank 12 of these influential challenges faced by the manufacturers based on their impact on enhancing the manufacturing and sales of electric vehicles in India using the triangular fuzzy number (TFN) method. Results obtained reveal that inadequate charging infrastructure is one of the biggest hurdles.