Browse Topic: Market research
The switch to electrified off-highway vehicles can help reduce reliance on hydraulic components that decrease system efficiency via parasitic losses. The off-highway machine industry is embracing new technologies to optimize operations, specifically regarding electric and hybrid off-highway equipment. The electric off-highway equipment market is poised for growth, with an expected 12.5% compound annual growth rate (CAGR) from 2025-2034, reaching over $17 billion, according to Market Research Future. These off-highway vehicles operate on tough terrain and require unprecedented amounts of power for long duty cycles. Diesel engines have always been the conventional application for this kind of work, but now hybrid and electric vehicles are starting to gain traction thanks to new innovations and more investment. While the implications of replacing traditional combustion engines with hybrid or electric counterparts can be intimidating, learning the challenges and opportunities each option holds gives end users the power to determine what's right for their needs.
In addition to providing safety advantages, sound and vibration are being utilized to enhance the driver experience in Battery Electric Vehicles (BEVs). There's growing interest and investment in using both interior and exterior sounds for pedestrian safety, driver awareness, and unique brand recognition. Several automakers are also using audio to simulate virtual gear shifting of automatic and manual transmissions in BEVs. According to several automotive industry articles and market research, the audio enhancements alone, without the vibration that drivers are accustomed to when operating combustion engine vehicles, are not sufficient to meet the engagement, excitement, and emotion that driving enthusiasts expect. In this paper, we introduce the use of new automotive, high-force, compact, light-weight circular force generators for providing the vibration element that is lacking in BEVs. The technology was developed originally for vibration reduction/control in aerospace applications, has been recently tested in various vehicles, and demonstrates the effectiveness for providing a real haptic feel across the entire vehicle. Shaking the vehicle globally provides a unique capability for BEVs, including Hybrid Electric Vehicles and for helping to create a smooth transition between Gas and Electric power, for example. The technology can be used to generate and emulate high-performance, high power, combustion engine feel, including idle, engine run-up/acceleration, simulated gear shifts, and Advanced Driving Assistance and Systems (ADAS) haptic indicators. The optional and customizable vibration can also mask road vibration which becomes very noticeable in otherwise smooth BEVs and can provide the perfect supplement to existing audio enhancements and gear shifting features. Additionally, the paper describes how the force generating device can be packaged in a light weight, compact, low-power manner. The technology will be compared to other force generating methods, and discuss its pros and cons.
This study aims to explore the multifaceted influencing factors of market acceptance and consumer behavior of low-altitude flight services through online surveys and advanced neuroscientific methods (such as functional magnetic resonance imaging fMRI, electroencephalography EEG, functional near-infrared spectroscopy fNIRS) combined with artificial intelligence and video advertisement quantitative analysis. We conducted an in-depth study of the current trends in low-altitude flight vehicle development and customer acceptance of low-altitude services, focusing particularly on the survey methods used for market acceptance. To overcome the influence of strong opinion leaders in volunteer group experiments, we designed specialized surveys targeting broader online and social media groups. Utilizing specialized knowledge in aviation psychology, we designed a distinctive questionnaire and, within just 7 days of its launch, gathered a significant number of valid responses. The data was then analyzed using AI to provide original, insightful data on the acceptance of low-altitude services. Furthermore, we addressed the limitations of traditional manual survey methods by designing an advanced system combining EEG and AI analysis to automatically generate surveys by measuring neural and physiological responses while subjects watched video advertisements for low-altitude services. Our research offers a comparison with existing online survey forms and proposes specific predictions to potentially improve the accuracy of online surveys.
The automotive industry is going through one of its greatest restructuring, the migration from internal combustion engines to electric powered / internet connected vehicles. Adapting to a new consumer who is increasingly demanding and selective may be one of the greatest challenges of this generation, Original Equipment Manufacturers (OEM) have been struggling to keep offering a diversified variety of features to their customers while also maintaining its quality standards. The vehicles leave the factory with an embedded SIM Card and a telematics module, which is an electronic unit to enable communication between the car, data center. Connected vehicles generate tens of gigabytes of data per hour that have the potential to be transformed into valuable information for companies, especially regarding the behavior and desires of drivers. One of the techniques used to gather quality feedback from the customers is the NPS it consists of open questions focused on top-of-mind feedback. Here is where AI and ML comes into play, using NLP and several other computational techniques to download, extract, structure, read, process, understand and categorize all this data into specific predetermined categories, allowing engineers to accelerate fixing quality issues and improving user experience. The ML model developed in this article identify costumer complains in an enormous data lake and groups them into categories. After a significative amount of data is collected and grouped into it enables the algorithm to predict future trends and together with real time connected vehicle data the model can alert the responsible engineers to develop an action to solve the problem without more customers even actually experience the failure. The ML algorithm is still on its development phase, but the initial results are promising, we have successfully processed more them 6 millioncustomers feedback finding problems with precision and accuracy close to 90%.
During the early phase of vehicle development, one of the key design attributes to consider are the interior storages for occupants. Internal storage is the pillar that is responsible for user’s comfort and make into customer comfort needs in engineer metrics. Therefore, it is one of the key requirements to be considered during the vehicle design. The vehicle has some interior storages, like storages on door trim, floor console and IP and to define the best solution for the customer, engineering team has certain internal vehicle characteristics such as the volume and size of storage are engineer metrics that influence the perception of comfort for occupants. One specific characteristic influencing satisfaction is the glove box volume, which is the subject of this paper. The objective of this project is to analyze the relationship between the glove box volume with the occupant’s satisfaction under real world driving conditions, based on research, statistical data analysis and dynamic clinics.
Stryker is one of the world’s leading medtech companies. From medical and surgical to neurotechnology and orthopedics, Stryker’s innovative products include digital and enabling technologies to support customers and drive the growth of the company. Stryker is “a category leader across all of its businesses in one category or another. It has a diversified customer base with massive potential upsides, including advanced imaging, stroke care, safety, and medical robotics,” according to market analysis firm Seeking Alpha. “While there are always leaders in specific fields, very few fields have as clear a leader as Stryker seems to be in these segments.”1
Defining a global powertrain roadmap is a challenging yet crucial step towards meeting sustainability goals. OEMs usually start from independent market research, competition, market requirements and regulations to build this roadmap. However, how do they make sure that the preliminary powertrain choices they make will meet the defined requirements? How big are the assumptions they take at this early stage of powertrain development? In this paper a holistic system simulation method is proposed to assess, compare, and rank different powertrain choices for different markets under different regulations, from the tailpipe CO2 emissions point of view. This method relies on simplified powertrain and vehicles graphical models that are compared together for different mission profiles, corresponding to different markets. It includes battery electric, hybrid, plug-in hybrid, fuel cell electric and conventional vehicles. Fuel consumption, tailpipe CO2 emissions and vehicle longitudinal performances are the compared criteria. The outcome of this study is a fictive powertrain roadmap that has been refined using relatively simple and fast simulations. The next step is to extend this roadmap study to a complete fleet analysis as well as a full life cycle assessment.
Even before the pandemic disrupted patients’ in-person interactions with their healthcare providers, visionary designers had made significant strides in developing new options for self-operated medical devices. Innovations in wearable technologies along with more streamlined and intuitive handheld options are gaining traction at a rapid pace. A Transparency Market Research analysis put the value of the global remote patient monitoring devices market at $8 billion in 2019, with a projected CAGR of 12.5 percent from 2020 to 2030.1
The automotive landscape has under-gone a revolution in technology demands in the last two decades. Today, automakers can integrate cutting edge silicon chips, space and energy efficient electronics, and powerful software into their vehicles. As a result, differentiation in the modern automotive industry is no longer derived from mechanical and physical design, but by the vehicle features and functionality that embedded chips, electronics, and software deliver. Analogizing today's vehicles to 'smartphones on wheels’ has never been closer to reality.
BSR noise is an important parameters for customer discomfort. According to a market survey, squeaks and rattles are the third most important customer concern in cars after six months of ownership. The high quality acoustic environment of a car, annoying noises like buzz, squeak, and rattle is related to various parameters such as material assembly, tolerance, aging, humidity, surface contact, and surface hardness. BSR is originated from frictional movement between two parts or from the impact between two parts. The rattle noise is caused when surfaces close to each other move perpendicular to each other due to insufficient attachments or insufficient structural strength. In our study, we have shown the impact of various front suspension component in front suspension assembly on BSR noise and also the method to detect and attenuate the same. A methodical analysis process is shown to identify the contributing part and resolve the BSR issue. A detailed case study on vehicle level is carried out to achieve the required BSR score.
With recent advances in electric vehicles, there is a plethora of powertrain topologies and components available in the market. Thus, the performance of electric vehicles is highly sensitive to the choice of various powertrain components. This paper presents a multi-objective optimization model that can optimally select component sizes for batteries, supercapacitors, and motors in regular passenger battery-electric vehicles (BEVs). The BEV topology presented here is a hybrid BEV which consists of both a battery pack and a supercapacitor bank. Focus is placed on optimal selection of the battery pack, motor, and supercapacitor combination, from a set of commercially available options, that minimizes the capital cost of the selected power components, the fuel cost over the vehicle lifespan, and the 0-60 mph acceleration time. Available batteries, supercapacitors, and motors are from a market survey. The considered lifespan is taken as 10 years, and the traveling distance is estimated at 50.9 miles per day using a combination of standard driving cycles. The resulting optimization problem is solved with the help of a quasi-static powertrain model which is developed using MATLAB/Simulink. A Genetic Algorithm is used to find the optimal solution in the case study. Normalized weighting factors are given to help users meeting their preferred performance during the power component design. Battery packs in the case study are chosen from LiFePO4 18650 cells with total capacity up to 100 kWh. Seven available types of supercapacitors along with 6 popular motors are also included in the design options. Two samples of the design results are compared to analyze the relevant tradeoff between performance indicators and cost.
With the development of autonomous driving technology, automated buses have begun trial operations in many cities around the world, and marketization has become an important issue. In order to explore the influencing factors of the public's willingness to use automated buses, two rounds of surveys were conducted. Firstly, the importance of the attributes of automated buses was studied, based on which questionnaires on willingness to use automated buses were designed. Using data from 266 questionnaires collected, a logistic regression model was established. Model results show that demographic variables and historical travel behavior characteristics will have a significant impact. Women are less willing to choose automated buses than men, and older people aged above 50 are more likely to use the mode. People who often use regular buses to travel have higher willingness to choose automated buses than people using other modes. Among people using other modes including private cars, subways, ride-hailing, bicycles, etc., those using private cars and bicycles have higher potential to become automated bus users, while ride-hailing and taxi users have the lowest potential. It is also found that being equipped with a driver, having dedicated lanes, short bus headways, low fares, and short walking distances will increase people’s willingness to use automated buses. The research results can provide a theoretical basis for improving the acceptance of automated buses. Suggestions for the operation and promotion of automated buses are put forward.
Because the scooter is convenient, the market continues increasing. As a result of market survey, it was revealed that the scooter repeated sudden acceleration and deceleration. Therefore, the oil consumption may occur in special pattern. The oil consumption is an important development target. On the other hand, the oil consumption is complicated phenomenon. In this study, the sulfur trace method was used for measurement of the oil consumption. When a throttle was closed and the intake pipe became the negative pressure, the SO2 emission (oil consumption) increases. Then, using a glass cylinder and a high-speed camera, motion of the engine oil was observed. It was revealed that the engine oil rises to the combustion chamber after stopping at the 4th Land. By a combination of the sulfur trace test and the visualization test, oil consumption phenomenon of the scooter was able to be understood.
Small internal combustion engines outperform batteries and fuel cells in regards to weight for a range of applications, including consumer products, marine vehicles, small manned ground vehicles, unmanned vehicles, and generators. The power ranges for these applications are typically between 1 kW and 10 kW. There are numerous technical challenges associated with engines producing power in this range resulting in low power density and high specific fuel consumption. As such, there is a large range of engine design solutions that are commercially available in this power range to overcome these technical challenges. A market survey was conducted of commercially available engines with power outputs less than 10 kW. The subsequent analysis highlights the trade-offs between power output, engine weight, and specific fuel consumption. These engines are analyzed to show the benefits and disadvantages of different engine design parameters including fuel type, number of strokes per cycle, number of cylinders, intake pressure, and cooling strategy. A Pareto frontier analysis is conducted to identify the top performing engines based on the output power and the total power system weight. Recommended designs are presented for different ranges of output powers.
The Deep Orange program immerses automotive engineering students into the world of an OEM as part of their 2-year graduate education. In support of developing the program’s seventh vehicle concept, the students studied the sponsoring brand essence, conducted market research, and made a heuristic assessment of competitor vehicles. The upfront research lead to the definition of target customers and setting vehicle level targets that were broken down into requirements to develop various vehicle sub-systems. The powertrain team was challenged to develop a scalable propulsion concept enabled by a common vehicle architecture that allowed future customers to select (at the point of purchase) among various levels of electrification best suiting their needs and personal desires. Four different configurations were identified and developed: all-electric, two plug-in hybrid electric configurations, and an internal combustion engine only. The electrified powertrain comprises of an innovative thermal system using the structural rocker beams as heat exchangers, thereby eliminating the need for conventional radiators. Two cargo compartments (one at each end of the vehicle) were realized through efficient packaging of the electric units and an internal combustion engine in the front and rear, respectively, with a modular energy (battery and/or fossil fuel) storage system located under the passenger compartment. Simulation tools were used to size the powertrain components for each of the four propulsion configurations. The efficiency of the thermal system was verified using CFD analyses in combination with preliminary bench testing. The outcome of the Deep Orange 7 project was a drivable vehicle demonstrator designed, engineered, built and tested by the student team. Industry partners functioned as project sponsors as well as mentors throughout the 2-year development cycle.
This study is an attempt to develop a decision support and control structure based on fuzzy logic for deployment of automotive airbags. Airbags, though an additional safety feature in vehicles, have proven to be fatal at various instances. Most of these casualties could have been avoided by using seat belts in the intended manner that is, as a primary restraint system. Fatalities can be prevented by induction of smart systems which can sense the presence and differentiate between passengers and conditions prevailing at a particular instant. Fuzzy based decision making has found widespread use due to its ability to accept non-binary or grey data and compute a reliable output. Smart airbags also allow the Airbag Control Unit to control inflation speed depending on instantaneous conditions. The objective of this study is to develop a decision system which could control a microcontroller using IF-THEN statements and thereby control and optimize airbag deployment speed depending on the accident circumstances. After an in-depth study of literature available on accidents and airbag deployment circumstances, rules for fuzzy decision structure have been drafted and rule surfaces computed. The study involved collection of ergonomic data of over 20 passenger car variants of some prominent automobile companies sold in the Indian market and then their careful analysis in conjunction with deceleration of vehicle, distance from airbag panel, velocity of vehicle, seat belt and weight of passenger. The input variables include the aforementioned factors and multilevel inflation speed is the output variable of the proposed decision structure.
The competitiveness within the automotive sector increases constantly. Research institutes, universities and manufacturers are commonly trying to discover the new trends and to develop novel technologies. Nevertheless, it is important to understand if a certain technology is worth researching. In order to do that, a state of the art survey is necessary which is usually divided in two main groups: Literature Information and Market Analysis. The literature information regards papers, congress proceedings, books, among other types of formal publication. The market analysis is responsible to gather information within the manufacturers press releases, websites and events, for example. Even though, depending on the technology, those two topics are not enough to reveal the importance of a given technology. Therefore, it is necessary to search the patents database, where it is possible to find the development status of a device. However, a patent survey it is not trivial and some methodology is needed. This papers aims on proposing a proper methodology for patent survey focused on automotive mechanisms such as gear trains, suspensions, steering and engines. The methodology it is explained step by step and a brief overview about patents is given. At the end a case study regarding the Variable Compression Ratio engines is given. This proper methodology for patent survey allowed the researchers to propose an enhanced classification for the VCR topic, to help defining design requirements and to understand the potential for innovation within the desired topic.
The Deep Orange framework is an integral part of the graduate automotive engineering education at Clemson University International Center for Automotive Research (CU-ICAR). The initiative was developed to immerse students into the world of an OEM. For the 6th generation of Deep Orange, the goal was to develop an urban utility/activity vehicle for the year 2020. The objective of this paper is to describe the development of a multimaterial lightweight Body-in-White (BiW) structure to support an all-electric powertrain combined with an interior package that maximizes volume to enable a variety of interior configurations and activities for Generation Z users. AutoPacific data were first examined to define personas on the basis of their demographics and psychographics. The resulting market research, benchmarking, and brand essence studies were then converted to consumer needs and wants, to establish vehicle target and subsystem requirement, which formed the foundation of the Unique Selling Points (USPs) of the concept. The various sub-systems within the vehicle were then developed; a systems integration approach was used to balance design, engineering, and project (cost, weight, and timing) compromises. The paper discusses the BiW as an enabler of the vehicle USPs, including an very low, flat floor, a utility-oriented asymmetric door concept, and an integrated hatch and rear bumper which create a low lift-over height for loading and unloading. The development of the topology, geometry, and properties of the BiW structure in relation to the chassis, powertrain, and occupant packaging elements required balancing design space, functionality, cost, and weight. Novel manufacturing processes, materials, and joining techniques are described in addition to elaborations on the final realization of the BiW concept.
The Deep Orange framework is an integral part of the graduate automotive engineering education at Clemson University International Center for Automotive Research (CU-ICAR). The initiative was developed to immerse students into the world of an OEM. For the 6th generation of Deep Orange, the goal was to develop an urban utility/activity vehicle for the year 2020. The objective of this paper is to explain the interior concept that offers a flexible interior utility/activity space for Generation Z (Gen Z) users. AutoPacific data were first examined to define personas on the basis of their demographics and psychographics. The resulting market research, benchmarking, and brand essence studies were then converted to consumer needs and wants, to establish technical specifications, which formed the foundation of the Unique Selling Points (USPs) of the concept. Then the various sub-systems within the vehicle were developed; a systems integration approach was used to balance design, engineering and project (cost, weight and timing) compromises. The vehicle provides a flexible interior concept designed to support the active lifestyles of Gen Z that enables a broad range of use cases including stationary activities. The paper discusses the occupant packaging, seating, personalization and customization of the interior, power supply, infotainment, color selection, and interior lighting concepts which provide novel ways to support users in urban environments.
Reducing vehicle fuel consumption has become one of the most important issues in recent years in connection with environmental concerns such as global warming. Therefore, in the vehicle development process, attention has been focused on reducing aerodynamic drag as a way of improving fuel economy. When considering environmental issues, the development of vehicle aerodynamics must take into account real-world driving conditions. A crosswind is one of the representative conditions. It is well known that drag changes in a crosswind compared with a condition without a crosswind, and that the change depends on the vehicle shape. It is generally considered that the influence of a crosswind is relatively small since drag accounts for a small proportion of the total running resistance. However, for electric vehicles, the energy loss of the drive train is smaller than that of an internal combustion engine (ICE) vehicle. Therefore, drag represents a relatively larger proportion of the total running resistance. That makes it necessary to consider the influence of a crosswind in order to reduce electric power consumption in real-world driving. In this study, representative test conditions taking into account a crosswind were proposed for wind tunnel tests based on an analysis of U.S. market data such as vehicle speeds and wind speeds. The test results made clear the mechanism causing drag to change under the representative test conditions. The representative test conditions were calculated by the Monte Carlo method using real-world driving data. It was found that a yaw angle of 4 degrees is the most influential yaw angle. The mechanism causing drag to change was studied in wind tunnel tests. The factors affecting the change in drag were identified, and measures for reducing that change were examined.
The Deep Orange framework is an integral part of the graduate automotive engineering education at Clemson University International Center for Automotive Research (CU-ICAR). The initiative was developed to immerse students into the world of an OEM. For the sixth generation of Deep Orange, the goal was to develop an urban utility/activity vehicle for the year 2020. The objective of this paper is to describe the development and implementation of a dual-purpose powertrain system enabling vehicle propulsion as well as stationary activities of the Deep Orange 6 vehicle concept. AutoPacific data were first examined to define personas on the basis of their demographics and psychographics. The resulting market research, benchmarking, and brand essence studies were then converted to consumer needs and wants, to establish vehicle target and subsystem requirement, which formed the foundation of the Unique Selling Points (USPs) of the concept. The Deep Orange 6 vehicle contains a very low floor supporting the active lifestyles of the target consumers through re-configurability of the interior, which enables a broad range of use cases including invehicle stationary activities. This concept required the development of a shallow dual-purpose electrical energy storage and power conversion system for the purpose of propelling the vehicle as well as powering various 110 VAC in-vehicle stationary activities for an extended period of time, at low noise levels and with zero local emissions. The paper explains simulation based sub-system sizing and component selection to meet the overall vehicle performance and fuel economy targets. Furthermore, special attention will be paid to the development of a control logic keeping functional safety in mind especially related to the two modes of operation of the vehicle’s energy supply and power conversion system. The paper concludes with a description of the subsystem testing, including driveline and vehicle integration as part of the vehicle performance validation process.
The evaluation of perceived comfort inside a car during the early stages of the design process is still an open issue. Modern technologies like CAE (Computer Aided Engineering) and DHM (Digital Human Modeling) already offer several tools for a preventive evaluation of ergonomic parameters for car drivers using detailed CAD (Computer Aided Design) models of car interiors and by a MBS (multi-body-system) solver for evaluating movements and interactions. Such evaluations are, nonetheless, not sufficient because the subjectivity of comfort perception is due to factors that are very difficult to evaluate in the early stage of design. Physical prototypes are needed and these are often too expensive to be realized. In the last 30 years, several researchers have tried to develop methods to objectivize comfort performance but most of these methods are based on questionnaires, market research, or physiological and biomechanical analyses, and need devices or interactions that modify perceived comfort. Recently, the authors of this study developed a software tool named CaMAN® for postural comfort evaluations of upper limbs. The software employs a static analysis of human joints in a working environment (the car cockpit is assumed to be a working environment). Some software, like AnyBody™, allows evaluations of the muscular efforts made during body actions. In the literature, it is possible to find several papers such as Na et al. [26] and Telfer et al. [40] that demonstrate a correlation between muscular effort and perceived comfort. Empirical evidence suggests that better comfort is related to lower muscular activity. This paper shows the results obtained from numerical and experimental analyses using AnyBody™ and CaMAN®. Simulations of the static and dynamic behaviors of a car driver using a steering wheel are performed. The results show the differences between preventive analyses of perceived comfort that are made with and without an applied load for a subject in the fiftieth percentile. This paper shows the results obtained from numerical and experimental analyses using AnyBody™ and CaMAN®. Simulations of the static and dynamic behaviors of a car driver using a steering wheel are performed. The results show the differences between preventive analyses of perceived comfort that are made with and without an applied load for a subject in the fiftieth percentile.
Items per page:
50
1 – 50 of 155