Matching Items (5)

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The History and Evolution of SUVs: Predicting Future SUVs

Description

Sport Utility Vehicles have grown to be one of the most popular vehicle choices in the automotive industry. This thesis explores the history of SUVs with their roots starting in

Sport Utility Vehicles have grown to be one of the most popular vehicle choices in the automotive industry. This thesis explores the history of SUVs with their roots starting in the 1930s up until 2020 in order to understand the essence of what an SUV is. The definition applied to the SUV for this thesis is as follows: a vehicle that is larger and more capable than the average sedan by offering more interior space, cargo area, and possibly off-road capability. This definition must be sufficiently broad to encompass the diverse market that manufactures are calling SUVs. Then the trends of what current (2020) SUVs are experiencing are analyzed from three major aspects: sociology, economics, and technology. Sociology focuses on the roles an SUV fulfills and the type of people who own SUVs. The economics section reviews the profitability of SUVs and their dependence on a nation’s economic strength. Technology pertains to the trends in safety features and other advances such as autonomous or electric vehicles. From these current and past trends, predictions could be made on future SUVs. In regards to sociology, trends indicate that SUVs will be more comfortable as newly entering luxury brands will be able to innovate aspects of comfort. In addition, SUVs will offer more performance models so manufacturers can reach a wider variety of demographics. Economic trends revealed that SUVs are at risk of losing popularity as the economy enters a hard time due to the COVID-19 pandemic. Technological trends revealed that hybrids and electric vehicles will now move into the SUV market starting with the more compact sizes to help improve manufacturer’s fleet fuel efficiency.

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Date Created
  • 2020-05

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Dyno Goes Green

Description

All around the automotive industry, the chassis dynamometer exists in a variety of configurations but all function to provide one common goal. The underlying goal is to measure a vehicle’s

All around the automotive industry, the chassis dynamometer exists in a variety of configurations but all function to provide one common goal. The underlying goal is to measure a vehicle’s performance by measuring torque output and taking that measurement to calculate horsepower. This data is crucial in situations of testing development vehicles or for tuning heavily modified vehicles. While the current models in the industry serve their purposes for what they were intended to do, in theory, an additional system can be introduced to the dyno to render the system into an electric generator.
The hardware will consist of electric motors functioning as a generator by reversing the rotation of the motor (regenerative braking). Using the dynamometer with the additional motor system paired with a local battery, the entire system can be run off by their tuning service. When considering the Dynojet and Dynapack dynamometer, it was calculated that an estimated return of 81.5% of electricity used can be generated. Different factors such as how frequent the dyno is used and for how long affect the savings. With a generous estimate of 6 hours dyno run time a day for 250 business days and the cost of electricity being 13.19 cents/kwh the Dynapack came out to $326.45 a year and $1424.52 for the Dynojet. With the return of electricity, the amount saved comes out to $266.18 for the Dynapack and $1161.50 for the Dynojet. This will alleviate electrical costs dramatically in the long term allowing for performance shops to invest their saved money into more tools and equipment.

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Created

Date Created
  • 2020-05

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Overall Market and Performance Comparison of Coil-over and Air Shock Absorbers

Description

The SAE Baja series is a competition that challenges university student teams on all aspects of designing, building, and testing an all-terrain vehicle. In the competition, the teams present their

The SAE Baja series is a competition that challenges university student teams on all aspects of designing, building, and testing an all-terrain vehicle. In the competition, the teams present their engineering analysis of all components of their vehicle to a panel of professional engineers to show why the team's design is the overall best in performance and in manufacturing cost. Currently Arizona State University's SAE Baja team does not have a method to analyze their vehicle's suspension system, especially on the car's shock absorbers. The current solution to this problem is to change the shock absorber parameters, test drive the car, and repeat the shock absorber tuning until the car is able to produce the performance that the team desires. The following paper introduces and demonstrates three different methods, ADAMS Car, SOLIDWORKS, and MATLAB, that can be used to analyze the suspension system and gather data that can be used in the competition presentation. ADAMS Car is a power software that is used in the automotive and other engineering fields. The program does have a steep learning curve, but once the team is comfortable using it, ADAMS is very helpful with subsystem analysis and full body analysis. SOLIDWORKS can be used to perform motion analysis and drop tests, which can then be exported into ADAMS for further analysis. MATLAB can be used to model the Baja vehicle as a quarter model, which makes it easier for the team to model. Using the methods presented in this paper, ASU's Baja team can test coil-over and air shock absorbers to determine which type is more suitable for the performance and overall cost of the whole vehicle.

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Date Created
  • 2016-12

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Comprehensive Model-Based Design and Analysis Approach for Thermal Management Systems in Hybridized Vehicles

Description

This research effort focuses on thermal management system (TMS) design for a high-performance, Plug-in Hybrid Electric Vehicle (PHEV). The thermal performance for various components in an electrified powertrain is investigated

This research effort focuses on thermal management system (TMS) design for a high-performance, Plug-in Hybrid Electric Vehicle (PHEV). The thermal performance for various components in an electrified powertrain is investigated using a 3D finite difference model for a complete vehicle system, including inherently temperature-sensitive components. The components include the electric motor (EM), power electronics, Energy Storage System (ESS), and Internal Combustion Engine (ICE).

A model-based design approach is utilized, where a combination of experimental work and simulation are integrated. After defining heat sources and heat sinks within the power train system, temporal and spatial boundary conditions were extracted experimentally to facilitate the 3D simulation under different road-load scenarios. Material properties, surface conditions, and environmental factors were defined for the geometrical surface mesh representation of the system. Meanwhile the finite differencing code handles the heat transfer phenomena via conduction and radiation, all convective heat transfer mode within the powertrain are defined using fluid nodes and fluid streams within the powertrain.

Conclusions are drawn through correlating experimental results to the outcome from the thermal model. The outcome from this research effort is a 3D thermal performance predictive tool that can be utilized in order to evaluate the design of advanced thermal management systems (TMSs) for alternative powertrains in early design/concept stages of the development process.

For future work, it is recommended that a full validation of the 3D thermal model be completed. Subsequently, design improvements can be made to the TMS. Some possible improvements include analysis and evaluation of shielding of the catalytic converter, exhaust manifold, and power electronics, as well as substituting for material with better thermal performance in other temperature-sensitive components, where applicable. The result of this improvement in design would be achieving an effective TMS for a high-performance PHEV.

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Created

Date Created
  • 2017

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Fuzzy logic based driving pattern recognition for hybrid electric vehicle energy management

Description

For years the automotive industry has been shifting towards hybridization and electrification of conventional powertrains due to increase in fossil fuel cost and environmental impact due heavy emission of Green

For years the automotive industry has been shifting towards hybridization and electrification of conventional powertrains due to increase in fossil fuel cost and environmental impact due heavy emission of Green House Gases (GHG) and various pollutants into atmosphere by combustion engine powered vehicles. Hybrid Electric Vehicles (HEV) have proved to achieve superior fuel economy and reduced emissions. Supervisory control strategies determining the power split among various onboard power sources are evolving with time, providing better fuel economies.

With increasing complexity of control systems driving HEV’s, mathematical modeling and simulation tools have become extremely advanced and have derived whole industry into adopting Model Based Design (MBD) and Hardware-in-the-loop (HIL) techniques to validate the performance of HEV systems in real world.

This report will present a systematic mythology where MBD techniques are used to develop hybrid powertrain, supervisory control strategies and control systems. To validate the effectiveness of various energy management strategies for HEV energy management in a real world scenario, Conventional rule-based power split strategies are compared against advanced Equivalent Consumption Minimization Strategy (ECMS), in software and HIL environment.

Since effective utilization of the fuel reduction potential of a HEV powertrain requires a careful design of the energy management control methodology, an advanced ECMS strategy involving implementation with Fuzzy Logic to reduce computational overload has been proposed. Conventional real-time implementation of ECMS based strategy is difficult due to the involvement of heavy computation. Methods like Fuzzy Logic based estimation can be used to reduce this computational overload.

Real-time energy management is obtained by adding a Fuzzy Logic based on-the-fly algorithm for the estimation of driving profile and adaptive equivalent consumption minimization strategy (A-ECMS) framework. The control strategy is implemented to function without any prior knowledge of the future driving conditions. The idea is to periodically refresh the energy management strategy according to the estimated driving pattern, so that the Battery State of Charge (SOC) is maintained within the boundaries and the equivalent fuel consumption is minimized. The performance of the presented Fuzzy Logic based adaptive control strategy utilizing driving pattern recognition is benchmarked using a Dynamic Programming based global optimization approach.

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Created

Date Created
  • 2015