Search Results

Battery temperature variations have a strong effect on both battery aging and battery performance. Significant temperature variations will lead to different battery behaviors. This influences the performance of the Hybrid Electric Vehicle (HEV) energy management strategies. This paper investigates how variations in battery temperature will affect Lithium-ion battery aging and fuel economy of a HEV. The investigated energy management strategy used in this paper is the Equivalent Consumption Minimization Strategy (ECMS) which is a well-known energy management strategy for HEVs. The studied vehicle is a Honda Civic Hybrid and the studied battery, a BLS LiFePO4 3.2Volts 100Ah Electric Vehicle battery cell. Vehicle simulations were done with a validated vehicle model using multiple combinations of highway and city drive cycles. The battery temperature variation is studied with regards to outside air temperature.

This paper studies the hardware-in-the-loop (HiL) design of a power-split hybrid electric vehicle (HEV) for the research of HEV lithiumion battery aging. In this paper, an electrochemical model of a lithium-ion battery pack with the characteristics of battery aging is built and integrated into the vehicle model of Autonomie® software from Argonne National Laboratory. The vehicle model, together with the electrochemical battery model, is designed to run in a dSPACE real-time simulator while the powertrain power distribution is managed by a dSPACE MicroAutoBoxII hardware controller. The control interface is designed using dSPACE ControlDesk to monitor the real-time simulation results. The HiL simulation results with the performance of vehicle dynamics and the thermal aging of the battery are presented and analyzed.

Engineering education at the University level is enhanced by competition-based projects. The SAE Clean Snowmobile Challenge is a prime example of how competition-based engineering education benefits the small engines industry and improves the engineering talent pool of the nation in general. For the past several decades, SAE has encouraged young engineers to compete in designing off road vehicles (Baja SAE ®), small race cars (Formula SAE ®), remote control airplanes (Aero Design ®), high mileage vehicles (Supermileage ®) and robots (Walking Robot ®). Now a new competition, the SAE Clean Snowmobile Challenge ™ (CSC), based on designing a cleaner and quieter snowmobile has led to a new path for young engineers to explore the challenges of designing engines that emit less pollution and noise. The paper will summarize the results of the most recent Clean Snowmobile Challenge 2006 and document the successes of the past seven years of the Challenge.

Michigan Technological University has established a broad-based university-wide research initiative, termed Wood-to-Wheels (W2W), to develop and evaluate improved technologies for growing, harvesting, converting, and using woody biomass in renewable transportation fuel applications. The W2W program bridges the entire biomass development-production-consumption life cycle with research in areas including forest resources, bioprocessing, engine/vehicle systems, and sustainable decisions. The W2W chain establishes a closed cycle of carbon between the atmosphere, woody biomass, fuels, and vehicular systems that can reduce the accumulation of CO2 in the atmosphere. This paper will summarize the activities associated with the Wood-to-Wheels initiative and describe challenges and the potential benefits that are achievable.

Due to the low cost of the battery cells and excellent performance at ambient temperature, Lithium-ion (Li-ion) battery is a promising technology for propulsion applications. However, the performance of Li-ion batteries erodes drastically at extreme temperatures (above 65 °C or below 0 °C). Therefore, in order to maintain battery life and performance, it is crucial to keep the batteries within the temperature range where their operating characteristics are optimal. The need for expensive and complex thermal management systems has in fact kept the Li-ion technology from becoming the first choice for Hybrid Electric Vehicle (HEV) applications. In this paper, we propose a Phase Change Material (PCM) for the temperature control. Due to its high heat capacity, PCM absorbs the heat dissipated by the battery. As long as the heat emitted by the battery does not melt the PCM completely, the system is stable.

This paper analyzes the control of the series-parallel powersplit used in the 2001 Michigan Tech FutureTruck. An electronic throttle controller is implemented and a new control algorithm is proposed and tested. A vehicle simulation has been created in MATLAB and the control algorithm implemented within the simulation. A program written in C has also been created that implements the control algorithm in the test vehicle. The results from both the simulation and test vehicle are presented and discussed and show a 15% increase in fuel economy. With the increase in fuel economy, and through the use of the original exhaust after treatment, lower emissions are also expected.

Living in the era of rising environmental sensibility and increasing gasoline prices, the development of a new environmentally friendly generation of vehicles becomes a necessity. Hybrid electric vehicles are one means of increasing propulsion system efficiency and decreasing pollutant emissions. In this paper, the series-parallel power-split configuration for Michigan Technological University's FutureTruck is analyzed. Mathematical equations that describe the hybrid power-split transmission are derived. The vehicle's differential equations of motion are developed and the system's need for a controller is shown. The engine's brake power and brake specific fuel consumption, as a function of its speed and throttle position, are experimentally determined. A control strategy is proposed to achieve fuel efficient engine operation. The developed control strategy has been implemented in a vehicle simulation and in the test vehicle.

This study investigates a methodology in which the general public's subjective interpretation of vehicle handling and performance can be predicted. Several vehicle handling measurements were acquired, and associated metrics calculated, in a controlled setting. Human evaluators were then asked to drive and evaluate each vehicle in a winter driving school setting. Using the acquired data, multiple linear regression and artificial neural network (ANN) techniques were used to create and refine mathematical models of human subjective responses. It is shown that artificial neural networks, which have been trained with the sets of objective and subjective data, are both more accurate and more robust than multiple linear regression models created from the same data.

Most of the history of systems engineering has been focused on processes for engineering a single complex system. However, most large enterprises design, manufacture, operate, sell, or support not one product but multiple product lines of related but varying systems. They seek to optimize time to market, costs of development and production, leverage of intellectual assets, best use of talented human resources, overall competitiveness, overall profitability and productivity. Optimizing globally across multiple product lines does not follow from treating each system family member as an independently engineered system or product. Traditional systems engineering principles can be generalized to apply to families. This article includes a multi-year case study of the actual use of a generic model-based systems engineering methodology for families, Systematica™, across the embedded electronic systems products of one of the world's largest manufacturers of heavy equipment.

The automotive industry is dramatically changing. Many automotive Original Equipment Manufacturers (OEMs) proposed new prototype models or concept vehicles to promote a green vehicle image. Non-traditional players bring many latest technologies in the Information Technology (IT) industry to the automotive industry. Typical vehicle’s characteristics became wider compared to those of vehicles a decade ago, and they include not only a driving range, mileage per gallon and acceleration rating, but also many features adopted in the IT industry, such as usability, connectivity, vehicle software upgrade capability and backward compatibility. Consumers expect the latest technology features in vehicles as they enjoy in using digital applications in laptops and mobile phones. These features create a huge challenge for a design of a new vehicle, especially for a human-machine-interface (HMI) system.

Today’s business climate and economy demand new, innovative strategies from the initial kickoff of research and development - to the mining of ore from the earth - to the final inspection of a finished product in a mid-western factory. From startup companies with two employees to the largest companies, the world faces new and challenging requirements every day. The demands from companies, customers, executives, and shareholders continue to drive for higher outputs with more efficient use of personnel and investments. Fortunately, the rate of technology continues to exponentially accelerate, which allows those at the cutting edge of technology to capitalize. Caterpillar has been a pioneer in advanced technology since its inception and has been developing the foundation for autonomy over the past four decades.

In this study, the impact of the intake valve timing on knock propensity is investigated on a dual-fuel engine which leverages a low octane fuel and a high octane fuel to adjust the fuel mixture’s research octane rating (RON) based on operating point. Variations in the intake valve timing have a direct impact on residual gas concentrations due to valve overlap, and also affect the compression pressure and temperature by altering the effective compression ratio (eCR). In this study, it is shown that the fuel RON requirement for a non-knocking condition at a fixed operating point can vary significantly solely due to variations of the intake valve timing. At 2000 rpm and 6 bar IMEP, the fuel RON requirement ranges from 80 to 90 as a function of the intake valve timing, and the valve timing can change the RON requirement from 98 to 104 at 2000 rpm and 14 bar IMEP.

This paper studies the nonlinear model predictive control for a power-split Hybrid Electric Vehicle (HEV) power management system to improve the fuel economy. In this paper, a physics-based battery model is built and integrated with a base HEV model from Autonomie®, a powertrain and vehicle model architecture and development software from Argonne National Laboratory. The original equivalent circuit battery model from the software has been replaced by a single particle electrochemical lithium ion battery model. A predictive model that predicts the driver’s power request, the battery state of charge (SOC) and the engine fuel consumption is studied and used for the nonlinear model predictive controller (NMPC). A dedicated NMPC algorithm and its solver are developed and validated with the integrated HEV model. The performance of the NMPC algorithm is compared with that of a rule-based controller.

In this paper, two correlations, Windowed Selected Moving Autocorrelation (WSMA) and Tri-Correlation (TriC), are introduced and discussed. The WSMA is simpler than the conventional autocorrelation. WSMA uses less data points to obtain useful signal content at desired frequencies. The computational requirement is therefore reduced compared to the conventional autocorrelation. The simplified TriC provides improved signal to noise separation capability than WSMA does while still requiring reduced computational effort compared to the standard autocorrelation. Very often, computation resource limitation exists for real-time applications. Therefore, the WSMA and TriC offer more opportunities for real-time monitor and feedback control applications in the frequency domain due to their high efficiencies. As an example, applications in internal combustion (IC) engine misfire detection are presented. Simulation and vehicle test results are also presented in this paper.

The paper describes an experimental activity on the spatial and temporal liquid- and vapor-phase distributions of diesel fuel at engine-like conditions. The influence of the k-factor (0 and 1.5) of a single-hole axial-disposed injector (0.100 mm diameter and 10 L/d ratio) has been studied by spraying fuel in an optically-accessible constant-volume combustion vessel. A high-speed imaging system, capable of acquiring Mie-scattering and Schlieren images in a near simultaneous fashion mode along the same line of sight, has been developed at the Michigan Technological University using a high-speed camera and a pulsed-wave LED system. The time resolved pair of schlieren and Mie-scattering images identifies the instantaneous position of both the vapor and liquid phases of the fuel spray, respectively. The studies have been performed at three injection pressures (70, 120 and 180 MPa), 23.9 kg/m3 ambient gas density and 900 K gas temperature in the vessel.

Battery State of Charge (SOC) constraints are used to prevent the battery in Hybrid Electric Vehicles (HEVs) from over-charging or over-discharging. These constraints strongly influence the power-split of the HEV. This paper presents results on how Battery State of Charge (SOC) constraints effects Lithium ion battery aging and fuel economy when using the Equivalent Consumption Minimization Strategy (ECMS). The vehicle studied is the Honda Civic Hybrid. The battery used is A123 Systems’ ANR26650 battery cell. Vehicle simulation uses multiple combinations of highway and city drive cycles. For each combination of drive cycles, nine SOC constraints ranges are used. Battery aging is evaluated using a semi-empirical model combined with the accumulated Ah-throughput method which uses, as an input, the battery SOC trajectory from the vehicle simulations. The simulation results provide insight into how SOC constraints effect fuel economy as well as battery aging.

This paper identifies potential performance benefits and computational costs of applying advanced multivariable control theory concepts to coordinate the control of a general multi-degree-of-freedom fuel system. The control variables are injection duration and pressure. The focus is on the design of a robust multi-input multi-output controller using H-infinity and mu synthesis methodology to coordinate the control of injection duration and pressure; reduce overshoots and system sensitivity to parameter variations caused by component aging. Model reduction techniques are used to reduce the order of the H-infinity controller to make it practically implementable. Computer simulation is used to test the robust performance of a generic engine and fuel system model controlled by the reduced order H-infinity controller and a traditional proportional plus integral controller.

The Illinois Institute of Technology (IIT) Interprofessional Projects (IPRO®) Program engages multidisciplinary teams of students in semester-long projects, with a total of thirty to thirty-five different projects offered every semester. This program greatly contributes to IIT's signature undergraduate education experience, with each interprofessional course delivering a team-oriented, project-based requirement within the undergraduate curriculum. Among its many benefits, each interprofessional course offers students the opportunity to integrate the education and research environment of the university to tackle real-world problems. In the process, students get the chance to develop and emerge from the experience with maturity, confidence, and valuable professional skills that are highly sought after in the workplace, simultaneously preparing them for the realities of today's global, highly-competitive environment [1].

Most pieces of mobile equipment (machines) produce an audible signal to indicate movement in the rearward direction. This signal is intended to alert nearby personnel of the potential danger associated with the machine moving in a direction where the operator may not be able to see people or objects in the machine path. Anyone who has been on or near a construction site recognizes the familiar “beep…beep…beep…” of this signal as the backup alarm. To be effective, the backup alarm must be discernible, timely, and relevant to those people where a reaction is intended. As machine designers respond to various sound directives for reducing sound emissions (including the backup alarm), the performance of the backup alarm is receiving special attention. An emerging solution is an alarm capable of sensing ambient sounds and producing an audible signal proportional to the sensed sound levels-a self-adjusting backup alarm.

The Society of Automotive Engineers' (SAE) Clean Snowmobile Challenge 2004 (CSC 2004) was held at Michigan Technological University in Houghton, Michigan, from March 15 - 20, 2004. The Clean Snowmobile Challenge has been a competition in the SAE Collegiate Design Series since 2000, and began in Jackson Hole, Wyoming, as a response to rising concerns about snowmobiling in environmentally-sensitive areas. Teams from fifteen universities competed in CSC 2004. The winning snowmobile (sled) was developed by the University of Wisconsin, Madison, and featured a four-stroke engine with electronic fuel injection (EFI), a two-stage tuned muffler, and catalytic exhaust aftertreatment. A hybrid-electric design was used to increase the snowmobile's powertrain output and improve acceleration. [8] Teams should be competitive in all events to gain enough points to win the competition.