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Energy management is one of the key challenges for the development of Hybrid Electric Vehicle (HEV) due to its complex powertrain structure. Hardware-In-the-Loop (HIL) simulation provides an open software architecture which enables rapid prototyping HEV energy management system. This paper presents the investigation of the energy management system for a single shaft parallel hybrid electric vehicle using dSPACE eDrive HIL system. The parallel hybrid electric vehicle, energy management system, and low-level Electronic Control Unit (ECU) were modeled using dSPACE Automotive Simulation Models and dSPACE blocksets. Vehicle energy management is achieved by a vehicle-level controller called hybrid ECU, which controls vehicle operation mode and torque distribution among Internal Combustion Engine (ICE) and electric motor. The individual powertrain components such as ICE, electric motor, and transmission are controlled by low-level ECUs.

Lookup tables and functions are widely used in real-time embedded automotive applications to conserve scarce processor resources. To minimize the resource utilization, these lookup tables (LUTs) commonly use custom data structures. The lookup function code is optimized to process these custom data structures. The legacy routines for these lookup functions are very efficient and have been in production for many years. These lookup functions and the corresponding data structures are typically used for calibration tables. The third-party calibration tools are specifically tailored to support these custom data structures. These tools assist the calibrators in optimizing the control algorithm performance for the targeted environment for production. Application software typically contains a mix of both automatically generated software and manually developed code. Some of the same calibration tables may be used in both auto generated and hand-code [ 1 ] [ 2 ].

Hydrotreated vegetable oil (HVO) is a high-cetane number alternative fuel with the potential of drastic emissions reductions in high-pressure diesel engines. In this study the behavior of HVO sprays is investigated computationally and compared with conventional diesel fuel sprays. The simulations are performed with a modified version of the C++ open source code OpenFOAM using Reynolds-averaged conservation equations for mass, species, momentum and energy. The turbulence has been modeled with a modified version of the RNG k-ε model. In particular, the turbulence interaction between the droplets and the gas has been accounted for by introducing appropriate source terms in the turbulence model equations. The spray simulations reflect the setup of the constant-volume combustion cell from which the experimental data were obtained.

Environmental awareness and fuel economy legislation has resulted in greater emphasis on developing more fuel efficient vehicles. As such, achieving fuel economy improvements has become a top priority in the automotive field. Companies are constantly investigating and developing new advanced technologies, such as hybrid electric vehicles, plug-in hybrid electric vehicles, improved turbo-charged gasoline direct injection engines, new efficient powershift transmissions, and lighter weight vehicles. In addition, significant research and development is being performed on energy management control systems that can improve fuel economy of vehicles. Another area of research for improving fuel economy and environmental awareness is based on improving the customer's driving behavior and style without significantly impacting the driver's expectations and requirements.

Diesel particulate trap regeneration is a complex process involving the interaction of phenomena at several scales. A hierarchy of models for the relevant physicochemical processes at the different scales of the problem (porous wall, filter channel, entire trap) is employed to obtain a rigorous description of the process in a multidimensional context. The final model structure is validated against experiments, resulting in a powerful tool for the computer-aided study of the regeneration behavior. In the present work we employ this tool to address the effect of various spatial non-uniformities on the regeneration characteristics of diesel particulate traps. Non-uniformities may include radial variations of flow, temperature and particulate concentration at the filter inlet, as well as variations of particulate loading. In addition, we study the influence of the distribution of catalytic activity along the filter wall.

In this paper, the conversion of a production SUV to a hybrid electric vehicle with a drive system utilizing a planetary power-split transmission is presented. The uniqueness of this design comes from its ability to couple the advantages of a parallel hybrid with the advantages of a series hybrid. Depending on operating conditions and recent operating history, the drive system transitions to one of several driving modes. The drive system consists of a planetary gear set coupled to an alternator, motor, and internal combustion engine. It performs the power-split operation without the need for belt drives or clutching devices. The effects on driveability, manufacturing, fuel economy, emissions, and performance are presented along with the design, selection, and implementation of all of the vehicle conversion components.

Three-dimensional diesel engine combustion simulations with single-step chemistry have been compared with two-step and three-step chemistry by means of the Laminar and Turbulent Characteristic Time Combustion model using the Star-CD program. The second reaction describes the oxidation of CO and the third reaction describes the combustion of H2. The comparisons have been performed for two heavy-duty diesel engines. The two-step chemistry was investigated for a purely kinetically controlled, for a mixing limited and for a combination of kinetically and mixing limited oxidation. For the latter case, two different descriptions of the laminar reaction rates were also tested. The best agreement with the experimental cylinder pressure has been achieved with the three-step mechanism but the differences with respect to the two-step and single-step reactions were small.

The successful implementation of a clean, quiet, four-stroke engine into an existing snowmobile chassis has been achieved. The snowmobile is easy to start, easy to drive and environmentally friendly. The following paper describes the conversion process in detail with actual engine test data. The hydrocarbon emissions of the new, four-stroke snowmobile are 98% lower than current, production, two-stroke models. The noise production of the four-stroke snowmobile was 68 dBA during an independent wide open throttle acceleration test. If the four-stroke snowmobile were to replace all current, two-stroke snowmobiles in Yellowstone National Park (YNP), the vehicles would only produce 16% of the combined automobile and snowmobile hydrocarbon emissions compared to the current 93% produced by two-stroke snowmobiles.

A modified version of the Laminar and Turbulent Characteristic Time combustion model and the Hiroyasu-Magnussen soot model have been implemented in the flow solver Star-CD. Combustion simulations of three DI diesel engines, utilizing the standard k-ε turbulence model and a modified version of the RNG k-ε turbulence model, have been performed and evaluated with respect to combustion performance and emissions. Adjustments of the turbulent characteristic combustion time coefficient, which were necessary to match the experimental cylinder peak pressures of the different engines, have been justified in terms of non-equilibrium turbulence considerations. The results confirm the existence of a correlation between the integral length scale and the turbulent time scale. This correlation can be used to predict the combustion time scale in different engines.

Exergy or availability is the potential of a system to do work. In this paper, an innovative exergy-based control approach is presented for Internal Combustion Engines (ICEs). An exergy model is developed for a Homogeneous Charge Compression Ignition (HCCI) engine. The exergy model is based on quantification of the Second Law of Thermodynamic (SLT) and irreversibilities which are not identified in commonly used First Law of Thermodynamics (FLT) analysis. An experimental data set for 175 different ICE operating conditions is used to construct the SLT efficiency maps. Depending on the application, two different SLT efficiency maps are generated including the applications in which work is the desired output, and the applications where Combined Power and Exhaust Exergy (CPEX) is the desired output. The sources of irreversibility and exergy loss are identified for a single cylinder Ricardo HCCI engine.

Battery management system (BMS) plays a key role in the power management of hybrid electric vehicles (HEV). It measures the state of charge (SOC), state of health (SOH) of the battery, protects the battery package and extends cells' life cycles. For HEV applications, lithium-ion battery is usually selected as electric power source due to its high specific energy, high energy density, and long life cycle. However, the non-linear characteristic of a Li-ion battery, complicated electro-chemical model, and environmental factors, raises the difficulties in the real-time estimation of the SOC for a Li-ion battery. To address this challenge, a BMS for HEVs is modeled with MATLAB/Simulink. In addition, a regenerative braking control strategy is proposed to determine the magnitude of the regenerative torque based on the battery SOC.

A mathematical model is presented for analysis and optimization of the adsorbents in the multifiltration beds contained in the International Space Station (ISS) water processor. The model consists of a physical properties database, an equilibrium description for single and multicomponent adsorption, and a kinetic description for adsorption beds in the water processor. The model is verified on a surrogate mixture designed to mimic the adsorption potential of the ISS shower/handwash waste stream.

The destruction of organic contaminants in waste water for closed systems, such as that of the International Space Station, is crucial due to the need for recycling the waste water. A cocurrent upflow bubble column using oxygen as the gas phase oxidant and packed with catalyst particles consisting of a noble metal on an alumina substrate is being developed for this process. This paper addresses the development of a plug-flow model that will predict the performance of this three phase reactor system in destroying a multicomponent mixture of organic contaminants in water. Mass balances on a series of contaminants and oxygen in both the liquid and gas phases are used to develop this model. These mass balances incorporate the gas-to-liquid and liquid-to-particle mass transfer coefficients, the catalyst effectiveness factor, and intrinsic reaction rate.

A mathematical model is presented for analysis and optimization of the ion exchange beds in the International Space Station (ISS) Water Processor. The model consists of a physical properties database, an equilibrium description for binary and multicomponent ion exchange, and a kinetic description for ion exchange beds in the Water Processor. The ion exchange model will be verified for an Ersatz water designed to mimic the ISS shower/handwash waste stream.

A computational design of experiments was constructed to analyze two basic nozzle designs. Several geometric features of the nozzles such as cavity height, exit orifice area, turbulence generator area and exit orifice position in addition to the pressure differential across the injector were used in a 2k factorial design study. Performance characteristic which were analyzed in an analysis of variance study included the discharge coefficient. atomization efficiency and predicted spray pattern. The computational design of experiments revealed which of the studied parameters had the greatest influence on a given nozzle performance characteristic. These results were compared to a similar investigation which was later performed experimentally from which similar conclusions were drawn.

The effect of truck dieselization for three levels of diesel penetration into each of the eight classes of trucks is modeled. Diesel and total truck sales, population, mileage and yearly fuel usage data are aggregated by four truck classes representing light, medium, light-heavy and heavy-heavy classes. Four fuel economy scenario's for different technological improvements were studied. Improvement of fuel economy for light and heavy-heavy duty vehicle classes provides significant total fuel savings. Truck dieselization of light and light-heavy duty vehicle classes provides the largest improvement of fuel usage due to the fact that they have large numbers of vehicles and presently have few diesels. Total car and truck fuel usage in the 1980's shows roughly a constant demand with cars decreasing due to improved new fleet fuel economy and trucks increasing due to a larger population with better fuel economy due to dieselization and improved technology.

This paper studies model predictive control algorithm for Hybrid Electric Vehicle (HEV) energy management to improve HEV fuel economy. In this paper, Model Predictive Control (MPC), a predictive control method, is applied to improve the fuel economy of power-split HEV. A dedicated model predictive control method is developed to predict vehicle speed, battery state of charge (SOC), and engine fuel consumption. The power output from the engine, motor, and the mechanical brake will be adjusted to match driver's power request at the end of the prediction window while minimizing fuel consumption. The controller model is built on Matlab® MPC toolbox® and the simulations are based on MY04 Prius vehicle model using Autonomie®, a powertrain and fuel economy analysis software, developed by Argonne National Laboratory. The study compares the performance of MPC and conventional rule-base control methods.

The following paper discusses alternative strategies for reducing noise and emission production from a two-stroke snowmobile. Electric, two-stroke and four-stroke solutions were analyzed and considered for entry in the Clean Snowmobile Challenge (CSC) 2000. A two-stroke solution was utilized primarily due to time constraints. Complete snowmobile competition results are provided. The electric solution, while the most effective at reducing emissions, is negatively impacted by weight and cost. A modified two-stroke solution, limited by cost and complexity, does not provide the required improvements in emissions. A four-stroke solution reduces noise and emissions and provides an acceptable trade-off between noise, emissions, performance and cost.

This paper details the computer algorithm which was developed to determine the A/C refrigeration circuit balance point under the system transient operating conditions. The A/C circuit model consisting of major component submodels, such as the evaporator, compressor, condenser, orifice, air handling system, and connecting hoses, are included in the study. Pressure drop and thermal capacity for the evaporator, condenser, and connecting ducts/hoses are also considered in the simulation. The results obtained from the simulation model are in good agreement with the experimental data. Users can take advantage of this CAE tool to optimize the A/C system design and to minimize the development process with time-saving and cost-effective perspectives.

A Computer-Aided Engineering (CAE) model for automobile climate control system is presented to provide engineers with an cost effective analysis tool for designing, developing, and optimizing the vehicle interior climate. It is the objective of this paper to develop a mathematical model which predicts the lumped temperature and lumped humidity variations inside the passenger compartment under design and operating conditions. The transient nature of the passenger cabin temperature, average interior mass temperature, and humidity are modeled using three coupled non-linear ordinary differential equations based on mass and energy balances. These equations are then solved by a fourth-order Runge-Kutta method with adaptive step size control.