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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 considers optimal power management during the establishment of an expeditionary outpost using battery and vehicle assets for electrical generation. The first step in creating a new outpost is implementing the physical protection and barrier system. Afterwards, facilities that provide communications, fires, meals, and moral boosts are implemented that steadily increase the electrical load while dynamic events, such as patrols, can cause abrupt changes in the electrical load profile. Being able to create a fully functioning outpost within 72 hours is a typical objective where the electrical power generation starts with batteries, transitions to gasoline generators and is eventually replaced by diesel generators as the outpost matures. Vehicles with power export capability are an attractive supplement to this electrical power evolution since they are usually on site, would reduce the amount of material for outpost creation, and provide a modular approach to outpost build-up.

This paper investigates an optimal design of a diesel engine and after-treatment systems for a series hybrid electric forklift application. A holistic modeling approach is developed in GT-Suite® to establish a model-based hardware definition for a diesel engine and an after-treatment system to accurately predict engine performance and emissions. The used engine model is validated with the experimental data. The engine design parameters including compression ratio, boost level, air-fuel ratio (AFR), injection timing, and injection pressure are optimized at a single operating point for the series hybrid electric vehicle, together with the performance of the after-treatment components. The engine and after-treatment models are then coupled with a series hybrid electric powertrain to evaluate the performance of the forklift in the standard VDI 2198 drive cycle.

In this paper, a model-based linear estimator and a non-linear control law for an Fe-zeolite urea-selective catalytic reduction (SCR) catalyst for heavy duty diesel engine applications is presented. The novel aspect of this work is that the relevant species, NO, NO2 and NH3 are estimated and controlled independently. The ability to target NH3 slip is important not only to minimize urea consumption, but also to reduce this unregulated emission. Being able to discriminate between NO and NO2 is important for two reasons. First, recent Fe-zeolite catalyst studies suggest that NOx reduction is highly favored by the NO 2 based reactions. Second, NO2 is more toxic than NO to both the environment and human health. The estimator and control law are based on a 4-state model of the urea-SCR plant. A linearized version of the model is used for state estimation while the full nonlinear model is used for control design.

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.

A global optimization method has been developed for an engine simulation code and utilized in the search of optimal fuel injection strategies. This method uses a Lagrange interpolation function which interpolates engine output data generated at the vertices and the intermediate points of the input parameters. This interpolation function is then used to find a global minimum over the entire parameter set, which in turn becomes the starting point of a CFD-based optimization. The CFD optimization is based on a steepest descent method with an adaptive cost function, where the line searches are performed with a fast-converging backtracking algorithm. The adaptive cost function is based on the penalty method, where the penalty coefficient is increased after every line search. The parameter space is normalized and, thus, the optimization occurs over the unit cube in higher-dimensional space.

An experimental and modeling study was conducted to study the passive regeneration of a catalyzed particulate filter (CPF) by the oxidation of particulate matter (PM) via thermal and Nitrogen dioxide/temperature-assisted means. Emissions data in the exhaust of a John Deere 6.8 liter, turbocharged and after-cooled engine with a low-pressure loop EGR and a diesel oxidation catalyst (DOC) - catalyzed particulate filter (CPF) in the exhaust system was measured and used for this study. A series of experiments was conducted to evaluate the performance of the DOC, CPF and DOC+CPF configurations at various engine speeds and loads.

Steady-state particulate loading experiments were conducted on an advanced production catalyzed particulate filter (CPF), both with and without a diesel oxidation catalyst (DOC). A heavy-duty diesel engine was used for this study with the experiments conducted at 20, 40, 60 and 75 % of full load (1120 Nm) at rated speed (2100 rpm). The data obtained from these experiments were used and are necessary for calibrating the MTU 1-D 2-Layer CPF model. These experimental and modeling results were compared to previous research conducted at MTU that used the same engine but an earlier development version of the combination of DOC and CPF. The motivation for the comparison of the two systems was to determine whether the reformulated production catalysts performed as good or better than the early development catalysts. The results were compared to understand the filtration and oxidation differences between the two DOC+CPF and the CPF-only aftertreatment systems.

Model-based control strategies are important for meeting the dual objective of maximizing NOx reduction and minimizing NH3 slip in urea-SCR catalysts. To be implementable on the vehicle, the models should capture the essential behavior of the system, while not being computationally intensive. This paper discusses the adequacy of two different reduced order SCR catalyst models and compares their performance with a higher order model. The higher order model assumes that the catalyst has both diffusion and reaction kinetics, whereas the reduced order models contain only reaction kinetics. After describing each model, its parameter identification and model validation based on experiments on a Navistar I6 7.6L engine are presented. The adequacy of reduced order models is demonstrated by comparing the NO, NO2 and NH3 concentrations predicted by the models to their concentrations from the test data.

A one-dimensional model simulating the oxidation of CO, HC, and NO was developed to predict the gaseous emissions downstream of a diesel oxidation catalyst (DOC). The model is based on the conservation of mass, species, and energy inside the DOC and draws on past research literature. Steady-state experiments covering a wide range of operating conditions (exhaust temperatures, flow rates and gaseous emissions) were performed, and the data were used to calibrate and validate the model. NO conversion efficiencies of 50% or higher were obtained at temperatures between 300°C and 350°C. CO conversion efficiencies of 85% or higher and HC conversion efficiencies of 75% or higher were found at every steady state condition above 200°C. The model agrees well with the experimental results at temperatures from 200°C to 500°C, and volumetric flow rates from 8 to 42 actual m3/min.

Vehicle-to-Grid (V2G) service has a potential to improve the reliability and stability of the electrical grid due to the ability of providing bi-directional power flow from/to the grid. However, frequent charging/discharging may impact the battery lifetime. This paper presents the analysis of battery degradation in three scenarios. In the first scenario, different battery capacities are considered. In the second scenario, the battery degradation with various depth of discharge (DOD) are studied. In the third scenario, the capacity loss due to different charging regime are compared. The charging/discharging of plug-in electric vehicles (PEVs) are simulated in a single-phase microgrid system integrated with a photovoltaics (PV) farm, an energy storage system (ESS), and ten electric vehicle service equipment (EVSE). The battery degradation model is an energy throughput model, which is developed based on the Arrhenius equation and a power law relationship between time and capacity fading.

A high voltage battery is an essential part of hybrid electric vehicles (HEVs). It is imperative to precisely estimate the state of charge (SOC) and state of health (SOH) of battery in real time to maintain reliable vehicle operating conditions. This paper presents a method of estimating SOC and SOH through the incorporation of current integration, voltage translation, and Ah-throughput. SOC estimation utilizing current integration is inadequate due to the accumulation of errors over the period of usage. Thus voltage translation of SOC is applied to rectify current integration method which improves the accuracy of estimation. Voltage translation data is obtained by subjecting the battery to hybrid pulse power characterization (HPPC) test. The Battery State of Health was determined by semi-empirical model combined with accumulated Ah-throughput method. Battery state of charge was employed as an input to estimate damages accumulated to battery aging through a real-time model.

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.

Verification and validation (V&V) are essential stages in the design cycle of industrial controllers to remove the gap between the designed and implemented controller. In this study, a model-based adaptive methodology is proposed to enable easily verifiable controller design based on the formulation of a sliding mode controller (SMC). The proposed adaptive SMC improves the controller robustness against major implementation imprecisions including sampling and quantization. The application of the proposed technique is demonstrated on the engine cold start emission control problem in a mid-size passenger car. The cold start controller is first designed in a single-input single-output (SISO) structure with three separate sliding surfaces, and then is redesigned based on a multiinput multi-output (MIMO) SMC design technique using nonlinear balanced realization.

Real-time control of Reactivity Controlled Compression Ignition (RCCI) during engine load and speed transient operation is challenging, since RCCI combustion phasing depends on nonlinear thermo-kinetic reactions that are controlled by dual-fuel reactivity gradients. This paper discusses the design and implementation of a real-time closed-loop combustion controller to maintain optimum combustion phasing during RCCI transient operations. New algorithms for real-time in-cylinder pressure analysis and combustion phasing calculations are developed and embedded on a Field Programmable Gate Array (FPGA) to compute RCCI combustion and performance metrics on cycle-by-cycle basis. This cycle-by-cycle data is then used as a feedback to the combustion controller, which is implemented on a real-time processor. A computationally efficient algorithm is introduced for detecting Start of Combustion (SOC) for the High Temperature Heat Release (HTHR) or main-stage heat release.

In this study, a DOC catalyst was experimentally studied in an engine test cell with a2010 Cummins 6.7L ISB diesel and a production aftertreatment system. The test matrix consisted of steady state, active regeneration with in-cylinder fuel dosing and transient conditions. Conversion efficiencies of total hydrocarbon (THC), CO, and NO were quantified under each condition. A previously developed high-fidelity DOC model capable of predicting both steady state and transient active regeneration gaseous emissions was calibrated to the experimental data. The model consists of a single 1D channel where mass and energy balance equations were solved for both surface and bulk gas regions. The steady-state data were used to identify the activation energies and pre-exponential factors for CO, NO and HC oxidation, while the steady-state active regeneration data were used to identify the inhibition factors. The transient data were used to simulate the thermal response of the DOC.

The primary goal of this project was to design and implement an oxidation catalyst specific to a high-performance spark ignited two stroke engines to reduce vehicle-out emissions. The primary challenges of two stroke catalysis at high loads include controlling the catalytic reaction temperature as well as minimizing the increase in exhaust back pressure due to the addition of a catalyst. Reaction temperature is difficult to control due to high HC and CO concentrations paired with an excess of oxygen in the exhaust stream. By limiting catalyst conversion efficiency, the reaction temperatures were controlled. Two stroke engines are also inherently sensitive to changes in exhaust back pressure and therefore location and sizing of the catalyst are key design considerations. Because of these challenges significant effort was directed toward developing the two-stroke specific catalyst design process.

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.

The paper reviews the role of drawbeads in sheet metal stamping. The design of drawbeads is discussed in depth, with treatment of different bead cross sections, bead end shapes, and bead materials. International standards and practices are included. This is followed by the historical development of the modeling of the drawbead restraining force, starting with basic equilibrium approaches, and leading to the use of the finite element method which permits the study of drawbead effects on sheet metal flow in three dimensions. Finally, the potential of active drawbeads is described based upon ongoing research which is directed toward closed-loop computer control of the stamping process through adjustment of the drawbead penetration.

The objective of this research was to study the effects of a CCRT®, henceforth called Diesel Oxidation Catalyst - Catalyzed Particulate Filter (DOC-CPF) system on particulate and gaseous emissions from a heavy-duty diesel engine (HDDE) operated at Modes 11 and 9 of the old Environmental Protection Agency (EPA) 13-mode test cycle Emissions characterized included: total particulate matter (TPM) and components of carbonaceous solids (SOL), soluble organic fraction (SOF) and sulfates (SO4); vapor phase organics (XOC); gaseous emissions of total hydrocarbons (HC), carbon monoxide (CO), oxides of nitrogen (NOx), nitric oxide (NO) and nitrogen dioxide (NO2), oxygen (O2) and carbon dioxide (CO2); and particle size distributions at normal dilution ratio (NDR) and higher dilution ratio (HDR). Significant reductions were observed for TPM and SOL (>90%), SOF (>80%) and XOC (>70%) across the DOC-CPF at both modes.