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Real-Time Embedded Models for Simulation and Control of Clean and Fuel-Efficient Heavy-Duty Diesel Engines

University of Michigan-Saravanan Duraiarasan, Rasoul Salehi, Fucong Wang, Anna Stefanopoulou
Daimler Trucks North America-Marc Allain, Siddharth Mahesh
  • Technical Paper
  • 2020-01-0257
To be published on 2020-04-14 by SAE International in United States
The ever increasing demand for fuel economy and stringent emission norms drives researchers to continuously innovate and improve engine modes to implement adaptive algorithms, where the engine states are continuously monitored and the control variables are manipulated to operate the engine at the most efficient regime. This paper presents a virtual engine developed by modeling a modern diesel engine and aftertreatment which can be used in real-time on a control unit to predict critical diesel engine variables such as fuel consumption and feed gas conditions including emissions, flow and temperature. A physics-based approach is followed in order to capture vital transient airpath and emission dynamics encountered during real driving condition. A minimal realization of the airpath model is coupled with a cycle averaged NOx emissions predictor to estimate transient feed gas NOx during steady state and transient conditions. The complete airpath and NOx emission model was implemented on a rapid prototyping controller and experimentally validated over steady state and transient emission cycles. The overall performance of the reduced order model was comparable to that of…
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Evaluating the Performance of a Conventional and Hybrid Bus operating on Diesel and B20 Fuel for Emissions and Fuel Economy

University of Michigan-Rinav Pillai, Andre Boehman
U.S. Environmental Protection Agency-Scott Ludlam
  • Technical Paper
  • 2020-01-1351
To be published on 2020-04-14 by SAE International in United States
With ongoing concerns about the elevated levels of ambient air pollution in urban areas and the contribution from heavy-duty diesel vehicles, hybrid electric buses are considered as a potential solution as they are perceived to be less polluting and more fuel-efficient than their conventional engine counterparts. However, recent studies have shown that real-world emissions may be substantially higher than those measured in the laboratory, mainly due to operating conditions that are not fully accounted for in dynamometer test cycles. At the U.S. EPA National Fuel and Vehicle Emissions Laboratory (NVFEL), the in-use criteria emissions and energy efficiency of heavy-duty class 8 vehicles (up to 80,000 lbs) may be evaluated under controlled conditions in the heavy-duty chassis dynamometer test. The present study evaluated the performance of a conventional bus and hybrid bus for emissions and fuel economy under representative test cycles (including cold start and hot start conditions) with Diesel (#2) and Biodiesel (B20) fuel. The conventional bus was equipped with a Cummins ISL 8.3L engine and a Diesel Particulate Filter (DPF) and Diesel Oxidation Catalyst…
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Accelerometer-Based Estimation of Combustion Features for Engine Feedback Control

University of Michigan-Bryan Maldonado, Anna Stefanopoulou
Army Research Laboratory-Kenneth Kim
  • Technical Paper
  • 2020-01-1147
To be published on 2020-04-14 by SAE International in United States
An experimental investigation of non-intrusive combustion sensing was performed using a tri-axial accelerometer mounted to a small-bore high-speed 4-cylinder diesel engine. This study investigates potential techniques to extract combustion features from accelerometer signals to be used for cycle-to-cycle engine control. Selection of accelerometer location and vibration axis were performed by analyzing vibration signals for three different locations along the block for all three of the accelerometer axes. A magnitude squared coherence (MSC) statistical analysis was used to select the best location and axis. Based on previous work from the literature, the vibration signal filtering was optimized and the filtered vibration signals were analyzed. It was found that the vibration signals correlate well with the second derivative of pressure during the initial stages of combustion. Two combustion parameters were the focus of this investigation, start of combustion (SOC) and crank angle of fifty-percent heat release (CA50). The results showed that, for a wide range of engine conditions, SOC can be obtained solely from the first derivative of the vibration signal with respect to crank angle. In…
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Energy, Fuels, and Cost Analyses for the M1A2 Tank: A Weight Reduction Case Study

University of Michigan-Geoffrey M. Lewis, Gregory A. Keoleian
CCDC Ground Vehicle Systems Center-Rob Hart
  • Technical Paper
  • 2020-01-0173
To be published on 2020-04-14 by SAE International in United States
Reducing the weight of the Abrams M1A2 tank has been studied by lightweighting three separate components: hull, suspension, and track, resulting in 5.1, 1.3, and 0.6 percent tank mass reductions, respectively. The impact of replacing an existing with a lightweight component on tank performance are evaluated in terms of three metrics: primary energy demand (PED), cost, and tank operational fuel consumption (FC). The life cycle phases included are: preproduction, material production, part fabrication, and tank operation. The metrics for each of the tank lightweight components are expressed as ratios: for example, the sum of PED for the four life cycle phases of the lightweight tank / the PED for the operational phase only of the base case (unmodified) tank. For Army defined duty cycles, a FC/mass elasticity of 0.55 was employed for estimating changes in tank FC upon mass reductions. On a per tank basis, we find that the relative costs to retrofit and operate a tank with a lightweight hull ranges from 19 to 3.5 times those for simply operating (fuel costs) an existing…
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Machine Learning Techniques for the Prediction of Combustion Events in Cooperative Fuel research Engine (CFR) at Homogeneous Charge Compression Ignition (HCCI) conditions.

University of Michigan-Kwang Hee Yoo, Andre Boehman
Aramco-Alexander Voice
  • Technical Paper
  • 2020-01-1132
To be published on 2020-04-14 by SAE International in United States
This research assesses the capability of data-science models to predict the combustion events occurring for certain input conditions in Cooperative Fuel Research Engine (CFR) at Homogeneous Charge Compression Ignition (HCCI) conditions. The experimental data from CFR engine of University of Michigan (UM), operated at different input conditions for various gasoline type fuels was utilized for the study. The current study developed a capable machine learning framework to predict the auto-ignition propensity of a fuel under HCCI conditions. The combustion events happening at HCCI conditions in CFR engine are primarily classified into four different classes depending on the combustion phasing and pressure rise during the combustion in engine. The classes are: no ignition, normal combustion, high MPRR and early CA 50. Two machine learning (ML) models, K-nearest neighbors and Support Vector Machines, are compared for their classification capabilities of combustion events. Seven conditions are used as the input features for this ML models viz. Research Octane Number (RON) of fuel, Sensitivity of fuel (S), fuel rate (J/L/cycle), oxygen mole fraction, intake temperature and pressure, and compression…
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Numerical Investigation of Friction Material Contact Mechanics in Automotive Clutches

University of Michigan-Nikolaos Katopodes
FCC. CO., LTD.-Masatoshi Miyagawa, Takahiro Tsuchiya, Shinji Nakamura, Matthew Wendel
  • Technical Paper
  • 2020-01-1417
To be published on 2020-04-14 by SAE International in United States
A wet clutch model is required in automotive propulsion system simulations for enabling robust design and control development. It commonly assumes a Coulomb’s model for simplicity, even though it does not physically represent viscous torque transfer. A Coulomb friction coefficient is treated as a tuning parameter in simulations to match vehicle data for targeted conditions. The simulations tend to deviate from actual behaviors for different drive conditions unless the friction coefficient is adjusted repeatedly. Alternatively, a complex hydrodynamic model, coupled with a surface contact model, is utilized to enhance the fidelity of system simulations for broader conditions. The theory of elastic asperity deformation is conventionally employed to model clutch surface contact. However, the recent examination of friction material shows that elasticity modulus of surface fibers significantly exceeds contact load, implying no deformation of fibers. This article investigates the friction material contact mechanics through numerical simulations. A surface model is constructed based on microscopic examination of material topography and properties. FEM simulation is conducted to examine the interactions between surface fibers and surrounding medium under loaded…
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Engine and Aftertreatment Co-Optimization of Connected HEVs via Multi-Range Vehicle Speed Planning and Prediction

University of Michigan-Qiuhao Hu, Mohammad Reza Amini, Yiheng Feng, Zhen Yang, Hao Wang, Ilya Kolmanovsky, Jing Sun
Ford Motor Company-Ashley Wiese, Zeng Qiu, Julia Buckland
  • Technical Paper
  • 2020-01-0590
To be published on 2020-04-14 by SAE International in United States
Connected vehicles (CVs) have situational awareness that can be exploited for control and optimization of the powertrain system. While extensive studies have been carried out for energy efficiency improvement of CVs via eco-driving and platooning, the implication of such technologies on the thermal responses of CVs (including those of the engine and aftertreatment systems) has not been fully investigated. One of the key challenges in leveraging connectivity for optimization-based thermal management of CVs is the relatively slow thermal dynamics, which necessitate the use of a long prediction horizon to achieve the best performance. Long-term prediction of the CV speed, unlike the short-range prediction based on vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications-based information, is difficult and error-prone. The multiple timescales inherent to power and thermal systems call for a variable timescale optimization framework with access to short- and long-term vehicle speed preview. To this end, a model predictive controller (MPC) with a multi-range speed preview for integrated power and thermal management (iPTM) of connected hybrid electric vehicles (HEVs) is presented in this paper. The MPC is…
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Portable In-cylinder Pressure Measurement and Signal Processing System for Real-time Combustion Analysis and Engine Control

University of Michigan-Bryan Maldonado, Charles Solbrig, Anna Stefanopoulou
Southwest Research Institute-Yilun Luo, Siying Liu, Devon Adair
  • Technical Paper
  • 2020-01-1144
To be published on 2020-04-14 by SAE International in United States
To meet ever strict emissions regulations, cycle-to-cycle combustion control based on statistical processing and model-based prediction has attracted considerable attention from academia and industry. Feedback combustion control typically adjusts ignition-related parameters (spark advance, injection timing, cam timing, etc.) in a cycle-by-cycle manner based on the combustion characteristics measured from previous events. Cycle-to-cycle control guarantees a tight control at steady state and fast response during transients, enforcing an optimal combustion process over a wide variety of engine speed/load conditions. However, these control strategies are constrained by the combustion cycle duration, usually in the order of tens of milliseconds. Therefore, high-speed data acquisition and real-time processing is required. This paper describes a portable in-cylinder pressure measurement and processing system (P-BOT) that enables such a feedback control application to be used on an engine control unit (ECU). This system measures high-speed cylinder pressure signals and engine position, performs real-time heat release analysis, and sends the combustion results to the ECU for engine control at the end of each combustion event. This system is implemented on a Xilinx Zynq…
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Security Analysis of Android Automotive

University of Michigan-Mert Pese, Kang Shin
Georgia Institute of Technology-Josiah Bruner
  • Technical Paper
  • 2020-01-1295
To be published on 2020-04-14 by SAE International in United States
In-vehicle infotainment (IVI) platforms are getting increasingly connected. Besides OEM apps and services, the next generation of IVI platforms are expected to offer third-party application integration. Under this business model, vehicular sensor and event data can be collected and shared with selected third-party apps. To this end, Google is pushing towards standardization among proprietary IVI operating systems with their Android Automotive platform which is running natively on the vehicle’s IVI platform. Unlike Android Auto’s limited functionality of display-mirroring certain smartphone apps to the IVI screen, Android Automotive will have access to the in-vehicle network (IVN) and be able to read and share various sensor data from the car with third-party apps. This increased connectivity opens new business opportunities for both the car manufacturer as well as third-party entities, but also introduces a new attack surface on the vehicle. Therefore, Android Automotive must have a secure system architecture to prevent any potential attacks that might compromise the security and privacy of the vehicle and the driver. In particular, malicious third-party entities could possibly remotely compromise a…
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Variability of driving conditions and its effect on charging time for urban battery electric buses

University of Michigan-Anshul Paunikar, Rasoul Salehi
  • Technical Paper
  • 2020-01-0598
To be published on 2020-04-14 by SAE International in United States
Due to growing environmental concerns and stringent vehicle emissions regulations, there is a constant urge in the automotive industry to move towards electrified propulsion systems. Public transportation plays a major role in contributing towards lowering the emission level. Battery electric buses are regarded as a type of promising green mass transportation as they provide the advantage of less greenhouse gas emissions per passenger. However, the electric bus poses a threat of limited range and is not able to drive throughout the day without being charged again. This research focuses on the current bus transit systems in the city of Ann Arbor and investigates the impact of different electrification levels on the final CO2 reduction. Utilizing models of a conventional diesel bus, hybrid electric bus, and battery electric bus, the CO2 emission for each type of transportation bus is estimated. Measured vehicle speed data from various routes under different driving conditions are used to investigate the variability of performance metrics. Finally, recommendations are made for charge requirements of battery electric bus considering the variation in drive…