ARC Researchers at the ASME
2016 Dynamic Systems and Control Conference
(October 12-14, 2016 at Minneapolis, Minnesota)

ARC researchers (principal investigators in bold) will be presenting their latest research developments. Below are their papers which may include non-ARC funded research (paper titles in bold are ARC funded).

31-4 Invited Session: Modeling and Control of Combustion Engines
Wednesday, October 12 01:30 PM - 03:30 PM
DSCC2016-9690 Minimum Backpressure Wastegate Control for a Boosted Gasoline Engine With Low Pressure External EGR
Shima Nazari, Anna Stefanopoulou, Jason Martz, University of Michigan (not ARC funded)
Abstract: Turbocharging and downsizing (TRBDS) a gasoline direct injection (GDI) engine can reduce fuel consumption but with increased drivability challenges compared to larger displacement engines. This tradeoff between efficiency and drivability is influenced by the throttle-wastegate control strategy. A more severe tradeoff between efficiency and drivability is shown with the introduction of Low-Pressure Exhaust Gas Recirculation (LP-EGR). This paper investigates and quantifies these tradeoffs by designing and implementing in a one-dimensional (1D) engine simulation two prototypical throttle-wastegate strategies that bound the achievable engine performance with respect to efficiency and torque response. Specifically, a closed-wastegate (WGC) strategy for the fastest achievable response and a throttle-wastegate strategy that minimizes engine backpressure (MBWG) for the best fuel efficiency, are evaluated and compared based on closed loop response. The simulation of an aggressive tip-in (the driver's request for torque increase) shows that the wastegate strategy can negotiate a 0.8% efficiency gain at the expense of 160 ms slower torque response both with and without LP-EGR. The LP-EGR strategy, however offers a substantial 5% efficiency improvement followed by an undesirable 1 second increase in torque time response, clarifying the opportunities and challenges associated with LP-EGR.
DSCC2016-9691 A Coordinated Boost Control in a Twincharged Spark Ignition Engine With High External Dilution
Shima Nazari, Anna Stefanopoulou, Rani Kiwan, University of Michigan; Vasilios Tsourapas, EATON, Global Research and Technology (not ARC funded)
Abstract: This paper proposes a novel master-slave control strategy for coordination of throttle, wastegate and supercharger actuators in an electrically twincharged engine in order to guarantee efficient boost control during transients, while at steady state a throttle-wastegate coordination provides minimum engine backpressure hence engine efficiency elevation. The benefits and challenges associated with Low Pressure Exhaust Gas Recirculation (LP-EGR) in a baseline turbocharged engine, including improved engine efficiency, mainly due to better combustion phasing, and sluggish engine response to a torque demand due to slowed down air path dynamics were studied and quantified in [1]. Hence in this paper an electrical Eaton TVS roots type supercharger at high pressure side of the turbocharger compressor (TC compressor) is added to the baseline turbocharged engine and the performance of the proposed controller in the presence of LP-EGR, which is a more demanding condition, is evaluated and compared to the turbocharged engine. One dimensional (1D) crankangle resolved engine simulations show that the proposed master-slave control strategy can effectively improve the transient response of the twincharged engine, making it comparable to naturally aspirated engines, while the consumed electrical energy during transients can be recovered from the decreased fuel consumption due to LP-EGR conditions at steady state in approximately 1 second. Finally, a simple controller is developed to bypass the TC compressor and maximize the engine feeding charge during the transients in order to avoid TC compressor choking and achieve faster response.
31-5 Invited Session: Modeling and Control of Automotive Systems
Wednesday, October 12 04:00 PM - 06:00 PM

Design Considerations for Waste Energy Recovery With Electric Turbocompounding
Rasoul Salehi, Rani Kiwan, Jason Martz, Anna Stefanopoulou, University Of Michigan (not ARC funded)

Abstract: This paper studies the use of an electric turbogenerator (ETG) for waste energy recovery from the exhaust gas of a 13 L Heavy Duty Diesel (HDD) engine. Up to 1% brake specific fuel consumption (BSFC) reduction is predicted for this system at high engine loads using a validated mean value engine model. However, the addition of the ETG reduces the air-fuel equivalence ratio and increases exhaust gas recirculation (EGR) rate by 10%, deteriorating the engine-out smoke emissions. This challenge is addressed by decreasing the EGR valve position and the asymmetry in the twin scroll turbine. With these modifications, the predicted high load BSFC reduction is 2% and the EGR and ? approach their original values. The HDD engine is then tested experimentally with the ETG emulated by a valve downstream of the main turbocharger. The experimental results confirm the simulation predictions with the stock engine calibration and geometry, where EGR valve sweeps show the potential of this actuator for remedying the detrimental ETG backpressure effects, which ultimately improves the combined engine and ETG BSFC by 0.6% at high loads. Combining the simulated turbo sizing and the experimental EGR valve results indicates that up to 1.6% BSFC reductions are possible for the HDD engine with an integrated ETG, without deteriorating emission levels. Finally, simulations show that during a torque step the ETG should be bypassed to avoid deterioration in the dynamic response of the engine.

Assessing Fuel Economy From Automated Driving: Influence of Preview and Velocity Constraints
Niket Prakash, Gionata Cimini, Anna Stefanopoulou, University Of Michigan; Matthew J. Brusstar, US Environmental Protection Agency (not ARC funded)

Abstract: Constrained optimization control techniques with preview are designed in this paper to derive optimal velocity trajectories in longitudinal vehicle following mode, while ensuring that the gap from the lead vehicle is both safe and short enough to prevent cut-ins from other lanes. The lead vehicle associated with the Federal Test Procedures (FTP) is used as an example of the achieved benefits with such controlled velocity trajectories of the following vehicle. Fuel Consumption (FC) is indirectly minimized by minimizing the accelerations and decelerations as the autonomous vehicle follows the hypothetical lead. Implementing the cost function in offline Dynamic Programming (DP) with full drive cycle preview showed up to a 17% increase in Fuel Economy (FE). Real time implementation with Model Predictive Control (MPC) showed improvements in FE, proportional to the prediction horizon. Specifically, 20s preview MPC was able to match the DP results. A minimum of 1:5s preview of the lead vehicle velocity with velocity tracking of the lead was required to obtain an increase in FE. The optimal velocity trajectory found from these algorithms exceeded the presently allowable error from standard drive cycles for FC testing. However, the trajectory was still safe and acceptable from the perspective of traffic flow. Based on our results, regulators need to consider relaxing the constant velocity error margins around the standard velocity trajectories dictated by the FTP to encourage FE increase in autonomous driving.
Session: 5-1 Uncertain Systems and Robustness
Wednesday, October 12 04:00 PM - 06:00 PM
DSCC2016-9758 Optimization Based Weighting Matrices Design for Norm Optimal Iterative Learning Control
Xinyi Ge, Jeffrey Stein, Tulga Ersal, University of Michigan
Abstract: This paper focuses on Norm-Optimal Iterative Learning Control (NO-ILC) framework for Single-Input-Single-Output (SISO) Linear Time Invariant (LTI) systems and considers the weighting matrices design problem. The ideal design of weighting matrices should ensure Robust Monotonic Convergence (RMC) against modeling uncertainties while maximizing the convergence speed and minimizing the steady state error. The state-of-art RMC design methodologies either lead to conservative performance or require manual tunings. This paper provides a methodology to systematically achieve an optimal balance between robustness, convergence speed and steady state error. To this end, optimization problems are formulated at each frequency to maximize the convergence speed and minimize the steady state error. Two optimization formulations are proposed: one for an optimal nominal performance and one for an optimal performance against uncertainties. Both formulations offer a systematic approach for designing the weighting matrices for NO-ILC, thereby eliminating the manual tuning process and avoiding an unnecessarily conservative design. A simulation example is given to confirm the analysis and demonstrate the utility of the developed methodologies to design the weighting matrices.
Session: 27-2 Modeling and Validation 2
Thursday, October 13 01:30 PM - 03:30 PM
DSCC2016-9908 Production as a Service: Optimizing Utilization in Manufacturing Systems
Matthew Porter, Vikram Raghavan, Yikai Lin, Z. Morley Mao, Kira Barton, Dawn Tilbury, University of Michigan
(not ARC funded)
Abstract: While advances in technology have greatly improved the process of mass production, producing small batches or one-offs in an efficient manner has remained challenging for the manufacturing industry. As a result, designing and prototyping new parts can require a significant investment, and often require extra time to produce. Some progress has been made towards implementing more adaptable manufacturing systems but, in both large and small companies, there are still available manufacturing resources that sit idle between larger projects. The utilization of these resources can be difficult due to current organizational structures used in manufacturing, but if applied correctly these resources could prove beneficial. The concept of service oriented architecture has been commonly used in programming for quite some time now, but is still relatively new when it comes to applications in manufacturing. By characterizing manufacturing resources based on the services they can offer, the process of organizing production schemes can be generalized. This can allow for greater flexibility and robustness on a company level, and allows for industry wide organization of available resources. Some work has been done in this area but most of it has lacked implementation and has been limited to a single manufacturing facility or company. By expanding to more than one company, further considerations for privacy must be taken, but there are also many potential benefits for both the companies participating and their customers. In this paper we present a Production as a Service framework for providing manufacturing options to designers of new products based on available manufacturing resources. These resources may be all at a single company or spread across multiple companies. The designed framework aims to bridge the gap between the theoretical work that has been done on service oriented architecture in manufacturing, and what is required for implementation. This is done by providing a structure for a web-based framework that is able to maintain a database of available resources described as service capabilities and using this information to match the services to customer requests. Functionality for specific components of this framework are described in detail. This includes interfaces for companies to upload their available resources, and those for customers to generate requests for products to be manufactured, as well as the optimization framework for determining the optimal manufacturing options. Privacy needs for all parties are identified and steps are taken to mitigate these risks. An industrial use case provides a real world example of the framework.
7-2 Path Planning and Motion Control 2
Thursday, October 13 01:30 PM - 03:30 PM
DSCC2016-9816 Run Time Verification of Trust-based Symbolic Robot Motion Planning with Human-in-the-Loop
Maziar Mahani, Yue Wang, Clemson University
(not ARC funded)
Abstract: In this paper, we address the runtime verification problem of robot motion planning with human-in-the-loop. By bringing together approaches from runtime verification, trust model, and symbolic motion planning, we developed a framework which guarantees that a robot is able to safely satisfy task specifica- tions while improving task efficiency by switches between human supervision and autonomous motion planning. A simple robot model in a domain path planning scenario is considered and the robot is assumed to have perfect localization capabilities. The task domain is partitioned into a finite number of identical cells. A trust model based on the robot and human performance is used to provide a switching logic between different modes. Model checking techniques are utilized to generate plans in autonomous motion planning and for this purpose, Linear Temporal Logic (LTL) as a task specification language is employed to formally express specifications in model checking. The whole system is implemented in a runtime verification framework to monitor and verifies the system execution at runtime using ROSRV. Finally, we illustrated the effectiveness of this framework as well as its feasibility through a simulated case study.
Session: 31-11 Invited Session: Battery and Oil & Gas Systems
Friday, October 14 10:00 AM - 12:00 PM
DSCC2016-9730 Battery State of Health Monitoring by Estimation of the Number of Cyclable Li-Ions
Xin Zhou, Jeffrey Stein, Tulga Ersal, University of Michigan (not ARC funded)
Abstract: This paper introduces a method to monitor battery state of health (SOH) by estimating the number of cyclable Li-ions, a health-relevant electrochemical variable. SOH monitoring is critical to battery management in particular for balancing the trade-off between maximizing system performance and minimizing battery degradation. However, SOH-related electrochemical variables cannot be directly measured non-invasively. Hence, estimation algorithms are needed to track those variables non-destructively while the battery is in use. In this paper, the extended Kalman filter (EKF) is used to estimate the number of cyclable Li-ions as an unknown battery parameter. Simulations are performed using an example parameter set for a hybrid-electric-vehicle battery whose cathode material is LiMn2O4 mixed with other Li-compounds to obtain estimation results under a typical electric vehicle current profile that consists of a 1 C constant current charge mode and a discharge current profile for an electric vehicle subject to the Urban Dynamometer Driving Schedule cycle. The simulations show promising results in estimation of the number of cyclable Li-ions using the EKF under the ideal conditions. Next, robustness of the algorithm under non-ideal conditions (i.e., with SOC estimation error, modeling error, and measurement noise) is analyzed, and it is shown that estimation of the number of cyclable Li-ions using the EKF preserves high accuracy even under these non-ideal conditions. The proposed estimation technique for the number of cyclable Li-ions can also be applied to other parameter sets and batteries with other cathode materials to monitor the SOH change resulting from any degradation mechanism that consumes cyclable Li-ions.