ARC Researchers at DSCC 2014

2014 ASME Dynamic Systems and Control Conference
October 22-24, 2014, San Antonio, TX, USA

Below listed are papers from ARC investigators for DSCC 2014. ARC-funded work have titles highlighted in bold.

Wednesday, October 22, 2014

Session: 1-2-1 WA6 Energy Storage: Transportation Applications
10:00 AM-12:00 PM
DSCC2014-6321 (Invited Session Paper)
Parameterization and Validation of a Distributed Coupled Electro-Thermal Model for Prismatic Cells
Nassim Abdul Samad, Anna Stefanopoulou, Jason Siegel (University of Michigan)
Abstract: The temperature distribution in a prismatic Li-ion battery cell can be described using a spatially distributed equivalent circuit electrical model coupled to a 3D thermal model. The model represents a middle ground between simple one or two state models (generally used for cylindrical cells) and complex finite element models. A lumped parameter approach for the thermal properties of the lithium-ion jelly-roll is used. The battery is divided into nxm nodes in 2-dimensions, and each node is represented by an equivalent circuit and 3 temperatures in the through plane direction to capture the electrical and thermal dynamics respectively. The thermal model is coupled to the electrical through heat generation. The parameters of the equivalent circuit electrical model are temperature and state of charge dependent. Parameterization of the distributed resistances in the equivalent circuit model is demonstrated using lumped parameter measurements, and are a function of local temperature. The model is parameterized and validated with data collected from a 3-cell fixture that replicates pack cooling conditions. Pulsing current experiments are used for validation over a wide range of operating conditions (ambient temperature, state of charge, current amplitude and pulse width). The model is shown to match experimental results with good accuracy.

Session: 2-30-1 WA5 Modeling, Estimation, and Control of Automotive Systems
10:00 AM-12:00 PM
DSCC2014-6175: Location Isolability of Intake and Exhaust Manifold Leaks in a Turbocharged Diesel Engine With Exhaust Gas Recirculation
Michael Hand, Anna Stefanopoulou (University of Michigan)
Abstract: An investigation into the isolability of the location of intake and exhaust manifold leaks in heavy duty diesel engines is presented. In particular, established fault detection and isolation (FDI) methods are explored to assess their utility in successfully determining the location of a leak within the air path of an engine equipped with exhaust gas recirculation and an asymmetric twin-scroll turbine. It is further shown how consideration of the system's variation across multiple operating points can lead to improved ability to isolate the location of leaks in the intake and exhaust manifolds.

Session: 1-12-1 TA5 Dynamics and Control of Mobile and Locomotion Robots
13:30 PM-15:30 PM
DSCC2014-6302: Dynamic Weight-Shifting to Reduce Rollover Risk in High Speed Mobile Manipulators
John Broderick, Dawn M. Tilbury, Ella Atkins (University of Michigan)
Abstract: Energy storage is a major limiting factor for small unmanned ground vehicle endurance. This paper presents a hybrid model of a robot power system and a method to optimize power production and limit power loss for extended UGV operation. The optimization is based on a hybrid automaton model of the power system and produces the optimal controls for the different power components. An abstraction of power use and averaging of dynamics within a state can model the system with sufficient accuracy for power system optimization. Simulation studies of a Packbot equipped with a fuel cell and a battery are presented. The optimized power system is shown to require less energy over the mission compared to a baseline controller.

Session: 2-19-1 WP5 Intelligent Transportation Systems
16:00 PM-18:00 PM
DSCC2014-6269: A Multi-Stage Optimization Formulation for MPC-Based Obstacle Avoidance in Autonomous Vehicles Using a LIDAR sensor
Jiechao Liu (University of Michigan); Paramsothy Jayakumar (U.S. Army RDECOM-TARDEC); Jeffrey L. Stein, Tulga Ersal (University of Michigan)
Abstract: The dynamics of an autonomous unmanned ground vehicle (UGV) that is at least the size of a passenger vehicle are critical to consider during obstacle avoidance maneuvers to ensure vehicle safety. Methods developed so far do not take vehicle dynamics and sensor limitations into account simultaneously and systematically to guarantee the vehicle’s dynamical safety during avoidance maneuvers. To address this gap, this paper presents a model predictive control (MPC) based obstacle avoidance algorithm for high-speed, large-size UGVs that perceives the environment only through the information provided by a sensor, takes into account the sensing and control delays and the dynamic limitations of the vehicle, and provides smooth and continuous optimal solutions in terms of minimizing travel time. Specifically, information about the environment is obtained using an on-board Light Detection and Ranging (LIDAR) sensor. Ensuring the vehicle’s dynamical safety is translated into avoiding single tire lift-off. The obstacle avoidance problem is formulated as a multi-stage optimal control problem with a unique optimal solution. To solve the optimal control problem, it is transcribed into a nonlinear programming (NLP) problem using a pseudo-spectral method, and solved using the interior-point method. Sensing and control delays are explicitly taken into consideration in the formulation. Simulation results show that the algorithm is capable of generating smooth control commands to avoid obstacles while guaranteeing dynamical safety.

Session: 2-19-1 WP5 Intelligent Transportation Systems
16:00 PM-18:00 PM
DSCC2014-5884: A Mobile Tailgating Detection System for Law Enforcement Surveillance
Tyler J. Zellmer (Michelin) Paul T. Freeman, John R. Wagner, Kim E. Alexander, Philip Pidgeon (Clemson University)
Abstract: A number of automotive crashes occur each year due to semitrailers following passenger vehicles too closely on interstate highways and secondary roads. This hazardous practice, called tailgating, accounted for over 40% of the 110,000 trailer-passenger vehicle crashes recorded by the National Highway Traffic Safety Administration (NHTSA) in 2010. Tailgating is difficult to detect and document using visual methods and law enforcement agencies must depend on trained officers, whose abilities may be limited. In this paper, a proposed tailgating detection system, mounted to the officer’s patrol vehicle, continuously monitors both passenger and commercial vehicles, as the officer travels down the roadway. A rotating laser range-finding sensor feeds information to a microprocessor that continuously searches for the occurrence of tailgating. A weighting algorithm determines when a tailgating event has definitively occurred to reduce system sensitivity. If an event is detected, the officer is notified with audio and visual cues. A time stamped record including all relevant system information for later use in legal prosecution is also produced. In a virtual case study, the computer generated roadway environment was populated with vehicles of varying velocity and location. The numerical results show that the detection algorithm was able to successfully locate all of the virtual vehicles and accurately determine tailgating events under a number of different simulation conditions.

Session: 1-11-1 WP4 Energy Storage: Transportation & Grid Applications
16:00 PM-18:00 PM
DSCC2014-6058 (Invited Session Paper)
An Electrical Microgrid: Integration of Solar Panels, Compressed Air Storage, and a Micro-Cap Gas Turbine
Shreyas M. Patel (Solar Topps LLC); Paul T. Freeman, John R. Wagner (Clemson University)
Abstract: Non-renewable energy sources such as coal, oil, and natural gas are being consumed at a brisk pace while greenhouse gases contribute to atmospheric pollution. A global shift is underway toward the inclusion of renewable energy sources, such as solar and wind, for generating electrical and mechanical power. To meet this emerging demand, a solar based electrical microgrid study is underway at Clemson University. Solar energy is harvested from photovoltaic panels capable of producing 15 kW of DC power. Compressed air energy storage has been evaluated using the generated solar power to operate an electric motor driven piston compressor. The compressed air is then stored under pressure and supplied to a natural gas driven Capstone C30 MicroTurbine with attached electric power generator. The compressed air facilitates the turbine’s rotor-blade operated compression stage resulting in direct energy savings. A series of mathematical models have been developed. To evaluate the feasibility and energy efficiency improvements, the experimental and simulation results indicated that 127.8 watts of peak power was delivered at 17.5 Volts and 7.3 Amps from each solar panel. The average power generation over a 24-hour time period from 115 panels was 15 kW DC or 6 kW of AC power at 208/240 VAC and 25 Amps from the inverter. This electrical power could run a 5.2 kW reciprocating compressor for approximately 5 hours storing 1,108 kg of air at a 1.2 MPa pressure. A case study indicated that the microturbine, when operated without compressed air storage, consumed 11.2 kg of gaseous propane for 30 kW?hr of energy generation. In contrast, the microturbine operated in conjunction with solar supplied air storage could generate 50.8 kW?hr of electrical energy for a similar amount of fuel consumption. The study indicated an 8.1% efficiency improvement in energy generated by the system which utilized compressed air energy storage over the traditional approach.

Session: 1-1-1 WP6 Dynamic Systems Modeling for the Design and Optimization of Vehicle Systems
16:00 PM-18:00 PM
DSCC2014-6065: Thermoelectric Generation Using Diesel Engine Exhaust Waste Heat
Kelly Austin, xin she, John R. Wagner (Clemson University)
No abstract available.


Thursday, October 23, 2014

Session: 1-12-1 TA5 Dynamics and Control of Mobile and Locomotion Robots
10:00 AM-12:00 PM
DSCC2014-6111 (Invited Session Paper)
Modeling and Scheduling of Multiple Power Sources for a Ground Robot
John Broderick, Dawn M. Tilbury, Ella Atkins (University of Michigan)
Abstract: Energy storage is a major limiting factor for small unmanned ground vehicle endurance. This paper presents a hybrid model of a robot power system and a method to optimize power production and limit power loss for extended UGV operation. The optimization is based on a hybrid automaton model of the power system and produces the optimal controls for the different power components. An abstraction of power use and averaging of dynamics within a state can model the system with sufficient accuracy for power system optimization. Simulation studies of a Packbot equipped with a fuel cell and a battery are presented. The optimized power system is shown to require less energy over the mission compared to a baseline controller.

Session: 2-33-1 TM7 Modeling and Control of IC Engines
13:30 PM-15:30 PM
DSCC2014-6275 (Invited Session Paper)
A Low-Order HCCI Model Extended to Capture SI-HCCI Mode Transition Data with Two-Stage Cam Switching
Patrick Gorzelic, Prasad Shingne, Jason Martz, Anna Stefanopoulou (University of Michigan); Jeff Sterniak, Li Jiang (Robert Bosch LLC)
Abstract: A low-order homogeneous charge compression ignition (HCCI) combustion model to support model-based control development for spark ignition (SI)/HCCI mode transitions is presented. Emphasis is placed on mode transition strategies wherein SI combustion is abruptly switched to recompression HCCI combustion through a change of the cam lift and opening of the throttle, as is often employed in studies utilizing two-stage cam switching devices. The model is parameterized to a steady-state dataset which considers throttled operation and significant air-fuel ratio variation, which are pertinent conditions to two-stage cam switching mode transition strategies. Inspection and simulation of transient SI to HCCI (SI-HCCI) mode transition data shows that the extreme conditions present when switching from SI to HCCI can cause significant prediction error in the combustion performance outputs even with the model’s adequate steady-state fit. When a correction factor related to residual gas temperature is introduced to account for these extreme conditions, it is shown that the model reproduces transient performance output time histories in SI-HCCI mode transition data. The model is thus able to capture steady-state data as well as transient SI-HCCI mode transition data while maintaining a low-order cycle to cycle structure, making it tractable for model-based control of SI-HCCI mode transitions.

Session: 2-33-1 TM7 Modeling and Control of IC Engines
13:30 PM-15:30 PM
DSCC2014-6146 (Invited Session Paper)
Methodology to Evaluate the Fuel Economy of a Multimode Combustion Engine With Three-Way Catalytic Converter
Sandro Nuesch (University of Michigan); Li Jiang, Jeff Sterniak (Robert Bosch LLC); Anna Stefanopoulou (University Of Michigan)
Abstract: Highly diluted, low temperature homogeneous charge compression ignition (HCCI) combustion leads to ultra-low levels of engine-out NOx emissions. A standard drive cycle, however, would require switches between HCCI and spark-ignited (SI) combustion modes. In this paper a methodology is introduced, investigating the fuel economy of such a multimode combustion concept in combination with a three-way catalytic converter (TWC). The TWC needs to exhibit unoccupied oxygen storage sites in order to show acceptable performance. But the lean exhaust gas during HCCI operation fills the oxygen storage and leads to a drop in NOx conversion efficiency. Eventually the levels of NOx become unacceptable and a mode switch to a fuel rich combustion mode is necessary in order to deplete the oxygen storage. The resulting lean-rich cycling leads to a penalty in fuel economy. In order to evaluate the impact of those penalties on fuel economy, a finite state model for combustion mode switches is combined with a longitudinal vehicle model and a phenomenological TWC model, focused on oxygen storage. The aftertreatment model is calibrated using combustion mode switch experiments from lean HCCI to rich spark-assisted HCCI and back. Fuel and emissions maps acquired in steady state experiments are used. Two depletion strategies are compared in terms of their influence on drive cycle fuel economy and NOx emissions.

Session: 2-33-1 TM7 Modeling and Control of IC Engines
13:30 PM-15:30 PM
DSCC2014-6001
Cooling Air Temperature And Mass Flow Rate Control For Hybrid Electric Vehicle Battery Thermal Management
Xinran Tao, John R. Wagner (Clemson University)
Abstract: Lithium-Ion (Li-ion) batteries are widely used in electric and hybrid electric vehicles for energy storage. However, a Li-ion battery’s lifespan and performance is reduced if it’s overheated during operation. To maintain the battery’s temperature below established thresholds, the heat generated during charge/discharge must be removed and this requires an effective cooling system. This paper introduces a battery thermal management system (BTMS) based on a dynamic thermal-electric model of a cylindrical battery. The heat generation rate estimated by this model helps to actively control the air mass flow rate. A nonlinear back-stepping controller and a linear optimal controller are developed to identify the ideal cooling air temperature which stabilizes the battery core. The simulation of two different operating scenarios and three control strategies has been conducted. Simulation results indicate that the proposed controllers can stabilize the battery core temperature with peak tracking errors smaller than 2.4ºC by regulating the cooling air temperature and mass flow rate. Overall the model-based controllers developed for the battery thermal management system show improvements in both temperature tracking and cooling system power conservation in comparison to the classical controller. The next step in this study is to integrate these elements into a holistic cooling configuration with AC system compressor control to minimize the cooling power consumption.


Friday, October 24, 2014

Session: 2-9-1 FA4 Electrochemical Energy Systems
10:00 AM-12:00 PM
DSCC2014-6254: Battery State of Health Monitoring by Side Reaction Current Density Estimation via Retrospective-Cost Subsystem Identification
Xin Zhou, Tulga Ersal, Jeffrey L. Stein, Dennis S. Bernstein (University of Michigan)
Abstract: This paper introduces a method to estimate battery state of health (SoH) via health-relevant electrochemical features. Battery state of health estimation is a critical part of battery management because it allows for balancing the trade-off between maximizing performance and minimizing degradation. In this paper, a health-relevant electrochemical feature, the side reaction current density, is used as the indicator of battery SoH. An estimation algorithm is required due to the unavailability of the side reaction current density via noninvasive methods. In this paper, Retrospective-Cost Subsystem Identification (RCSI) is used to estimate the side reaction current density via identification of an unknown battery health subsystem that generates the side reaction current density. Simulation results are provided for constant current charge and discharge cycles with different C rates. A current profile for an electric vehicle (EV) going through Urban Dynamometer Driving Schedule (UDDS) cycles is also used as the excitation signal during estimation. The simulations show promising results in battery health dynamic identification and side reaction current density estimation with RCSI.

Session: 2-9-1 FA4 Electrochemical Energy Systems
10:00 AM-12:00 PM
DSCC2014-6352: Temperature Estimation in a Battery String under Frugal Sensor Allocation
Xinfan Lin, Shankar Mohan, Jason Siegel, Anna Stefanopoulou (University of Michigan)
Abstract: In electric vehicle applications, batteries are usually packed in modules to satisfy the energy and power demand. To facilitate the thermal management of a battery pack, a model-based observer could be designed to estimate the temperature distribution across the pack. Nevertheless, cost target in industry practice drives the number of temperature sensors in a pack to a number that is not sufficient to yield observability of all the temperature states. This paper focuses on formulating the observer design and sensor deployment strategy that could achieve the optimal observer performance under the frugal sensor allocation. The considered observer performance is the estimation errors induced by model and sensor uncertainty. The observer aims at minimizing the worst-case estimation errors under bounded model and sensor uncertainty.

Session: 2-10-1 FM4 Energy Storage and Optimization
13:30 PM-15:30 PM
DSCC2014-5933: Utilizing Intra-day Prediction Modification Strategies to Improve Peak Power Shaving Using Energy Storage Systems for Smart Buildings
Zheng Wang, Adrian Clarke, James Moyne, Dawn M. Tilbury (University of Michigan) Abstract: Peak power shaving is a technique that can be used to reduce monthly electricity bills. As control of Energy Storage Systems (ESS) is based on predicted power demand, power demand forecasting is a necessary component of entire building power optimization. Various forecasting methods have been developed. However, the importance of intra-day prediction error is overlooked by present models. In this paper, a variety of dynamic intra-day model modification strategies utilizing intra-day prediction error are proposed to improve power demand prediction and peak shaving performance. These modification strategies could be applied to any models which do the prediction at the beginning of the day. A Self-Organizing Map (SOM) & Support Vector Regression (SVR) Adaptive Hybrid Model proposed in previous literature is chosen as baseline in this paper. The method of bisection is adopted to calculate the optimal threshold to control the ESS. Simulation results demonstrate effectiveness of intra-day prediction modification strategies.