ARC Researchers at the ASME
2015 Dynamic Systems and Control Conference
(October 28-30, 2015 at Columbus, Ohio, USA)

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).

We are proud to announce the paper DSCC2015-9747 "An MPC Algorithm With Combined Speed and Steering Control for Obstacle Avoidance in Autonomous Ground Vehicles" authored by Mr. Jiechao Liu, et al, has been selected as a Best Student Paper Finalist.

Session: 2-3-1 TP6 Rollover Prevention (AVS)
Thursday, October 29, 2015 16:00 PM-18:00 PM
DSCC2015-9747 An MPC Algorithm With Combined Speed and Steering Control for Obstacle Avoidance in Autonomous Ground Vehicles
Jiechao Liu, University of Michigan; Paramsothy Jayakumar, U.S. Army RDECOM-TARDEC; Jeffrey Stein, Tulga Ersal, University of Michigan
Abstract: This article presents a model predictive control based obstacle avoidance algorithm for autonomous ground vehicles in unstructured environments. The novelty of the algorithm is the simultaneous optimization of speed and steering without a priori knowledge about the obstacles. Obstacles are detected using a planar light detection and ranging sensor and a multi-phase optimal control problem is formulated to optimize the speed and steering commands within the detection range. Acceleration capability of the vehicle as a function of speed, and stability and handling concerns such as tire lift-off are taken into account as constraints in the optimization problem, whereas the cost function is formulated to navigate the vehicle as quickly as possible with smooth control commands. Thus, a safe and quick navigation is enabled without the need for a preloaded map of the environment. Simulation results show that the proposed algorithm is capable of navigating the vehicle through obstacle fields that cannot be cleared with steering control alone.
 
Session: 2-4-2 WM7 Battery Management 2
Wednesday, October 28, 2015 13:30 PM-15:30 PM
DSCC2015-9966

On Improving Battery State of Charge Estimation using Bulk Force Measurements
Shankar Mohan, Youngki Kim, Anna G. Stefanopoulou, University of Michigan

Abstract: Lithium-ion (Li-ion) batteries undergo physical deformation as their state-of-charge (SOC) changes. The physical deformation causes changes in the pressure (equivalently, force) applied at the end-plates of constrained battery pack or module. This paper proposes the fusion of bulk force and battery voltage measurements to estimate the SOC in Li-ion battery packs. In this paper, using discrete Linear Quadratic Estimators (dLQEs), the advantage of using force measurements in addition to voltage measurement to improve SOC estimates is quantitatively studied with simulations. It is observed that including force measurements can decrease the mean and standard deviation of SOC estimation error by 50% in some SOC intervals.
 
Session: 1-29-1 WP6 Automotive Engine Control
Wednesday, October 28, 2015 16:00 PM-18:00 PM
DSCC2015-9729 Effective Component Tuning in a Diesel Engine Model Using Sensitivity Analysis
Rasoul Salehi, Anna G. Stefanopoulou, University of Michigan
(not ARC funded)
Abstract: Error propagation and accumulation is a common problem for system level engine modeling at which individually modeled components are connected to form a complete engine model. Engines with exhaust gas recirculation (EGR) and turbocharging have components connected in a feedback configuration (the exhaust conditions affect the intake and the intake, consequently, affects the exhaust), thus they have a challenging model tuning process. This paper presents a systematic procedure for effective tuning of an engine air-charge path model to improve accuracy at the system level as well as reducing the computational complexity of tuning a large set of components. Based on using sensitivity analysis, the presented procedure is used to inspect which component influences more a set of selected outputs in a model with high degree of freedom caused by many parameters of different components. After selecting the influential component, which is the turbocharger in this study, further tuning is applied to parameters in the component to increase the overall accuracy of the complete engine model. The corrections applied to the aircharge path model of a 6 cylinder 13L heavy duty diesel engine with EGR and twin-scroll turbocharger was shown to effectively improve the model accuracy.
 
Session: 2-2-2 TM6 Automotive 3: Internal Combustion Engines
Thursday, October 29, 2015 13:30 PM-15:30 PM
DSCC2015-9875 Is It Economical to Ignore the Driver? A Case Study on Multimode Combustion
Sandro P. Nuesch, Anna G. Stefanopoulou, University of Michigan
(not ARC funded)
Abstract: Ignoring the driver’s torque command can be beneficial for fuel economy, especially if it leads to extended residence time at efficient operating conditions. We answered this question for a particular engine, which allows mode switches between spark ignition (SI) and homogeneous charge compression ignition (HCCI) combustion. When operating such a multimode combustion engine it might be required to defer a load command outside the feasible regime of one combustion mode until a mode switch is accomplished. The resulting delays in engine torque response might negatively affect vehicle performance and drivability. In this paper a longitudinal vehicle model is presented, which incorporates dynamics associated with SI/HCCI mode switching. Two exemplary supervisory control strategies were evaluated in terms of fuel economy and torque behavior. It was seen that the duration of a mode switch may be short enough to avoid substantial impairment in torque response. This in turn would lead to the opportunity of purposefully ignoring the driver command. Thereby, the residence time in the beneficial HCCI combustion regime is prolonged and fuel-expensive mode switching avoided. The result is a trade-off between torque deviation and improvements in fuel economy. Finally, based on this trade-off the supervisory control strategy relying on a short-term prediction of engine load was seen to achieve similar fuel economy with slightly improved torque response than a strategy without prediction.
 
DSCC2015-9883 A Phenomenological Model for Predicting Combustion Phasing and Variability of Spark Assisted Compression Ignition (SACI) Engines
Niket Prakash, Jason B., Martz, Anna G. Stefanopoulou, University of Michigan
(not ARC funded)
Abstract: An advanced combustion mode, Spark Assisted Compression Ignition (SACI) has shown the ability to extend loads relative to Homogenous Charge Compression Ignition (HCCI) but at lower than fuel efficiency. SACI combustion is initiated by a spark followed by a rapid autoignition (HCCI-like combustion) of the remaining fraction of the fuel. Extending previous work the coefficients of the Wiebe function used to fit the two combustion phases, are here regressed as functions of the air path variables and actuator settings. The parameterized regression model enables the interpretation of the combustion model with manifold filling dynamics, which are essential for meanvalue modeling and model-based control of combustion phasing. SACI combustion, however exhibits high cyclic variability with random characteristics. Thus, control of combustion phasing needs to account for the cyclic variability to correctly fit the phasing data. This paper also documents the success of regressing the cyclic variability (standard deviation) at various operating conditions as again a function of air path variables and actuator settings. The combination of the two models is a breakthrough in predicting the mean-value engine behavior and the random statistics of the cycle-to-cycle variability.