Energy storage-aware prediction/control for mobile systems with unstructured loads
| dc.contributor.advisor | Longoria, Raul G. | |
| dc.creator | LeSage, Jonathan Robert, 1985- | en |
| dc.date.accessioned | 2013-09-26T17:52:03Z | en |
| dc.date.issued | 2013-08 | en |
| dc.date.submitted | August 2013 | en |
| dc.date.updated | 2013-09-26T17:52:04Z | en |
| dc.description | text | en |
| dc.description.abstract | Mobile systems, such as ground robots and electric vehicles, inherently operate in stochastic environments where load demands are largely unknown. Onboard energy storage, most commonly an electrochemical battery system, can significantly constrain operation. As such, mission planning and control of mobile systems can benefit from a priori knowledge about battery dynamics and constraints, especially the rate-capacity and recovery effects. To help overcome overly conservative predictions common with most existing battery remaining run-time algorithms, a prediction scheme was proposed. For characterization of a priori unknown power loads, an unsupervised Gaussian mixture routine identifies/clusters the measured power loads, and a jump-Markov chain characterizes the load transients. With the jump-Markov load forecasts, a model-based particle filter scheme predicts battery remaining run-time. Monte Carlo simulation studies demonstrate the marked improvement of the proposed technique. It was found that the increase in computational complexity from using a particle filter was justified for power load transient jumps greater than 13.4% of total system power. A multivariable reliability method was developed to assess the feasibility of a planned mission. The probability of mission completion is computed as the reliability integral of mission time exceeding the battery run-time. Because these random variables are inherently dependent, a bivariate characterization was necessary and a method is presented for online estimation of the process correlation via Bayesian updating. Finally, to abate transient shutdown of mobile systems, a model predictive control scheme is proposed that enforces battery terminal voltage constraints under stochastic loading conditions. A Monte Carlo simulation study of a small ground vehicle indicated significant improvement in both time and distance traveled as a result. For evaluation of the proposed methodologies, a laboratory terrain environment was designed and constructed for repeated mobile system discharge studies. The test environment consists of three distinct terrains. For each discharge study, a small unmanned ground vehicle traversed the stochastic terrain environment until battery exhaustion. Results from field tests with a Packbot ground vehicle in generic desert terrain were also used. Evaluation of the proposed prediction algorithms using the experimental studies, via relative accuracy and [alpha]-[lambda] prognostic metrics, indicated significant gains over existing methods. | en |
| dc.description.department | Mechanical Engineering | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.uri | http://hdl.handle.net/2152/21346 | en |
| dc.language.iso | en_US | en |
| dc.subject | Energy storage | en |
| dc.subject | Mobile systems | en |
| dc.subject | Ground robotics | en |
| dc.subject | Model-based prediction | en |
| dc.subject | Model-predictive control | en |
| dc.subject | Particle filter | en |
| dc.subject | Unsupervised learning | en |
| dc.title | Energy storage-aware prediction/control for mobile systems with unstructured loads | en |
| thesis.degree.department | Mechanical Engineering | en |
| thesis.degree.discipline | Mechanical Engineering | en |
| thesis.degree.grantor | The University of Texas at Austin | en |
| thesis.degree.level | Doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |