2026-09-20T14:17:26Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/109411 2023-07-11 col_2142_5131 col_2142_14787 com_2142_5130 com_2142_9630 com_2142_234

Alleyne, Andrew Alleyne, Andrew Beck, Carolyn Salapaka, Srinivasa Mehta, Prashant Tannous, Pamela Joseph 2021-03-05T21:38:14Z 2021-03-05T21:38:14Z 2020-12-02 2020-12 Driven by a desire to achieve reduced carbon emissions and maintenance costs, along with an increase in efficiency and performance, electrification has become a major trend in modern vehicles. This increase in electrification is accompanied by an increase in thermal power dissipated due to electrical inefficiencies. Consequently, temperature regulation becomes a greater challenge for these safety-critical systems. Electrified vehicles consist of systems of systems that operate over a wide span of energy domains and timescales. To ensure their safe, reliable, and efficient performance, a holistic system perspective for estimation is needed. Accurate dynamic state estimation is critical for two main reasons: 1. Thermal management: This dissertation proposes a system perspective state estimation framework for complex multi-domain and multi-timescale dynamical systems. The framework consists of a multilevel hierarchical network of observers with each level having a unique update rate. To account for the significant interactions between subsystems, a novel bidirectional coordination strategy is developed. Sufficient conditions for the stability and convergence of the hierarchical network are derived. Experimental validation is conducted on a testbed representative of a fluid thermal management system of an electrified aircraft. Closed-loop simulation and experimental results confirm a reduction in computational cost compared to a conventional centralized observer and an increase in estimation accuracy compared to a decentralized observer which ignores coupling between subsystems. 2. Fault diagnosis: This dissertation proposes a robust system-perspective fault diagnosis framework for complex energy systems. Fault detection and isolation is derived from a set of structured residuals obtained from a bank of observers. Robustness is achieved by decoupling the unknown disturbances such as modeling error, linearization error, parameter variation, and noise from the residuals. The proposed approach is validated on a testbed representative of a fluid thermal management system of an electrified aircraft. Simulation and experimental results demonstrate successful fault detection and isolation with no false alarms or missed detections. Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2021-03-04 without embargo terms The student, Pamela Tannous, accepted the attached license on 2020-12-01 at 12:25. The student, Pamela Tannous, submitted this Dissertation for approval on 2020-12-01 at 12:26. This Dissertation was approved for publication on 2020-12-02 at 08:18. DSpace SAF Submission Ingestion Package generated from Vireo submission #16010 on 2021-03-04 at 15:35:38 Made available in DSpace on 2021-03-05T21:38:14Z (GMT). No. of bitstreams: 2 TANNOUS-DISSERTATION-2020.pdf: 7512439 bytes, checksum: 1a7de48dcd63d5aedafd8ec05118ef28 (MD5) LICENSE.txt: 4211 bytes, checksum: 34fb9758afadcbf3d380dc28e471c09e (MD5) Previous issue date: 2020-12-02 application/pdf http://hdl.handle.net/2142/109411 en Copyright 2020 Pamela Tannous Estimation vehicle energy systems hierarchical estimation hierarchical control fault diagnosis model predictive control electrified vehicles Estimation and fault diagnosis for vehicle energy systems text Thesis Mechanical Sci & Engineering Mechanical Engineering University of Illinois at Urbana-Champaign Dissertation Ph.D.