Model-based real-time thermal fault diagnosis of Lithium-ion batteries |
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| Affiliation: | 1. Civil and Environmental Engineering, University of California, Berkeley, CA 94720, USA;2. Clemson University – International Center for Automotive Research, Greenville, SC 29607, USA;1. State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China;2. Department of Naval Architecture and Marine Engineering, University of Michigan, Ann Arbor, MI 48109, USA;1. Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University, Germany;2. Institute for Power Generation and Storage Systems (PGS), E.ON ERC, RWTH Aachen University, Germany;3. Jülich Aachen Research Alliance, JARA-Energy, Germany;1. Australian Energy Research Institute, School of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney 2052, Australia;2. Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia;1. Department of Electrical and Computer Engineering, San Diego State University, 5500 Campanile Drive, San Diego, CA 92182, USA;2. Department of Electrical and Computer Engineering, University of California San Diego, 9500 Gilman Dr., La Jolla, CA 92093, USA |
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| Abstract: | Ensuring safety and reliability is a critical objective of advanced Battery Management Systems (BMSs) for Li-ion batteries. In order to achieve this objective, advanced BMS must implement diagnostic algorithms that are capable of diagnosing several battery faults. One set of such critical faults in Li-ion batteries are thermal faults which can be potentially catastrophic. In this paper, a diagnostic algorithm is presented that diagnoses thermal faults in Lithium-ion batteries. The algorithm is based on a two-state thermal model describing the dynamics of the surface and the core temperature of a battery cell. The residual signals for fault detection are generated by nonlinear observers with measured surface temperature and a reconstructed core temperature feedback. Furthermore, an adaptive threshold generator is designed to suppress the effect of modelling uncertainties. The residuals are then compared with these adaptive thresholds to evaluate the occurrence of faults. Simulation and experimental studies are presented to illustrate the effectiveness of the proposed scheme. |
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| Keywords: | Lithium-ion batteries Fault diagnosis Thermal faults Observer Adaptive threshold |
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