Despite the widespread commercialization of Li-ion batteries in various markets including portable electronics, electrified transportation, and stationary energy storage systems, their safety and reliability still poses as a concern in the eyes of industry and general public. There has been great strides in the past few decades in the development of Battery Management Systems (BMSs). The majority of the efforts, however, avoid fault occurrence by conservative designs rather than directly incorporating fault diagnostics in the BMS. Such a functionality in the BMS would enable the detection of the occurrence, type, and location of the faults and therefore, a proper reaction to them. Realizing the need for such a feature in the BMSs, the development of a model-based fault detection scheme is proposed in this paper. This method is formulated based on the original PDEs describing a single particle electrochemical battery model. The use of PDEs in the fault detection scheme reduces uncertainties arising from the model approximation. Furthermore, the convergence of this PDE-based approach is proved using Lyapunov stability theorem. Finally, the effectiveness of the proposed method in detecting various fault types ranging from incipient degradation mechanisms to abrupt faults is illustrated through simulations.
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ASME 2017 Dynamic Systems and Control Conference
October 11–13, 2017
Tysons, Virginia, USA
Conference Sponsors:
- Dynamic Systems and Control Division
ISBN:
978-0-7918-5828-8
PROCEEDINGS PAPER
A PDE-Based Approach for Fault Detection in Li-Ion Batteries
Hasan Ferdowsi,
Hasan Ferdowsi
Texas A&M University, Texarkana, Texarkana, TX
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Nima Lotfi
Nima Lotfi
Southern Illinois University Edwardsville, Edwardsville, IL
Search for other works by this author on:
Hasan Ferdowsi
Texas A&M University, Texarkana, Texarkana, TX
Nima Lotfi
Southern Illinois University Edwardsville, Edwardsville, IL
Paper No:
DSCC2017-5367, V002T19A004; 9 pages
Published Online:
November 14, 2017
Citation
Ferdowsi, H, & Lotfi, N. "A PDE-Based Approach for Fault Detection in Li-Ion Batteries." Proceedings of the ASME 2017 Dynamic Systems and Control Conference. Volume 2: Mechatronics; Estimation and Identification; Uncertain Systems and Robustness; Path Planning and Motion Control; Tracking Control Systems; Multi-Agent and Networked Systems; Manufacturing; Intelligent Transportation and Vehicles; Sensors and Actuators; Diagnostics and Detection; Unmanned, Ground and Surface Robotics; Motion and Vibration Control Applications. Tysons, Virginia, USA. October 11–13, 2017. V002T19A004. ASME. https://doi.org/10.1115/DSCC2017-5367
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