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A PDE-Based Approach for Fault Detection in Li-Ion Batteries

[+] Author Affiliations
Hasan Ferdowsi

Texas A&M University, Texarkana, Texarkana, TX

Nima Lotfi

Southern Illinois University Edwardsville, Edwardsville, IL

Paper No. DSCC2017-5367, pp. V002T19A004; 9 pages
doi:10.1115/DSCC2017-5367
From:
  • 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
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5828-8
  • Copyright © 2017 by ASME

abstract

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.

Copyright © 2017 by ASME

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