Model-Based Stochastic Fault Detection and Diagnosis of Lithium-Ion Batteries

被引:26
作者
Son, Jeongeun [1 ]
Du, Yuncheng [1 ]
机构
[1] Clarkson Univ, Dept Chem & Biomol Engn, Potsdam, NY 13676 USA
基金
美国国家科学基金会;
关键词
fault detection and classification; uncertainty analysis; lithium-ion battery; optimization; thermal management; polynomial chaos expansion; DESIGN; CLASSIFICATION; ALGORITHMS;
D O I
10.3390/pr7010038
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
The Lithium-ion battery (Li-ion) has become the dominant energy storage solution in many applications, such as hybrid electric and electric vehicles, due to its higher energy density and longer life cycle. For these applications, the battery should perform reliably and pose no safety threats. However, the performance of Li-ion batteries can be affected by abnormal thermal behaviors, defined as faults. It is essential to develop a reliable thermal management system to accurately predict and monitor thermal behavior of a Li-ion battery. Using the first-principle models of batteries, this work presents a stochastic fault detection and diagnosis (FDD) algorithm to identify two particular faults in Li-ion battery cells, using easily measured quantities such as temperatures. In addition, models used for FDD are typically derived from the underlying physical phenomena. To make a model tractable and useful, it is common to make simplifications during the development of the model, which may consequently introduce a mismatch between models and battery cells. Further, FDD algorithms can be affected by uncertainty, which may originate from either intrinsic time varying phenomena or model calibration with noisy data. A two-step FDD algorithm is developed in this work to correct a model of Li-ion battery cells and to identify faulty operations in a normal operating condition. An iterative optimization problem is proposed to correct the model by incorporating the errors between the measured quantities and model predictions, which is followed by an optimization-based FDD to provide a probabilistic description of the occurrence of possible faults, while taking the uncertainty into account. The two-step stochastic FDD algorithm is shown to be efficient in terms of the fault detection rate for both individual and simultaneous faults in Li-ion batteries, as compared to Monte Carlo (MC) simulations.
引用
收藏
页数:19
相关论文
共 50 条
  • [31] Electrochemical model-based state estimation for lithium-ion batteries with adaptive unscented Kalman filter
    Li, Weihan
    Fan, Yue
    Ringbeck, Florian
    Jost, Dominik
    Han, Xuebing
    Ouyang, Minggao
    Sauer, Dirk Uwe
    JOURNAL OF POWER SOURCES, 2020, 476
  • [32] A Modified Model-Based Resistance Estimation of Lithium-Ion Batteries Using Unscented Kalman Filter
    Chen, Jing-Long
    Wang, Ri-Xin
    WIRELESS AND SATELLITE SYSTEMS, PT I, 2019, 280 : 25 - 40
  • [33] Parameter sensitivity analysis of electrochemical model-based battery management systems for lithium-ion batteries
    Li, Weihan
    Cao, Decheng
    Joest, Dominik
    Ringbeck, Florian
    Kuipers, Matthias
    Frie, Fabian
    Sauer, Dirk Uwe
    APPLIED ENERGY, 2020, 269
  • [34] A systematic model-based degradation behavior recognition and health monitoring method for lithium-ion batteries
    Xiong, Rui
    Tian, Jinpeng
    Mu, Hao
    Wang, Chun
    APPLIED ENERGY, 2017, 207 : 372 - 383
  • [35] Model based insulation fault diagnosis for lithium-ion battery pack in electric vehicles
    Wang, Yujie
    Tian, Jiaqiang
    Chen, Zonghai
    Liu, Xingtao
    MEASUREMENT, 2019, 131 : 443 - 451
  • [36] Fault diagnosis method for lithium-ion batteries based on the combination of voltage prediction and Z-score
    Liao, Li
    Li, Xunbo
    Yang, Da
    Wu, Tiezhou
    Jiang, Jiuchun
    INTERNATIONAL JOURNAL OF GREEN ENERGY, 2024, : 3270 - 3287
  • [37] Comprehensive fault diagnosis of lithium-ion batteries: An innovative approach based on hybrid coding and genetic search
    Ji, Chunhui
    Jin, Guang
    Zhang, Ran
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2025, 141
  • [38] An online fault diagnosis method for lithium-ion batteries based on signal decomposition and dimensionless indicators selection
    Niu, Liyong
    Du, Jingcai
    Li, Shuowei
    Wang, Jing
    Zhang, Caiping
    Jiang, Yan
    JOURNAL OF ENERGY STORAGE, 2024, 83
  • [39] In-depth bibliometric analysis on research trends in fault diagnosis of lithium-ion batteries
    Lan, Jiamei
    Wei, Ruichao
    Huang, Shenshi
    Li, Dongping
    Zhao, Chen
    Yin, Liang
    Wang, Jian
    JOURNAL OF ENERGY STORAGE, 2022, 54
  • [40] Model-Based Investigations of Porous Si-Based Anodes for Lithium-Ion Batteries with Effects of Volume Changes
    Zhang, Xingyu
    Chen, Jian
    Bao, Yinhua
    ENERGIES, 2022, 15 (23)