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Online Health Monitoring in Lithium-Ion Battery for Electrified Transportation Systems

机译:电动运输系统锂离子电池的在线健康监测

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摘要

Lithium-ion batteries have been proven to be a promising solution for powering vehicles since they feature high energy density, power density, low self-discharge and more importantly, long cycle life. However, due to cycle aging and calendar aging, the degradation of batteries also imposes additional electrical and thermal stress on the cells, resulting in a safety hazard.;This research tries to identify the degraded lithium-ion cells based on their electrical and thermal signatures. To focus on the characteristics of lithium-ion cells for electrified transportation systems, capacity and power are chosen to be indications of degradation level. An online estimation method is proposed to identify these indications and diagnose the health of the lithium-ion cell. Multiple experiments have proved the effectiveness of method. Moreover, the cell performance at various driving scenarios is also studied to understand their electrical and thermal stress under different circumstances. Based on the understanding of electrochemical process and the simulation of equivalent circuit model, the electrothermal behavior of the lithium-ion can be accurately simulated by the model incorporated with the effects of current and temperature. Then, the single-cell model is extended for a pack of batteries and verified by the experiment. The electrical characteristics of the cells in the pack have been studied to identify the possible cell-to-cell imbalance. Based on the electrical characteristics, the discrepant cell among a pack of cells with the parallel structure is identified and its health status is also diagnosed with the proposed estimation method. In this way, the abnormal cells in a pack can be identified and diagnosed in a complete procedure.
机译:锂离子电池由于具有高能量密度,功率密度,低自放电以及更重要的是较长的循环寿命,因此已被证明是为汽车提供动力的解决方案。然而,由于循环老化和日历老化,电池的退化也会对电池芯施加额外的电和热应力,从而导致安全隐患。这项研究试图根据退化的锂离子电池的电和热特征来识别它们。为了专注于电动运输系统中锂离子电池的特性,选择容量和功率作为退化程度的指标。提出了一种在线估算方法来识别这些迹象并诊断锂离子电池的健康状况。多次实验证明了该方法的有效性。此外,还研究了各种驾驶场景下的电池性能,以了解其在不同情况下的电应力和热应力。基于对电化学过程的理解和等效电路模型的仿真,可以通过结合电流和温度影响的模型来精确地模拟锂离子的电热行为。然后,将单电池模型扩展为一组电池,并通过实验进行验证。已经研究了电池组中电池的电气特性,以确定可能的电池间不平衡。根据电气特性,识别出具有平行结构的电池组中的差异电池,并使用提出的估算方法诊断其健康状况。这样,可以在一个完整的过程中识别并诊断包装中的异常细胞。

著录项

  • 作者

    Yang, Zhuo.;

  • 作者单位

    The University of Texas at Dallas.;

  • 授予单位 The University of Texas at Dallas.;
  • 学科 Electrical engineering.
  • 学位 Ph.D.
  • 年度 2018
  • 页码 130 p.
  • 总页数 130
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 康复医学;
  • 关键词

  • 入库时间 2022-08-17 11:53:15

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