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Online state of charge and temperature distribution monitoring in batteries for automotive applications.

机译：用于汽车应用的电池的在线充电状态和温度分布监控。

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摘要

Electrification has been the most viable way for achieving clean and efficient transportation as demonstrated in hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and battery electric vehicles (BEVs). As the most prominent energy storage device for these advanced electrified vehicles, batteries have been intensively investigated and used in recent years. However, under specific operating conditions, such as fast acceleration and regenerative braking, high current magnitudes can be delivered from or to the battery cells. Consequently, the large amount of heat generation caused by over-charging, over-discharging, and over-temperature may bring potential permanent damages to the batteries. In this respect, accurate monitoring of key operational parameters, such as current, voltage, temperature, state of charge (SOC), and state of health (SOH), is fundamental to the design and control of an effective battery management system (BMS).;In this dissertation, existing SOC and SOH estimation techniques are analyzed first with emphasis on their merits and demerits as applied to automotive applications. Based on the concept of impulse response, a precise online SOC estimation method is proposed. Furthermore, two pattern recognition techniques for detection of SOC are presented and compared. Experimental validations have been conducted on two battery systems.;Continuous monitoring of temperature distribution throughout the battery is crucial to determination of the SOH for the battery system. This dissertation also explores different temperature distribution estimation methods, namely, using thermodynamic and heat transfer principles in the battery, based on which a three-dimensional (3D) electro-thermal model has been developed and validated through multiple simulations and experiments. However, due to the computational time and system requirements, this numerical model cannot be employed for real time monitoring of temperature distribution in an online thermal management application. Therefore, a temperature estimation model based on virtual thermal sensors (VTS) for the Li-ion battery is proposed next. This model, using a small number of physical sensors, is able to estimate temperature distribution throughout the battery in real time under unknown initial values and model uncertainty. Experimental results from a high energy Li-ion battery validate the effectiveness of the proposed VTS model under various charging and discharging conditions. In addition, a new temperature estimation method based on thermal impulse response is also developed which doesn't require in-depth knowledge of battery chemical and structural properties. This method is able to accurately predict the temperature variations on the surface of a Li-ion battery.
机译：如混合动力电动汽车（HEV），插电式混合动力电动汽车（PHEV）和电池电动汽车（BEV）所示，电气化已成为实现清洁和高效运输的最可行方法。作为用于这些高级电动车辆的最突出的能量存储装置，近年来对电池进行了深入的研究和使用。然而，在特定的工作条件下，例如快速加速和再生制动，可以将高电流量从电池单元传递到电池单元或传递到电池单元。因此，由于过度充电，过度放电和温度过高而产生的大量热量可能会对电池造成潜在的永久损坏。在这方面，准确监控关键操作参数，例如电流，电压，温度，充电状态（SOC）和运行状况（SOH），对于有效的电池管理系统（BMS）的设计和控制至关重要本文首先对现有的SOC和SOH估计技术进行了分析，重点是它们在汽车应用中的优缺点。基于脉冲响应的概念，提出了一种精确的在线SOC估计方法。此外，提出并比较了两种用于检测SOC的模式识别技术。已经在两个电池系统上进行了实验验证。连续监控整个电池的温度分布对于确定电池系统的SOH至关重要。本文还探索了不同的温度分布估计方法，即利用电池中的热力学和传热原理，在此基础上建立了三维（3D）电热模型，并通过多次仿真和实验验证了模型。但是，由于计算时间和系统要求，该数值模型不能用于在线热管理应用程序中温度分布的实时监控。因此，接下来提出基于虚拟热传感器（VTS）的锂离子电池温度估算模型。该模型使用少量物理传感器，能够在未知初始值和模型不确定性的情况下实时估算整个电池的温度分布。高能量锂离子电池的实验结果验证了所提出的VTS模型在各种充电和放电条件下的有效性。此外，还开发了一种新的基于热冲击响应的温度估算方法，该方法不需要深入了解电池的化学和结构特性。这种方法能够准确地预测锂离子电池表面的温度变化。

著录项

  • 作者

    Xiao, Ying.;

  • 作者单位

    The University of Texas at Dallas.;

  • 授予单位 The University of Texas at Dallas.;
  • 学科 Electrical engineering.;Automotive engineering.
  • 学位 Ph.D.
  • 年度 2015
  • 页码 139 p.
  • 总页数 139
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 康复医学;
  • 关键词

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