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Effects of internal resistance on performance of batteries for electric vehicles.

机译：内阻对电动汽车电池性能的影响。

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摘要

An ever increasing acceptance of electric vehicles as passenger cars relies on better operation and control of large battery packs. The individual cells in large battery packs do not have identical characteristics and may degrade differently due to their manufacturing variability and other factors. It is beneficial to evaluate the performance gain by replacing certain battery modules/cells during actual driving. The following are the objectives of our research. We will develop an on-line battery module degradation diagnostic scheme using the intrinsic signals of a battery pack equalization circuit. Therefore, a battery "health map" can be constructed and updated in real time. Next based on the derived battery health map, the performance of the battery pack will be evaluated a user specified trip so as to evaluate the "worthiness of replacing" certain modules/cells. Different electric vehicles have different performance for the same driving cycle. These variations are due to variation in driving patterns, traffic, different light patterns, random behavior of the drivers etc. To account for this random behavior of the electric vehicle performance we generate 100 random trip cycles. We aim to model the behavior of the driving cycle and battery behavior. Finally, the thesis also explores the possibility of energy exchange between the battery packs and the smart grid. In the smart grid scenario where we have the knowledge of the electricity price and the load patterns on the grid, it is beneficial for the user to schedule charging and discharging patterns for electric vehicles. Our research will define charging and discharging patterns throughout the life of the battery. We will optimize the charging and discharging times and define the opportunity cost for each day during summer and winter months. The objective is to maximize the profit earned by selling excess energy in the battery to the grid and minimize the charging cost for the electric vehicle.
机译：乘用车对电动汽车的接受程度日益提高，这取决于大型电池组的更好操作和控制。大型电池组中的单个电池单元不具有相同的特性，并且由于其制造差异和其他因素而可能会有所不同。通过在实际行驶期间更换某些电池模块/电池来评估性能提升是有益的。以下是我们研究的目标。我们将使用电池组均衡电路的固有信号开发一种在线电池模块退化诊断方案。因此，可以实时构造和更新电池“健康状况图”。接下来，基于导出的电池健康状况图，将评估用户指定行程的电池组性能，以评估某些模块/电池的“更换价值”。对于相同的行驶周期，不同的电动汽车具有不同的性能。这些变化是由于驾驶模式，交通，不同的灯光模式，驾驶员的随机行为等的变化所导致的。为了说明电动汽车性能的这种随机行为，我们生成了100个随机行程周期。我们旨在对驾驶行为和电池行为进行建模。最后，本文还探讨了电池组与智能电网之间进行能量交换的可能性。在智能电网场景中，我们了解电价和电网上的负载模式，这对用户安排电动汽车的充电和放电模式很有帮助。我们的研究将定义整个电池寿命中的充电和放电模式。我们将优化充电和放电时间，并定义夏季和冬季月份每天的机会成本。目的是通过将电池中的多余能量出售给电网来获得最大的利润，并使电动汽车的充电成本最小化。

著录项

  • 作者

    Ugle, Rohit A.;

  • 作者单位

    The University of Wisconsin - Milwaukee.;

  • 授予单位 The University of Wisconsin - Milwaukee.;
  • 学科 Engineering Mechanical.
  • 学位 M.S.
  • 年度 2013
  • 页码 101 p.
  • 总页数 101
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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