Lithium-Ion Battery Degradation and Performance Prediction in 2nd-Life Applications project

该项目提供了一个独特的机会，可以在电动汽车（EV）电池可持续发展和循环经济的广泛领域接受由BP赞助的全额资助的博士生奖学金。在“第二次生命”中重复使用EV电池的概念-在回收之前延长其使用寿命-已经得到了广泛认可，并被视为可持续EV采用的重要组成部分。众所周知，锂离子电池在使用过程中会随着时间的推移而降解。在他们服役的头几年，这种退化在很大程度上是众所周知的。然而，电动汽车电池在第二次使用寿命期间将如何老化存在相当大的不确定性。本博士的目的是更好地定义在长时间使用期间驱动电池内退化的基础老化机制。一个重点领域将是预测电池寿命后期非线性退化的开始，其中电池容量下降得更快，这通常表明电池接近其使用寿命的终点：也称为“拐点”。此外，该博士学位还将研究通过不同的热管理策略或不同的电循环策略是否可以显著改变电池退化的轨迹。该博士学位的重点主要是在WMG能源创新中心进行的实验研究，并在BP（英国）进行现场工作。研究人员将被要求设计，实施和管理新的多学科实验，以生成支持他们的建议和结论所需的数据。

This project represents a unique opportunity to undertake a fully funded PhD studentship, sponsored by BP in the broad area of electric vehicle (EV) battery sustainably and circular economy.The concept of reusing EV batteries in a "2nd-life" - extending their useful life before being recycled is well established and seen as a vital part of sustainable EV adoption. It is well understood that lithium-ion batteries degrade as they are used and over time. In their first years of service, this degradation is largely well understood. However, considerable uncertainty exists in how EV batteries will age during 2nd-life operation.The aim of this PhD is to better define the underpinning ageing mechanisms that drive degradation within batteries during extended periods of use. One focus area will be the prediction of the onset of non-linear degradation later in battery life where battery capacity reduces more rapidly, which often indicates the battery is close to the end of its usable life: also known as the "knee-point". In addition, this PhD will seek to identify if the trajectory of battery degradation can be significantly changed through different thermal management strategies or different electrical cycling strategies.The focus of this PhD is primarily on experimental research to be conducted within the WMG Energy Innovation Centre with agreed periods of on-site work at BP (UK). The researcher will be required to design, implemented, and manage novel multidisciplinary experiments to generate the data required to underpin their recommendations and conclusions.