Lithium-ion battery state of health estimation

锂离子电池健康状态评估

• 批准号: 2601805
• 金额: --
• 依托单位: University of Bath
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2601805
• 关键词: Lithium; ion; battery; state; health

Electric vehicles (EVs) play a key role in decreasing the carbon footprint of the mobility sector. Their high upfront cost, limited range and slow charging speed are however a barrier to increased EV uptake. Reducing the cost and improving the EV Lithium-ion (Li-ion) battery could reduce these barriers. There is, however, limited knowledge in the safe operation and degradation rate of Li-ion batteries. This is largely due to the complex electrochemical mechanisms not being well understood. Furthermore, the large operating envelope (temperature, charging speed etc.) over its lifetime require resource intensive testing to parameterize semi-empirical models. The battery is therefore operated very conservatively, resulting in oversizing the battery and sub-optimal operating conditions resulting in inefficiencies and higher costs.This PhD aims to provide optimal testing strategies and accurate modelling of Li-ion batteries in order to provide information to facilitate more efficient operating strategies (e.g. fast charging). This will be achieved by a combination of advanced design of experiments (DOE), modelling and machine learning. The core of the PhD, will focus on methods to estimate the state of health of the battery cells over various operating ranges. The initial part of the PhD will focus on building a model structure which is based on a data driven neural network. The accuracy of this model will then be assessed using existing battery data in literature and data provided by the industrial partner. An experimental test campaign will then be designed and implemented, in an attempt to efficiently parameterize the battery models. The resultant battery models would then provide important information to improve the safe operation range of the battery.More efficient testing methods and accurate modelling of battery degradation will speed up development time in the design phase of the Electric Vehicle production. It will also facilitate development of new battery architectures and more efficient operating schemes (e.g. improved fast charging strategies). Both of these developments would decrease the overall cost of batteries in EVs. This could decrease overall EV cost and accelerate their uptake by consumers, thereby reducing the overall carbon impact of the mobility sector.

电动汽车（EV）在减少交通部门的碳足迹方面发挥着关键作用。然而，它们高昂的前期成本、有限的续航里程和缓慢的充电速度是增加电动汽车普及率的障碍。降低成本和改进电动汽车锂离子电池可以减少这些障碍。然而，对锂离子电池的安全操作和退化率的了解有限。这在很大程度上是由于复杂的电化学机制没有得到很好的理解。此外，较大的操作范围（温度、充电速度等）在其生命周期内，需要进行资源密集型测试，以参数化半经验模型。因此，电池的操作非常保守，导致电池尺寸过大，次优操作条件导致效率低下和成本上升。本博士旨在提供锂离子电池的最佳测试策略和准确建模，以提供信息，促进更有效的操作策略（例如快速充电）。这将通过先进的实验设计（DOE），建模和机器学习的组合来实现。博士学位的核心将专注于评估电池在各种工作范围内的健康状态的方法。博士学位的初始部分将专注于构建基于数据驱动神经网络的模型结构。然后，将使用文献中现有的电池数据和工业合作伙伴提供的数据来评估该模型的准确性。然后将设计和实施实验测试活动，以有效地参数化电池模型。由此产生的电池模型将为提高电池的安全工作范围提供重要信息。更有效的测试方法和电池退化的准确建模将加快电动汽车生产设计阶段的开发时间。它还将促进开发新的电池架构和更有效的操作方案（例如改进的快速充电策略）。这两项发展都将降低电动汽车电池的整体成本。这可以降低电动汽车的整体成本，并加速消费者对电动汽车的吸收，从而减少移动行业的整体碳影响。