Prediction of battery cooling processes in electric vehicles by Physics-Informed Neural Networks

通过物理信息神经网络预测电动汽车电池冷却过程

• 批准号: 2733957
• 金额: --
• 依托单位: Queen Mary University of London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2733957
• 关键词: Prediction; battery; cooling; processes; electric

This project exploits the latest developments in the field of machine learning and computational modelling and uses the so-called 'Physics-Informed Neural Network' (PNN) to model the time-dependent cooling processes in real battery packs. This leads to substantial reduction in the computational cost and therefore allows for utilisation of the method as a predictive design tool in industry.Battery thermal management (BTM) is an ongoing major challenge hindering the wide application of electric vehicles (EV). It is well established that batteries can operate optimally only in a small temperature range, while there are a number of time-dependent factors that can heavily influence battery temperature. For example, the drive cycles and thus battery discharge scenarios are highly unsteady, turning BTM to a dynamic problem. Nonetheless, a vast majority of the existing models of battery cooling are limited to steady cases. More importantly, they are predominantly focused on a single or a small number of battery cells. Yet, in practice, an EV often uses thousands of cells. These shortcomings have contributed to the formation of a major gap between capabilities of the existing models and the practical needs of industry.Performance prediction of BTM systems requires careful consideration of electrochemistry, fluid dynamics and heat transfer. Addition of unsteadiness and complex configuration of a battery packs renders the task quite complicated. This makes the conventional modelling approaches expensive and therefore impractical. To resolve this issue, this project exploits the latest developments in the field of machine learning and computational modelling and uses the so-called 'Physics-Informed Neural Network' (PNN) to model the time-dependent cooling processes in real battery packs. This leads to substantial reduction in the computational cost and therefore allows for utilisation of the method as a predictive design tool in industry.The project includes generation of limited training and testing data by using CFD integrated with the existing electrochemical models. In parallel, PNN models are developed through combining deep learning techniques with the governing physical equations of the system. This builds upon a recently developed PNN-based flow simulator in our group and advances that to include electrochemistry. The PNN will be validated and refined using the data generated by conventional modelling.

该项目利用了机器学习和计算建模领域的最新发展，并使用所谓的物理信息神经网络(PNN)对实际电池组中随时间变化的冷却过程进行建模。这使得计算成本大大降低，从而允许该方法作为预测设计工具在工业中使用。电池热管理(BTM)是阻碍电动汽车(EV)广泛应用的持续主要挑战。众所周知，电池只能在很小的温度范围内以最佳方式运行，而有许多与时间相关的因素可以严重影响电池温度。例如，驱动周期和电池放电情况非常不稳定，将BTM变成了一个动态问题。尽管如此，现有的绝大多数电池冷却模型仅限于稳定情况下。更重要的是，他们主要专注于单个或少数电池单元。然而，在实践中，一辆电动汽车经常使用数千个电池。这些缺陷导致了现有模型的性能与工业实际需求之间的巨大差距。BTM系统的性能预测需要仔细考虑电化学、流体动力学和传热学。再加上电池组的不稳定性和复杂的结构，使得这项任务变得相当复杂。这使得传统的建模方法成本高昂，因此不切实际。为了解决这一问题，该项目利用了机器学习和计算建模领域的最新发展，并使用所谓的物理信息神经网络(PNN)对实际电池组中随时间变化的冷却过程进行建模。这使得计算成本大大降低，从而允许将该方法作为预测设计工具在工业中使用。该项目包括通过使用CFD与现有电化学模型相结合来生成有限的训练和测试数据。同时，通过将深度学习技术与系统的控制物理方程相结合，建立了PNN模型。这建立在我们小组最近开发的基于PNN的流动模拟器的基础上，并将其包括电化学。将使用通过常规建模产生的数据来验证和改进PNN。