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

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）来模拟真实电池组中随时间变化的冷却过程。这导致了计算成本的大幅降低，因此允许将该方法用作工业中的预测设计工具。电池热管理（BTM）一直是阻碍电动汽车（EV）广泛应用的主要挑战。众所周知，电池只能在很小的温度范围内工作，而许多与时间相关的因素会严重影响电池温度。例如，驱动循环和电池放电场景是高度不稳定的，使BTM成为一个动态问题。尽管如此，绝大多数现有的电池冷却模型仅限于稳定的情况。更重要的是，它们主要集中在单个或少数电池单元上。然而，在实践中，一辆电动汽车通常使用数千个电池。这些缺点造成了现有模型的能力与工业实际需要之间的重大差距。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。再加上电池组的不稳定性和复杂的配置，使任务变得相当复杂。这使得传统的建模方法昂贵，因此不切实际。为了解决这个问题，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）来模拟真实电池组中随时间变化的冷却过程。这导致了计算成本的大幅降低，因此允许将该方法用作工业中的预测设计工具。该项目包括利用CFD与现有电化学模型集成生成有限的训练和测试数据。同时，PNN模型是通过将深度学习技术与系统的控制物理方程相结合来开发的。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，并将其推进到包括电化学。PNN将使用传统建模生成的数据进行验证和改进。

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.