Integrating Electrochemical Modeling with Machine Learning for Lithium-Ion Batteries
Integrating Electrochemical Modeling with Machine Learning for Lithium-Ion Batteries
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DOI：
10.23919/acc50511.2021.9482997
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发表时间：
2021-03
期刊：
影响因子：
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通讯作者：
H. Tu;S. Moura;H. Fang
中科院分区：
文献类型：
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作者：
H. Tu;S. Moura;H. Fang
锂离子电池(LIB)的数学建模是先进电池管理的核心挑战。本文提出了一种将基于物理的模型与机器学习相结合的新方法，以实现LIBS的高精度建模。这种方法独特地提出了将物理模型的动态状态告知机器学习模型，从而实现了物理和机器学习之间的深度集成。基于这种方法，我们提出了两种混合物理-机器学习模型，将单粒子模型与热力学模型(SPMT)和前馈神经网络(FNN)相结合，对LIB的动态行为进行物理信息学习。大量的模拟表明，所提出的模型在结构上相对简约，即使在高C率下也能提供相当大的预测精度。
Mathematical modeling of lithium-ion batteries (LiBs) is a central challenge in advanced battery management. This paper presents a new approach to integrate a physics-based model with machine learning to achieve high-precision modeling for LiBs. This approach uniquely proposes to inform the machine learning model of the dynamic state of the physical model, enabling a deep integration between physics and machine learning. We propose two hybrid physics-machine learning models based on the approach, which blend a single particle model with thermal dynamics (SPMT) with a feedforward neural network (FNN) to perform physics-informed learning of a LiB's dynamic behavior. The proposed models are relatively parsimonious in structure and can provide considerable predictive accuracy even at high C-rates, as shown by extensive simulations.