Grey-box modelling of lithium-ion batteries using neural ordinary differential equations

被引：9
作者：
Brucker J. [1 ]
Bessler W.G. [1 ]
Gasper R. [1 ]
机构：
[1] Institute of Energy Systems Technology, Offenburg University of Applied Sciences, Badstraße 24, Offenburg
来源：
Energy Informatics | 2021年 / 4卷 / Suppl 3期
关键词：
Dynamic systems; Equivalent circuit model; External variables; Grey-box model; Lithium-ion batteries; Neural ordinary differential equations;
D O I：
10.1186/s42162-021-00170-8
中图分类号：
学科分类号：
摘要：
Grey-box modelling combines physical and data-driven models to benefit from their respective advantages. Neural ordinary differential equations (NODEs) offer new possibilities for grey-box modelling, as differential equations given by physical laws and neural networks can be combined in a single modelling framework. This simplifies the simulation and optimization and allows to consider irregularly-sampled data during training and evaluation of the model. We demonstrate this approach using two levels of model complexity; first, a simple parallel resistor-capacitor circuit; and second, an equivalent circuit model of a lithium-ion battery cell, where the change of the voltage drop over the resistor-capacitor circuit including its dependence on current and State-of-Charge is implemented as NODE. After training, both models show good agreement with analytical solutions respectively with experimental data. © 2021, The Author(s).
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