论文标题
使用渐近方法的锂离子电池的还原热化学模型的系统推导和验证
Systematic derivation and validation of a reduced thermal-electrochemical model for lithium-ion batteries using asymptotic methods
论文作者
论文摘要
使用锂离子电池的广泛使用的Doyler-Fuller-Newman（DFN）模型对于某些应用来说在计算上太昂贵了，这激发了大量简单模型的外观。这些模型通常以临时方式构成，导致与DFN模型的不一致和同一模型的多个公式，而单个粒子模型（SPM）是后者的一个很好的例子。在这项工作中，我们讨论了SPM型模型的概念，表明尽管文献中发现了多种表述，但这些模型始终遵循相同的结构，并且我们将此讨论扩展到了占热效应的模型。然后，我们使用渐近技术以系统的方式介绍了一个以系统方式得出的热单粒子模型。 TSPME对热DFN模型的验证非常高准确性，计算成本较小40倍。与实验数据的比较表明，该模型在预测真实电池的行为方面可以做一个合理的工作，但是需要一个非常好的参数集来获得准确的预测。
The widely used Doyler-Fuller-Newman (DFN) model for lithium-ion batteries is too computationally expensive for certain applications, which has motivated the appearance of a plethora of simpler models. These models are usually posed in an ad hoc manner, leading to inconsistencies with the DFN model and to multiple formulations of the same model, with the Single Particle Model (SPM) being a very good example of the latter. In this work, we discuss the concept of SPM-type models showing that, despite the multiple formulations found in the literature, these models always follow the same structure, and we extend this discussion to models accounting for thermal effects. Then, we present a Thermal Single Particle Model with electrolyte (TSPMe) derived in a systematic manner using asymptotic techniques. The validation of the TSPMe against a thermal DFN model shows very high accuracy with a computational cost over forty times smaller. The comparison against experimental data shows that the model does a reasonable job predicting the behaviour of a real battery, but a very good parameter set is required to obtain accurate predictions.
