The prediction accuracy of Lithium-ion (Li-ion) battery ageing models based on laboratory data is uncertain in the context of online prediction. This is due to the difficulty to reproduce realistic operating profiles in laboratory. The development of self-adaptive ageing models, which are updated using the ageing data obtained in operation, allows enhancing the online prediction accuracy and reducing the required characterisation period in laboratory. At the same time, it offers the possibility to maximise systems' profitability, providing useful information to update the energy management strategy and for predictive maintenance purposes.

The present study aims at reviewing, classifying and comparing the different self-adaptive Li-ion battery ageing models proposed in the literature. Firstly, the different characteristics influencing the ability of a model to update itself are identified, and a classification is proposed for self-adaptive Li-ion battery ageing modelling methods. Secondly, specific criteria are defined to assess and compare the accuracy and computational cost of the different models, enabling a selection of the most suitable ones. Finally, relevant conclusions are drawn considering the key features required to achieve effective ageing predictions, and concise recommendations are suggested for future self-adaptive Li-ion battery ageing model development.

在在线预测的背景下，基于实验室数据的锂离子（Li-ion）电池老化模型的预测准确性尚不确定。这是由于难以在实验室中再现真实的操作配置文件。自适应老化模型的开发可以使用操作中获得的老化数据进行更新，从而可以提高在线预测的准确性，并减少实验室所需的表征时间。同时，它提供了最大化系统收益的可能性，提供了有用的信息来更新能源管理策略并用于预测性维护目的。

本研究旨在回顾，分类和比较文献中提出的不同的自适应锂离子电池老化模型。首先，确定了影响模型更新能力的不同特征，并提出了自适应锂离子电池老化建模方法的分类。其次，定义了特定的标准来评估和比较不同模型的准确性和计算成本，从而可以选择最合适的模型。最后，考虑实现有效老化预测所需的关键特征，得出相关结论，并为未来的自适应锂离子电池老化模型开发提供简洁的建议。