Data-driven nonparametric Li-ion battery ageing model aiming at learning from real operation data -- Part A: Storage operation

TL;DR

This paper presents a data-driven, nonparametric Li-ion battery ageing model based on Gaussian Processes, capable of learning from real operation data to improve prediction accuracy and extend operational understanding, with minimal laboratory testing.

Contribution

It develops a Gaussian Process-based ageing model tailored for battery storage, demonstrating effective learning from limited real-world data and validating its accuracy across diverse conditions.

Findings

Model trained with 18 cells achieves 0.53% MAE in capacity prediction.

The model effectively learns from limited data, reducing laboratory testing needs.

Validated over three years of data under various temperature and SOC conditions.

Abstract

Conventional Li-ion battery ageing models, such as electrochemical, semi-empirical and empirical models, require a significant amount of time and experimental resources to provide accurate predictions under realistic operating conditions. At the same time, there is significant interest from industry in the introduction of new data collection telemetry technology. This implies the forthcoming availability of a significant amount of real-world battery operation data. In this context, the development of ageing models able to learn from in-field battery operation data is an interesting solution to mitigate the need for exhaustive laboratory testing. In a series of two papers, a data-driven ageing model is developed for Li-ion batteries under the Gaussian Process framework. A special emphasis is placed on illustrating the ability of the Gaussian Process model to learn from new data observations, providing more accurate and confident predictions, and extending the operating window of the model. This first paper focusses on the systematic modelling and experimental verification of cell degradation through calendar ageing. A specific covariance function is composed, tailored for use in a battery ageing application. Over an extensive dataset involving 32 cells tested during more than three years, different training possibilities are contemplated in order to quantify the minimal number of laboratory tests required for the design of an accurate ageing model. A model trained with only 18 tested cells achieves an overall mean-absolute-error of 0.53% in the capacity curves prediction, after being validated under a broad window of both dynamic and static temperature and SOC storage conditions.