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

TL;DR

This paper presents a data-driven Gaussian Process model for Li-ion battery ageing during cycling, capable of learning from real operation data to improve prediction accuracy and reduce laboratory testing requirements.

Contribution

It introduces a tailored covariance function for cycling ageing, demonstrating effective learning from limited data and extending model applicability to real-world conditions.

Findings

Achieves 1.04% mean-absolute-error with only 26 cells trained.

Validates model across diverse cycling and temperature conditions.

Shows potential to reduce laboratory testing through data-driven modeling.

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. The first paper of the series focussed on the systematic modelling and experimental verification of cell degradation through calendar ageing. Conversantly, this second paper addresses the same research challenge when the cell is electrically cycled. A specific covariance function is composed, tailored for use in a battery ageing application. Over an extensive dataset involving 124 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 26 tested cells achieves an overall mean-absolute-error of 1.04% in the capacity curve prediction, after being validated under a broad window of both dynamic and static cycling temperatures, Depth-of-Discharge, middle-SOC, charging and discharging C-rates.