Abstract
The importance of accurate estimation of the state-of-health (SOH) for Lithium-ion (Li-ion) batteries is going to increase as Li-ion batteries become more integrated into daily life. As the reliance on Li-ion batteries increases so does the need for battery pack size optimisation and the extension of battery lifetime. Data-driven methods for estimation of the SOH of Li-ion batteries have shown to have good performance under laboratory conditions, but often fail to achieve similar performance when used in real life applications. This is a consequence of the field data seldomly matching the laboratory data, which is a necessary condition of most data-driven methods. A method which aims to account for discrepancies between laboratory and field data is transfer learning. This paper shows how the transfer learning algorithm kernel mean matching can be used to transfer both multiple linear regression (MLR) and bootstrapped random vector functional link (BRVFL) models from the laboratory to the field. It is shown that these methods can achieve mean absolute percentage errors (MAPE’s) smaller than 1% on both laboratory and field data simultaneously.
Highlights
T HERE has been an increase in the use of Lithiumion (Li-ion) batteries in daily life both through the deployment of more electric vehicles and in grid-connected residential energy storage systems
The methods used to parameterise the SOH estimation models based on the laboratory data, were chosen to be as simple as possible, while having good performance, narrowing the methods to multiple linear regression (MLR) and a bootstrapped variant of random vector functional link neural networks (BRVFL)
The analyses performed in the paper shows the ease of use and implementation of transfer learning for both the MLR and bootstrapped random vector functional link (BRVFL) methods
Summary
T HERE has been an increase in the use of Lithiumion (Li-ion) batteries in daily life both through the deployment of more electric vehicles and in grid-connected residential energy storage systems. It is, in the best interest of manufacturers and end users to optimise the size of the battery packs and the lifetime of the battery from both an environmental and economic perspective. Accurately estimating the battery state-of-health (SOH) often requires extensive and expensive laboratory experiments, which can quickly become obsolete. The physics-driven methods aim to model the the internal states of the battery using theory of physics, chemistry, and electrical circuits. Among the most popular approaches are the electrochemical models consisting of par-
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