Online SOH prediction of a battery is essential for battery management in real-world applications. Although many approaches have been proposed to date, most conventional approaches to SOH prediction neither incorporate intra-cycle variation of discharge currents nor consider non-standard charging and discharging practices along cycles. For real-world applicability, the above two factors, varying discharge current and non-standard practices, are crucial to deal with. This paper presents an approach to online SOH prediction based on charging and discharging cycles under intra-cycle variation of discharge current and non-standard charging and discharging practices. Here, for online SOH prediction, we represent a cyclic history of terminal voltages and currents by a vector sequence formed by four indices: total charge, voltage-time entropy, average and variance of current, where average and variance of current are intended to deal with varying discharge current. Furthermore, we identify various minimum ranges of charging and discharging voltages that can provide accurate relation between four indices and SOH under non-standard practices, while transforming this relation into that of standard practices. For experiments, an LSTM stack is implemented for the proposed online SOH prediction. The results indicate that the proposed approach is capable of accurate online SOH prediction even under randomly varying discharging currents and non-standard practices with RMSE errors of about 0.5% as well as of about 95%.
How to Cite
Lithum-ion battery, state-of-health, SOH, long-short-term-memory, LSTM, voltage distribution entropy
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.