Separating Multiscale Battery Dynamics and Predicting Multi-Step Ahead Voltage Simultaneously Through a Data-Driven Approach

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

Accurate prediction of battery performance under various ageing conditions is necessary for reliable and stable battery operations. Due to complex battery degradation mecha-nisms, estimating the accurate ageing level and ageing-dependent battery dynamics is difficult. This work presents a health-aware battery model that is capable of separating fast dynamics from slowly varying states of degradation and state of charge (SOC). The method is based on a sequence to sequence learning-based encoder-decoder model, where the encoder infers the slowly varying states as the latent space variables in an unsupervised way, and the decoder provides health-aware multi-step ahead prediction conditioned on slowly varying states from the encoder. The proposed approach is verified on a Lithium-ion battery ageing dataset based on real driving profiles of electric vehicles.
Original languageEnglish
Title of host publicationProceedings of the IEEE Conference on Vehicle Power and Propulsion (VPPC)
PublisherIEEE
Number of pages6
ISBN (Print)979-8-3503-4445-5
DOIs
Publication statusPublished - 2023
EventVPPC 2023: IEEE Vehicle Power and Propulsion Conference - Milan, Italy
Duration: 24 Oct 202327 Oct 2023

Publication series

Name2023 IEEE Vehicle Power and Propulsion Conference, VPPC 2023 - Proceedings

Conference

ConferenceVPPC 2023: IEEE Vehicle Power and Propulsion Conference
Country/TerritoryItaly
CityMilan
Period24/10/2327/10/23

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

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