Projekter pr. år
Abstract
With the increasing adoption of electric vehicles (EVs) by the general public, a lot of research is being conducted in Li-ion battery-related topics, where state-of-health (SoH) estimation has a prominent role. Accurate knowledge of this parameter is essential for efficient and safe EV operation. In this work, machine-learning techniques are applied to estimate this parameter in EV applications and in diverse scenarios. After thoroughly analysing cell ageing in different storage conditions, a novel approach based on impedance data is developed for SoH estimation. A fully-connected feed-forward neural network (FC-FNN) is employed to estimate the battery’s maximum available capacity from a small set of impedance measurements. The method was tested for estimation in long-term scenarios and for diverse degradation procedures with data from real EV batteries. High accuracy was obtained in all situations, with a mean absolute error as low as 0.9%. Thus, the proposed algorithm constitutes a powerful and viable solution for fast and accurate SoH estimation in real-world battery management systems.
Originalsprog | Engelsk |
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Artikelnummer | 1414 |
Tidsskrift | Electronics |
Vol/bind | 11 |
Udgave nummer | 9 |
Antal sider | 11 |
ISSN | 2079-9292 |
DOI | |
Status | Udgivet - 2022 |
Fingeraftryk
Dyk ned i forskningsemnerne om 'Capacity State-of-Health Estimation of Electric Vehicle Batteries Using Machine Learning and Impedance Measurements'. Sammen danner de et unikt fingeraftryk.Projekter
- 2 Afsluttet
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Workshop Automated Battery Tester
Schaltz, E., Stroe, D., Gismero, A. & Miltersen, A. H.
01/08/2019 → 31/07/2022
Projekter: Projekt › Forskning
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Batnostic: adaptive BATtery diagNOSTIC tools for lifetime assessment of EV batteries
01/01/2016 → 31/12/2018
Projekter: Projekt › Forskning