Projects per year
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.
Original language | English |
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Article number | 1414 |
Journal | Electronics |
Volume | 11 |
Issue number | 9 |
Number of pages | 11 |
ISSN | 2079-9292 |
DOIs | |
Publication status | Published - 2022 |
Keywords
- electric vehicle
- lithium-ion battery
- state-of-health
- machine learning
- neural networks
- capacity degradation
- battery management system
- battery impedance
- battery ageing
- Li-ion battery
- SoH
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Dive into the research topics of 'Capacity State-of-Health Estimation of Electric Vehicle Batteries Using Machine Learning and Impedance Measurements'. Together they form a unique fingerprint.Projects
- 2 Finished
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Workshop Automated Battery Tester
Schaltz, E., Stroe, D., Gismero, A. & Miltersen, A. H.
01/08/2019 → 31/07/2022
Project: Research
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Batnostic: adaptive BATtery diagNOSTIC tools for lifetime assessment of EV batteries
01/01/2016 → 31/12/2018
Project: Research