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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Teodorescu, Remus
Aalborg University
in Cooperation with on an Cooperation-Score of 37%
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Publications (7/7 displayed)
- 2024Computationally Efficient Machine-Learning-Based Online Battery State of Health Estimation
- 2017Short-Circuit Degradation of 10-kV 10-A SiC MOSFETcitations
- 2015Extensive EIS characterization of commercially available lithium polymer battery cell for performance modellingcitations
- 2014Reduction of DC-link Capacitor in Case of Cascade Multilevel Converters by means of Reactive Power Controlcitations
- 2014Diagnosis of Lithium-Ion Batteries State-of-Health based on Electrochemical Impedance Spectroscopy Techniquecitations
- 2014Diagnosis of Lithium-Ion Batteries State-of-Health based on Electrochemical Impedance Spectroscopy Techniquecitations
- 2012Modular Multilevel Converter Modelling, Control and Analysis under Grid Frequency Deviations
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document
Computationally Efficient Machine-Learning-Based Online Battery State of Health Estimation
Abstract
A key function of battery management systems (BMS) in e-mobility applications is estimating the battery state of health (SoH) with high accuracy. This is typically achieved in commercial BMS using model-based methods. There has been considerable research in developing data-driven methods for improving the accuracy of SoH estimation. The data-driven methods are diverse and use different machine-learning (ML) or artificial intelligence (AI) based techniques. Complex AI/ML techniques are difficult to implement in low-cost microcontrollers used in BMS due to the extensive use of non-linear functions and large matrix operations. This paper proposes a computationally efficient and data-lightweight SoH estimation technique. Online impedance at four discrete frequencies is evaluated to derive the features of a linear regression problem. The proposed solution avoids complex mathematical operations and it is well-suited for online implementation in a commercial BMS. The accuracy of this method is validated on two experimental datasets and is shown to have a mean absolute error (MAE) of less than 2% across diverse training and testing data.