JB
J.S. Bogaert
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2 records found
1
Master thesis
(2025)
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J.S. Bogaert, I.I. de Pater, J.S. Habib, P.C. Roling, P. Proesmans, D. Zappalá
Battery state of health (SOH) estimation is one of the three main analytical tasks of a battery management system (BMS), when viewed from engineering maintenance and prognostics perspective. With the current global effort towards more suitable and greener processes, lithium-ion batteries have shown to be an important element in facilitating this transition. One industry where this can be noticed in particular is the transportation sector, where a strong shift towards battery electric vehicles (BEV) can be observed. Within the aviation sector, current research efforts include electrical flight. However, numerous challenges remain, that are typically observable within a safety critical domain such as aerospace. One these challenges includes the determination of uncertainty in battery SOH prediction. This would provide improved transparency on the capabilities and limitation of a model, when used as part of a battery system. Within this report we propose the use of a bidirectional gated recurrent unit (Bi-GRU) with learnable soft attention, to predict battery SOH based on charge measurements. Uncertainty analysis is enabled through the use of simultaneous quantile regression (SQR) and orthonormal certificate (OC), to be able to highlight and distinguish the aleatoric and epistemic uncertainty of the proposed model. We afterwards evaluate the model for point prediction accuracy using standard metrics, and evaluate the produced uncertainty using specialised test cases and calibration metrics. We achieved strong results using the proposed framework on a 2-phase fast charging dataset published by Toyota.
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Battery state of health (SOH) estimation is one of the three main analytical tasks of a battery management system (BMS), when viewed from engineering maintenance and prognostics perspective. With the current global effort towards more suitable and greener processes, lithium-ion batteries have shown to be an important element in facilitating this transition. One industry where this can be noticed in particular is the transportation sector, where a strong shift towards battery electric vehicles (BEV) can be observed. Within the aviation sector, current research efforts include electrical flight. However, numerous challenges remain, that are typically observable within a safety critical domain such as aerospace. One these challenges includes the determination of uncertainty in battery SOH prediction. This would provide improved transparency on the capabilities and limitation of a model, when used as part of a battery system. Within this report we propose the use of a bidirectional gated recurrent unit (Bi-GRU) with learnable soft attention, to predict battery SOH based on charge measurements. Uncertainty analysis is enabled through the use of simultaneous quantile regression (SQR) and orthonormal certificate (OC), to be able to highlight and distinguish the aleatoric and epistemic uncertainty of the proposed model. We afterwards evaluate the model for point prediction accuracy using standard metrics, and evaluate the produced uncertainty using specialised test cases and calibration metrics. We achieved strong results using the proposed framework on a 2-phase fast charging dataset published by Toyota.
Bachelor thesis
(2023)
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J.S. Bogaert, F.R.G. Bononi Bello, G.W.A. van den Heuvel, A. Johary, Döme Kohlhéb, R. Teunissen, T.M. Trinh, T. Verhas, F.I. Vermeulen, C. van Zuylen, S.J. Watson, S.R. Turteltaub, V.O. Bonnin
The aim of this Design Synthesis Exercise was to design a floating large-scale wind farm of 1 GW in deep water using an Airborne Wind Energy System (AWES) that is cost-competitive, largely recyclable and uses less material than conventional wind turbines. By exploring the project foundation, carrying out an iterative single-system design process, and delving into the farm layout and management, this project assesses the feasibility of this novel concept. This report proposes an initial design with the resources currently available to the team while highlighting the next steps that need to be taken in order to pursue further design iterations, prototyping, and testing of this concept. This initial design, the tool developed to carry out the sizing, and a detailed reflection on the limitations of this design process and concept could be stated as the main contribution of this project to the field of airborne wind energy.
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The aim of this Design Synthesis Exercise was to design a floating large-scale wind farm of 1 GW in deep water using an Airborne Wind Energy System (AWES) that is cost-competitive, largely recyclable and uses less material than conventional wind turbines. By exploring the project foundation, carrying out an iterative single-system design process, and delving into the farm layout and management, this project assesses the feasibility of this novel concept. This report proposes an initial design with the resources currently available to the team while highlighting the next steps that need to be taken in order to pursue further design iterations, prototyping, and testing of this concept. This initial design, the tool developed to carry out the sizing, and a detailed reflection on the limitations of this design process and concept could be stated as the main contribution of this project to the field of airborne wind energy.