Prognostics for Lithium-ion batteries for electric Vertical Take-off and Landing aircraft using data-driven machine learning

Mitici, Mihaela; Hennink, Birgitte; Pavel, Marilena D.; Dong, Jianning

doi:10.1016/j.egyai.2023.100233

Cited by 14 publications

(5 citation statements)

References 43 publications

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“…To cope with the wide operating range of EVs and always deliver reliable SOH estimation, there exist some early works to formulate the estimation task based on different usage profiles. Mitici et al [31] constructed the features based on the charge and discharge process and conducted a feature importance analysis. However, the highly dynamic discharging process and irregular charging profiles may hinder the performance of such a method in real-world EV applications.…”

Section: Introductionmentioning

confidence: 99%

State of Health Estimation for Lithium-Ion Batteries Under Arbitrary Usage Using Data-Driven Multimodel Fusion

Zhang

Wik

Bergstrom

et al. 2024

IEEE Trans. Transp. Electrific.

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Accurately estimating the state of health (SoH) of batteries is indispensable for the safety, reliability, and optimal energy and power management of electric vehicles. However, from a data-driven perspective, complications, such as dynamic vehicle operating conditions, stochastic user behaviors, and cellto-cell variations, make the estimation task challenging. This work develops a data-driven multi-model fusion method for SoH estimation under arbitrary usage profiles. All possible operating conditions are categorized into six scenarios. For each scenario, an appropriate feature set is extracted to indicate the SoH. Based on the obtained features, four machine learning algorithms are applied individually to train SoH estimation models using time-series data. In addition to the estimates at the current time step, a histogram data-based and online adaptive model is taken from previous work for predicting the next-step SoH. Then, a Kalman filter is applied to systematically fuse the results of all the estimation and prediction models. Experimental data collected from different types of batteries operated under diverse profiles verify the effectiveness and practicability of the developed method, as well as its superiority over individual models.

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Section: Introductionmentioning

confidence: 99%

State of Health Estimation for Lithium-Ion Batteries Under Arbitrary Usage Using Data-Driven Multimodel Fusion

Zhang

Wik

Bergstrom

et al. 2024

IEEE Trans. Transp. Electrific.

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“…Nevertheless, the advantages of the PEMFCs make them perfect alternatives to fossil based power systems. Technical specifications related to different research topics of PEMFCs can be tracked in several publications [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

AI based Metaheuristic Optimization of PEMFC Control System for Robust and Efficient Operation

Aboshosha

Gouda

Awad

et al. 2023

Preprint

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Proton Exchange Membrane Fuel Cells (PEMFC) have several merits over the other fuel cells like low operating temperature, high power density, light weight, and robustness, fitting them in a wide variety of green energy applications. The most important application area of the PEMFC is hydrogen cars (Fuel Cell Electric Vehicles – FCEV), stationary and portable hydrogen power systems. Throughout the presented research work the complete three cycles of the fuel cell control are presented which are the control system, the FCEV control and the AI based metaheuristic optimization. The presented algorithm includes the PI controller – PI-based HHO-PI-based AEO-PI-based GWO-PI-based PSO. An objective statistical comparison among the presented algorithms to select the best one of them according to perfect criteria.

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“…Correspondingly, the typical eVTOL designs require a power-to-energy ratio (effective discharge rates) ranging from 10C to 60C with peak power required both at the beginning of the discharge cycle (low depth of discharge) and end of discharge (high depth of discharge) as shown schematically in Figure A. In specific power terms, these values can potentially exceed 1000 kW/kg depending on the mass of the payload, mission profile, and aircraft design. ,, …”

mentioning

confidence: 99%

“…In specific power terms, these values can potentially exceed 1000 kW/kg depending on the mass of the payload, mission profile, and aircraft design. 2,22,23 In recent years, significant research efforts have been directed toward enhancing the energy density, power output, and overall performance of LiBs. 24 However, the extreme power demands inherent to eVTOL operations necessitate a specialized investigation.…”

mentioning

confidence: 99%

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Lithium-Ion Battery Power Performance Assessment for the Climb Step of an Electric Vertical Takeoff and Landing (eVTOL) Application

Dixit,

Bisht,

Essehli

et al. 2024

ACS Energy Lett.

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Prognostics for Lithium-ion batteries for electric Vertical Take-off and Landing aircraft using data-driven machine learning

Cited by 14 publications

References 43 publications

State of Health Estimation for Lithium-Ion Batteries Under Arbitrary Usage Using Data-Driven Multimodel Fusion

State of Health Estimation for Lithium-Ion Batteries Under Arbitrary Usage Using Data-Driven Multimodel Fusion

AI based Metaheuristic Optimization of PEMFC Control System for Robust and Efficient Operation

Lithium-Ion Battery Power Performance Assessment for the Climb Step of an Electric Vertical Takeoff and Landing (eVTOL) Application

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