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Battery state-of-charge estimation using machine learning analysis of ultrasonic signatures

能源与人工智能(英文) 2022年第4期10卷 60-72页

作者： elias Galiounas Tom G.Tranter rhodri e.owen James B.Robinson Paul R.Shearing Dan J.L.Brett electrochemical Innovation Lab Department of Chemical EngineeringUniversity College LondonLondonWC1E 7JEUK The Faraday Institution Quad OneBecquerel AvenueHarwell CampusDidcotOX11 The Alan Turing Institute British Library96 Euston RoadLondon NW12DB

The potential of acoustic signatures to be used for State-of-Charge(SoC)estimation is demonstrated using artificial neural network regression *** approach represents a streamlined method of processing the entire acoustic waveform instead of performing manual,and often arbitrary,waveform peak *** applications where computational economy is prioritised,simple metrics of statistical significance are used to formally identify the most informative waveform *** alone can be exploited for SoC *** is further shown that signal portions representing both early and late interfacial reflections can correlate highly with the SoC and be of predictive value,challenging the more common peak selection methods which focus on the *** later echoes represent greater through-thickness coverage,and are intuitively more information-rich,their presence is not *** waveform treatment offers a more robust approach to correlating acoustic signatures to electrochemical *** is further demonstrated that transformation into the frequency domain can reduce the dimensionality of the problem significantly,while also improving the estimation *** importantly,it is shown that acoustic signatures can be used as sole model inputs to produce highly accurate SoC estimates,without any complementary voltage *** makes the method suitable for applications where redundancy and diversification of SoC estimation approaches is *** is obtained experimentally from a 210 mAh LiCoO2/graphite pouch *** estimation errors as low as 0.75%are achieved on a SoC scale of 0-100%.

关键词： Battery diagnostics Ultrasonic battery monitoring Acoustic battery inspection Mechanical-electrochemical correlation Machine learning Artificial neural networks

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electrospinning as a route to advanced carbon fibre materials for selected low-temperature electrochemical devices:A review

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Journal of energy Chemistry 2021年第8期30卷 492-529,I0011页

作者： Yue Wen Matt D.R.Kok Jorge Pavel Victoria Tafoya Ana BJorge Sobrido ellsworth Bell Jeff T.Gostick Servann Herou Philipp Schlee Maria-Magdalena Titirici Dan J.L.Brett Paul R.Shearing rhodri Jervis The electrochemical Innovation Lab Dept.of Chemical EngineeringUniversity College LondonTorrington PlaceWC1E 7JE LondonUK Queen Mary University of London School of Engineering and Materials ScienceMile End RoadE14NS LondonUK Department of Chemical engineering University of WaterlooWaterlooCanada Department of Chemical engineering Imperial College LondonSouth Kensington CampusSW72AZ LondonUK

electrospinning has been proven as a highly versatile fabrication method for producing nano-structured fibres with controllable morphology,of both the fibres themselves and the void structure of the ***,it is possible to use heteroatom doped polymers or to include catalytic precursors in the electrospinning solution to control the surface properties of the *** factors make it an ideal method for the production of electrodes and flow media for a variety of electrochemical devices,enabling reduction in mass transport and activation overpotentials and therefore increasing ***,the use of biomass as a polymer source has recently gained attention for the ability to embed sustainable principles in the materials of electrochemical devices,complementing their ability to allow an increase in the use of renewable electricity via their *** this review,the historical and recent developments of electrospun materials for application in redox flow batteries,fuel cells,metal air batteries and supercapacitors are thoroughly reviewed,including an overview of the electrospinning process and a guide to best ***,we provide an outlook for the emerging use of this process in the field of electrochemical energy devices with the hope that the combination of tailored microstructure,surface functionality and computer modelling will herald a new era of bespoke functional materials that can significantly improve the performance of the devices in which they are used.

关键词： electrospinning Redox flow batteries Fuel cells Metal-air batteries Supercapacitors

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