From 7398e79e605e174cd3540c11fa25ae4dcfe7574c Mon Sep 17 00:00:00 2001 From: beckyperriment <93582518+beckyperriment@users.noreply.github.com> Date: Thu, 7 Dec 2023 14:35:37 +0000 Subject: [PATCH] Update paper.bib --- joss/paper.bib | 164 ------------------------------------------------- 1 file changed, 164 deletions(-) diff --git a/joss/paper.bib b/joss/paper.bib index 8e955e7..0c79e0b 100644 --- a/joss/paper.bib +++ b/joss/paper.bib @@ -1,156 +1,3 @@ - -@article{reniers2019review, - title={Review and performance comparison of mechanical-chemical degradation models for lithium-ion batteries}, - author={Reniers, Jorn M and Mulder, Grietus and Howey, David A}, - journal={Journal of The Electrochemical Society}, - Doi = {10.1149/2.0281914jes}, - volume={166}, - number={14}, - pages={A3189}, - year={2019}, - publisher={IOP Publishing} -} - -@article{reiners2022digital, - title={Digital twin of a MWh-scale grid battery system for efficiency and degradation analysis}, - author={Reniers, Jorn M and Howey, David A}, - year={2022}, -} - - -@article{kumtepeli2020energy, - title={Energy arbitrage optimization with battery storage: 3D-MILP for electro-thermal performance and semi-empirical aging models}, - author={Kumtepeli, Volkan and Hesse, Holger C and Schimpe, Michael and Tripathi, Anshuman and Wang, Youyi and Jossen, Andreas}, - journal={IEEE Access}, - volume={8}, - pages={204325--204341}, - year={2020}, - publisher={IEEE} -} - -@inproceedings{naumann2017simses, - title={Simses: Software for techno-economic simulation of stationary energy storage systems}, - author={Naumann, Maik and Truong, Cong Nam and Schimpe, Michael and Kucevic, Daniel and Jossen, Andreas and Hesse, Holger C}, - booktitle={International ETG Congress 2017}, - pages={1--6}, - year={2017}, - organization={VDE} -} - -@article{moller2022simses, - title={SimSES: A holistic simulation framework for modeling and analyzing stationary energy storage systems}, - author={M{\"o}ller, Marc and Kucevic, Daniel and Collath, Nils and Parlikar, Anupam and Dotzauer, Petra and Tepe, Benedikt and Englberger, Stefan and Jossen, Andreas and Hesse, Holger}, - journal={Journal of Energy Storage}, - volume={49}, - pages={103743}, - year={2022}, - publisher={Elsevier} -} - -@article{tranter2022liionpack, - title={liionpack: A Python package for simulating packs of batteries with PyBaMM}, - author={Tranter, Thomas and Timms, Robert and Sulzer, Valentin and Planella, Ferran and Wiggins, Gavin and Karra, Suryanarayana and Agarwal, Priyanshu and Chopra, Saransh and Allu, Srikanth and Shearing, Paul and others}, - journal={Journal of Open Source Software}, - volume={7}, - number={70}, - year={2022}, - publisher={The Open Journal} -} - -@article{howey2019tools, - title={Tools for battery health diagnostics and prediction}, - author={Howey, David A}, - journal={The Electrochemical Society Interface}, - volume={28}, - number={1}, - pages={55}, - year={2019}, - publisher={IOP Publishing} -} - -@article{Pearson:2017, - Adsnote = {Provided by the SAO/NASA Astrophysics Data System}, - Adsurl = {http://adsabs.harvard.edu/abs/2017arXiv170304627P}, - Archiveprefix = {arXiv}, - Author = {{Pearson}, S. and {Price-Whelan}, A.~M. and {Johnston}, K.~V.}, - Eprint = {1703.04627}, - Journal = {ArXiv e-prints}, - Keywords = {Astrophysics - Astrophysics of Galaxies}, - Month = mar, - Title = {{Gaps in Globular Cluster Streams: Pal 5 and the Galactic Bar}}, - Year = 2017} - -@book{Binney:2008, - Adsnote = {Provided by the SAO/NASA Astrophysics Data System}, - Adsurl = {http://adsabs.harvard.edu/abs/2008gady.book.....B}, - Author = {{Binney}, J. and {Tremaine}, S.}, - Booktitle = {Galactic Dynamics: Second Edition, by James Binney and Scott Tremaine.~ISBN 978-0-691-13026-2 (HB).~Published by Princeton University Press, Princeton, NJ USA, 2008.}, - Publisher = {Princeton University Press}, - Title = {{Galactic Dynamics: Second Edition}}, - Year = 2008} - -@article{zenodo, - Abstractnote = {

Gala is a Python package for Galactic astronomy and gravitational dynamics. The bulk of the package centers around implementations of gravitational potentials, numerical integration, and nonlinear dynamics.

}, - Author = {Adrian Price-Whelan and Brigitta Sipocz and Syrtis Major and Semyeong Oh}, - Date-Modified = {2017-08-13 14:14:18 +0000}, - Doi = {10.5281/zenodo.833339}, - Month = {Jul}, - Publisher = {Zenodo}, - Title = {adrn/gala: v0.2.1}, - Year = {2017}, - Bdsk-Url-1 = {http://dx.doi.org/10.5281/zenodo.833339}} - -@ARTICLE{gaia, - author = {{Gaia Collaboration} and {Prusti}, T. and {de Bruijne}, J.~H.~J. and - {Brown}, A.~G.~A. and {Vallenari}, A. and {Babusiaux}, C. and - {Bailer-Jones}, C.~A.~L. and {Bastian}, U. and {Biermann}, M. and - {Evans}, D.~W. and et al.}, - title = "{The Gaia mission}", - journal = {\aap}, -archivePrefix = "arXiv", - eprint = {1609.04153}, - primaryClass = "astro-ph.IM", - keywords = {space vehicles: instruments, Galaxy: structure, astrometry, parallaxes, proper motions, telescopes}, - year = 2016, - month = nov, - volume = 595, - eid = {A1}, - pages = {A1}, - doi = {10.1051/0004-6361/201629272}, - adsurl = {http://adsabs.harvard.edu/abs/2016A%26A...595A...1G}, - adsnote = {Provided by the SAO/NASA Astrophysics Data System} -} - -@ARTICLE{astropy, - author = {{Astropy Collaboration} and {Robitaille}, T.~P. and {Tollerud}, E.~J. and - {Greenfield}, P. and {Droettboom}, M. and {Bray}, E. and {Aldcroft}, T. and - {Davis}, M. and {Ginsburg}, A. and {Price-Whelan}, A.~M. and - {Kerzendorf}, W.~E. and {Conley}, A. and {Crighton}, N. and - {Barbary}, K. and {Muna}, D. and {Ferguson}, H. and {Grollier}, F. and - {Parikh}, M.~M. and {Nair}, P.~H. and {Unther}, H.~M. and {Deil}, C. and - {Woillez}, J. and {Conseil}, S. and {Kramer}, R. and {Turner}, J.~E.~H. and - {Singer}, L. and {Fox}, R. and {Weaver}, B.~A. and {Zabalza}, V. and - {Edwards}, Z.~I. and {Azalee Bostroem}, K. and {Burke}, D.~J. and - {Casey}, A.~R. and {Crawford}, S.~M. and {Dencheva}, N. and - {Ely}, J. and {Jenness}, T. and {Labrie}, K. and {Lim}, P.~L. and - {Pierfederici}, F. and {Pontzen}, A. and {Ptak}, A. and {Refsdal}, B. and - {Servillat}, M. and {Streicher}, O.}, - title = "{Astropy: A community Python package for astronomy}", - journal = {\aap}, -archivePrefix = "arXiv", - eprint = {1307.6212}, - primaryClass = "astro-ph.IM", - keywords = {methods: data analysis, methods: miscellaneous, virtual observatory tools}, - year = 2013, - month = oct, - volume = 558, - eid = {A33}, - pages = {A33}, - doi = {10.1051/0004-6361/201322068}, - adsurl = {http://adsabs.harvard.edu/abs/2013A%26A...558A..33A}, - adsnote = {Provided by the SAO/NASA Astrophysics Data System} -} - @article{Aghabozorgi2015, abstract = {Clustering is a solution for classifying enormous data when there is not any early knowledge about classes. With emerging new concepts like cloud computing and big data and their vast applications in recent years, research works have been increased on unsupervised solutions like clustering algorithms to extract knowledge from this avalanche of data. Clustering time-series data has been used in diverse scientific areas to discover patterns which empower data analysts to extract valuable information from complex and massive datasets. In case of huge datasets, using supervised classification solutions is almost impossible, while clustering can solve this problem using un-supervised approaches. In this research work, the focus is on time-series data, which is one of the popular data types in clustering problems and is broadly used from gene expression data in biology to stock market analysis in finance. This review will expose four main components of time-series clustering and is aimed to represent an updated investigation on the trend of improvements in efficiency, quality and complexity of clustering time-series approaches during the last decade and enlighten new paths for future works.}, author = {Saeed Aghabozorgi and Ali Seyed Shirkhorshidi and Teh Ying Wah}, @@ -251,17 +98,6 @@ @article{Rajabi2020 volume = {120}, year = {2020}, } -@misc{Tavenard2020, - abstract = {tslearn is a general-purpose Python machine learning library for time series that offers tools for pre-processing and feature extraction as well as dedicated models for clustering, classification and regression. It follows scikit-learn's Application Programming Interface for transformers and estimators, allowing the use of standard pipelines and model selection tools on top of tslearn objects. It is distributed under the BSD-2-Clause license, and its source code is available at https://github.com/tslearn-team/tslearn.}, - author = {Romain Tavenard and Johann Faouzi and Gilles Vandewiele and Felix Divo and Guillaume Androz and Chester Holtz and Marie Payne and Roman Yurchak and Marc Rußwurm}, - journal = {Journal of Machine Learning Research}, - keywords = {classification,clustering,data mining,pre-processing,time series}, - pages = {1-6}, - title = {Tslearn, A Machine Learning Toolkit for Time Series Data}, - volume = {21}, - url = {https://github.com/tslearn-team/tslearn.}, - year = {2020}, -} @misc{Tavenard2020, abstract = {tslearn is a general-purpose Python machine learning library for time series that offers tools for pre-processing and feature extraction as well as dedicated models for clustering, classification and regression. It follows scikit-learn's Application Programming Interface for transformers and estimators, allowing the use of standard pipelines and model selection tools on top of tslearn objects. It is distributed under the BSD-2-Clause license, and its source code is available at https://github.com/tslearn-team/tslearn.},