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Set-based state of charge estimation for lithium-ion batteries

Matthias Rausch, Reinhardt Klein, Stefan Streif, Christian Pankiewitz, Rolf Findeisen
2014 2014 American Control Conference  
We present a method for set-based estimation of the SOC of lithium-ion batteries.  ...  The performance is compared to an electrochemistry-based observer employing a detailed distributed model for a realistic drive cycle.  ...  The estimator is based on a recently developed set-based feasibility formulation [10] , [11] for estimation and model invalidation.  ... 
doi:10.1109/acc.2014.6858941 dblp:conf/amcc/RauschKSPF14 fatcat:fxytsv4cm5cnfmhli7doxizpcu

Modeling and Simulation of Lithium-Ion Batteries from a Systems Engineering Perspective

Venkatasailanathan Ramadesigan, Paul W. C. Northrop, Sumitava De, Shriram Santhanagopalan, Richard D. Braatz, Venkat R. Subramanian
2012 Journal of the Electrochemical Society  
One of the authors (SS) gratefully acknowledges David Howell, Brian Cunningham, and the U.S. DOE Office of Vehicle Technologies Energy Storage Program for funding and support.  ...  Louis (ICARES), Institute for Advanced Computing Applications and Technologies at University of Illinois, Urbana-Champaign, and the U.S. government.  ...  battery performance in real time.  ... 
doi:10.1149/2.018203jes fatcat:j7b75swsb5gxfmvyd3ci3atwru

Constrained generalized predictive control of battery charging process based on a coupled thermoelectric model

Kailong Liu, Kang Li, Cheng Zhang
2017 Journal of Power Sources  
Battery temperature is a primary factor affecting the battery performance.  ...  This paper proposes a novel battery charging control strategy which applies the constrained generalized predictive control (GPC) to charge a LiFePO4 battery based on a newly developed coupled thermoelectric  ...  can now be estimated and controlled, and real world constraints on the battery operation can all be incorporated.  ... 
doi:10.1016/j.jpowsour.2017.02.039 fatcat:tsa7bmgbkbdl3kpjl6exqgwjle

Current Trends for State-of-Charge (SoC) Estimation in Lithium-Ion Battery Electric Vehicles

Ingvild B. Espedal, Asanthi Jinasena, Odne S. Burheim, Jacob J. Lamb
2021 Energies  
Battery management systems (BMSs) are vital components in ESS systems for Lithium-ion batteries (LIBs). One parameter that is included in the BMS is the state-of-charge (SoC) of the battery.  ...  SoC estimation is a key BMS feature, and precise modeling and state estimation will improve stable operation. This review discusses current methods use in BEV LIB SoC modelling and estimation.  ...  [148] , where an autoregressive long short-term memory network and moving horizon estimation were used for SoC estimation.  ... 
doi:10.3390/en14113284 fatcat:w2kyybjdfjcsdmvgvqikpqzkb4

Boosting Rechargeable Batteries R&D by Multiscale Modeling: Myth or Reality?

Alejandro A. Franco, Alexis Rucci, Daniel Brandell, Christine Frayret, Miran Gaberscek, Piotr Jankowski, Patrik Johansson
2019 Chemical Reviews  
Different kinds of multiscale models are discussed and demystified with a particular emphasis on methodological aspects.  ...  This review addresses concepts, approaches, tools, and outcomes of multiscale modeling used to design and optimize the current and next generation rechargeable battery cells.  ...  A.A.F. acknowledges the European Union's Horizon 2020 research and innovation programme for the funding support through the European Research Council (ERC) (Grant 772873, project "ARTISTIC") and for the  ... 
doi:10.1021/acs.chemrev.8b00239 pmid:30859816 pmcid:PMC6460402 fatcat:l2iubx435zb7ddpmabujgg5r2u

End-of-life or second-life options for retired electric vehicle batteries

Juner Zhu, Ian Mathews, Dongsheng Ren, Wei Li, Daniel Cogswell, Bobin Xing, Tobias Sedlatschek, Sai Nithin R. Kantareddy, Mengchao Yi, Tao Gao, Yong Xia, Qing Zhou (+2 others)
2021 Cell Reports Physical Science  
One is the research experiences of the authors' joint team in battery electrochemical performance and degradation, safety and health, circular economy, and data-driven prognostics.  ...  In this perspective, we evaluate the feasibility of second-life battery applications, from economic and technological perspectives, based on the latest industrial reports and technical publications.  ...  Appropriate sampling algorithms based on historical data and electrochemical test results can help to select proper batteries for safety tests and save testing time and resources.  ... 
doi:10.1016/j.xcrp.2021.100537 fatcat:fmqasp7zvnhr5oa4dzywwksxqe

Non‐Aqueous Electrolytes for Sodium‐Ion Batteries: Challenges and Prospects Towards Commercialization

Hussein Hijazi, Parth Desai, Sathiya Mariyappan
2021 Batteries & Supercaps  
Electrolytes for Na-ion batteries 2.1. Basic principles and designs 2.2. Electrochemical stability of electrolytes through the formation of stable interphases 3.  ...  Analyses on electrochemical performance of the electrolytes 6.3. Analysis of gas evolutions and pressure changes 6.4. Thermal stability of the electrolyte 6.5.  ...  Keywords: Commercial Sodium-ion Battery, SEI, Electrolyte Optimization, Interphase, Electrolyte Additives  ... 
doi:10.1002/batt.202000277 fatcat:ka2zph4p5rhf7jwwont7ek3r3q

Understanding Battery Interfaces by Combined Characterization and Simulation Approaches: Challenges and Perspectives

Duncan Atkins, Elixabete Ayerbe, Anass Benayad, Federico G. Capone, Ennio Capria, Ivano E. Castelli, Isidora Cekic‐Laskovic, Raul Ciria, Lenart Dudy, Kristina Edström, Mark R. Johnson, Hongjiao Li (+10 others)
2021 Advanced Energy Materials  
Acknowledgements The authors acknowledge BATTERY 2030+ funded by the European Union's Horizon 2020 research and innovation programme under Grant Agreement No. 957213.  ...  The authors acknowledge BIG-MAP funded by the European Union's Horizon 2020 research and innovation programme under Grant Agreement No. 957189.  ...  and performance of Li-ion and other battery cells  ... 
doi:10.1002/aenm.202102687 fatcat:w6es3jzierddphl6nirw27737e

Implications of the BATTERY 2030+ AI‐Assisted Toolkit on Future Low‐TRL Battery Discoveries and Chemistries

Arghya Bhowmik, Maitane Berecibar, Montse Casas‐Cabanas, Gabor Csanyi, Robert Dominko, Kersti Hermansson, M. Rosa Palacin, Helge S. Stein, Tejs Vegge
2021 Advanced Energy Materials  
BATTERY 2030+ targets the development of a chemistry neutral platform for accelerating the development of new sustainable high-performance batteries.  ...  The methodological perspectives and challenges in areas like predictive long time-and length-scale simulations of multi-species systems, dynamic processes at battery interfaces, deep learned multi-scaling  ...  Acknowledgements The authors acknowledge the European Union's Horizon 2020 research and innovation program under grant agreements no. 957189 (BIG-MAP) and no. 957213 (BATTERY 2030+).  ... 
doi:10.1002/aenm.202102698 fatcat:muafelf4yrb7licg3lqpbmqmry

Modeling the Solid Electrolyte Interphase: Machine Learning as a Game Changer?

Diddo Diddens, Williams Agyei Appiah, Youssef Mabrouk, Andreas Heuer, Tejs Vegge, Arghya Bhowmik
2022 Advanced Materials Interfaces  
Its properties crucially affect the overall performance and aging of a battery cell.  ...  time and length scales. [1] Although the SEI is most critical to the battery operation, we are far away from being able to model and predict its behavior. [2, 3] At the initial charging process, some of  ...  Acknowledgements The authors acknowledge the European Union's Horizon 2020 Research and Innovation Program under Grant Agreement Nos. 957189 (BIG-MAP) and 957213 (BATTERY2030PLUS).  ... 
doi:10.1002/admi.202101734 fatcat:xfsp6hxhovh77nca6qlncmmimy

A Roadmap for Transforming Research to Invent the Batteries of the Future Designed within the European Large Scale Research Initiative BATTERY 2030+

Julia Amici, Pietro Asinari, Elixabete Ayerbe, Philippe Barboux, Pascale Bayle‐Guillemaud, R. Jürgen Behm, Maitane Berecibar, Erik Berg, Arghya Bhowmik, Silvia Bodoardo, Ivano E. Castelli, Isidora Cekic‐Laskovic (+37 others)
2022 Advanced Energy Materials  
At the core of inventing the batteries of the future lies the discovery of high-performance materials and components that enable the creation of batteries with higher energy and power.  ...  Building on MAP, BATTERY 2030+ proposes to develop the Batteries Interface Genome (BIG) that will establish a new basis for understanding the interfacial processes that govern the operation and functioning  ...  Acknowledgements The authors acknowledge as BATTERY 2030PLUS funded by the European Union's Horizon 2020 research and innovation program under Grant Agreement No. 957213.  ... 
doi:10.1002/aenm.202102785 fatcat:4ex4slbedzgddjlqgfy3g2uw2i

Dynaformer: A Deep Learning Model for Ageing-aware Battery Discharge Prediction [article]

Luca Biggio, Tommaso Bendinelli, Chetan Kulkarni, Olga Fink
2022 arXiv   pre-print
While there are accurate predictive models of the processes underlying the charge and discharge phases of batteries, the modelling of ageing and its effect on performance remains poorly understood.  ...  In this paper, we propose for the first time an approach that can predict the voltage discharge curve for batteries of any degradation level without the need for calibration.  ...  Code Availability We publicly release the implementations of our method and the baselines along with the scripts to visualize the results at the following link.  ... 
arXiv:2206.02555v1 fatcat:5y3xdpjwjjhudita7ymsv35ohe

Toward a Unified Description of Battery Data

Simon Clark, Francesca L. Bleken, Simon Stier, Eibar Flores, Casper Welzel Andersen, Marek Marcinek, Anna Szczesna‐Chrzan, Miran Gaberscek, M. Rosa Palacin, Martin Uhrin, Jesper Friis
2021 Advanced Energy Materials  
and large-scale production of sustainable high-performance batteries is one of the most intensely pursued technical research topics in the world today.  ...  This review summarizes the current state of ontology development, the needs for an ontology in the battery field, and current activities to meet this need.  ...  Acknowledgements The authors acknowledge BATTERY 2030+ funded by the European Union's Horizon 2020 research and innovation programme under Grant Agreement No. 957213 and the project BIG-MAP with funding  ... 
doi:10.1002/aenm.202102702 fatcat:goazyklfyze25jog44m6zu5oxa

Event-Driven Coulomb Counting for Effective Online Approximation of Li-ion Battery State of Charge

Saeed Mian Qaisar
2020 Energies  
Unlike the conventional approach, parameters of battery cells, such as voltages and currents, are no longer regularly measured at a predefined time step and are instead recorded on the basis of events.  ...  This renders a considerable real-time compression.  ...  Acknowledgments: The author is thankful to anonymous reviewers for their valuable feedback. Conflicts of Interest: The author declares no conflict of interest.  ... 
doi:10.3390/en13215600 fatcat:unozqaeryveijlzzazaer5qmuu

Machine Learning Applied to Electrified Vehicle Battery State of Charge and State of Health Estimation: State-of-the-Art

Carlos Vidal, Pawel Malysz, Phillip Kollmeyer, Ali Emadi
2020 IEEE Access  
This paper provides a survey of battery state estimation methods based on ML approaches such as feedforward neural networks (FNNs), recurrent neural networks (RNNs), support vector machines (SVM), radial  ...  Data-driven approaches, such as ML, are becoming more popular for estimating the state of charge (SOC) and state of health (SOH) due to greater availability of battery data and improved computing power  ...  SOH estimation architecture based on battery capacity and resistance estimation using RNN [61] . the SOH of the Li-ion battery based on both the battery capacity fade and increase of its equivalent series  ... 
doi:10.1109/access.2020.2980961 fatcat:sofg7szwqbhdpbjbbwbmdaz2fa
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