A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Filters
Set-based state of charge estimation for lithium-ion batteries
2014
2014 American Control Conference
Preserved Fulltext
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
2012
Journal of the Electrochemical Society
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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
2017
Journal of Power Sources
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
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
2021
Energies
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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?
2019
Chemical Reviews
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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
2021
Cell Reports Physical Science
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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
2021
Batteries & Supercaps
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
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
2021
Advanced Energy Materials
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
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
2021
Advanced Energy Materials
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
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?
2022
Advanced Materials Interfaces
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
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+
2022
Advanced Energy Materials
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
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]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
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
2021
Advanced Energy Materials
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
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
2020
Energies
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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
2020
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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
« Previous
Showing results 1 — 15 out of 444 results