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| 001 | 1016980 | ||
| 005 | 20240709081918.0 | ||
| 037 | _ | _ | |a FZJ-2023-03887 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Ali, Haider Adel |0 P:(DE-Juel1)190784 |b 0 |e Corresponding author |
| 111 | 2 | _ | |a Advanced battery power conference |c Aachen |d 2023-04-27 - 2023-04-28 |w Germany |
| 245 | _ | _ | |a A comparison between physics-based Li-ion battery models |
| 260 | _ | _ | |c 2023 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
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| 520 | _ | _ | |a Physics-based electrochemical battery models are widely used as powerful tools for simulating lithium-ion battery behavior and for providing an understanding of the internal physical and electrochemical processes. However, due to their complexity and high computational demand, these models may not be feasible for battery management systems (BMS) and long-term aging simulations. Models with reduced order, such as the Extended Single Particle Model (ESPM), Single Particle Model (SPM), and Polynomial and Padé approximations, calculating Fick's 2nd law, improve calculation speed. However, choosing the appropriate simplification approach for a particular cell type and operating condition can be challenging. This study provides insights into the simulation accuracy and calculation speed of various reduced-order models for high-energy (HE) and high-power (HP) batteries at various C-rates. Results are compared to the DFN model. |
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| 700 | 1 | _ | |a Raijmakers, Luc |0 P:(DE-Juel1)176196 |b 1 |
| 700 | 1 | _ | |a Danilov, Dmitri |0 P:(DE-Juel1)173719 |b 2 |
| 700 | 1 | _ | |a Notten, Peter H. L. |0 P:(DE-Juel1)165918 |b 3 |
| 700 | 1 | _ | |a Eichel, Rüdiger-A. |0 P:(DE-Juel1)156123 |b 4 |
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