A reduced order electrolyte enhanced single particle lithium ion cell model for hybrid vehicle applications

2014 ◽  
Author(s):  
Tanvir R. Tanim ◽  
Christopher D. Rahn ◽  
Chao-Yang Wang
Keyword(s):  
Single Particle ◽  
Cell Model ◽  
Lithium Ion ◽  
Hybrid Vehicle ◽  
Reduced Order ◽  
Lithium Ion Cell

A Computational Framework for Lithium Ion Cell-Level Model Predictive Control Using a Physics-Based Reduced-Order Model

2021 ◽  
Vol 5 (4) ◽  
pp. 1387-1392
Author(s):  
Marcelo A. Xavier ◽  
Aloisio K. de Souza ◽  
Kiana Karami ◽  
Gregory L. Plett ◽  
M. Scott Trimboli
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Lithium Ion ◽  
Reduced Order Model ◽  
Order Model ◽  
Cell Level ◽  
Computational Framework ◽  
Reduced Order ◽  
Lithium Ion Cell ◽  
Level Model

A Temperature Dependent, Single Particle, Lithium Ion Cell Model Including Electrolyte Diffusion

2014 ◽  
Vol 137 (1) ◽  
Author(s):  
Tanvir R. Tanim ◽  
Christopher D. Rahn ◽  
Chao-Yang Wang
Keyword(s):  
Single Particle ◽  
Lithium Ion ◽  
Particle Model ◽  
Voltage Response ◽  
Model Parameters ◽  
Battery Management System ◽  
Temperature Dependent ◽  
Lithium Ion Cell ◽  
Pulse Charge ◽  
Wide Range

Low-order, explicit models of lithium ion cells are critical for real-time battery management system (BMS) applications. This paper presents a seventh-order, electrolyte enhanced single particle model (ESPM) with electrolyte diffusion and temperature dependent parameters (ESPM-T). The impedance transfer function coefficients are explicit in terms of the model parameters, simplifying the implementation of temperature dependence. The ESPM-T model is compared with a commercially available finite volume based model and results show accurate matching of pulse responses over a wide range of temperature (T) and C-rates (I). The voltage response to 30 s pulse charge–discharge current inputs is within 5% of the commercial code for 25 °C<T<50 °C at I≤12.5C and -10 °C<T<50°C at I≤1C for a graphite/nickel cobalt manganese (NCM) lithium ion cell.

Extended Physics-Based Reduced-Order Capacity Fade Model for Lithium Ion Battery Cells

2021 ◽  
pp. 1-12
Author(s):  
Zachary Salyer ◽  
Matilde D'Arpino ◽  
Marcello Canova
Keyword(s):  
Lithium Ion Battery ◽  
Cell Model ◽  
Active Material ◽  
Lithium Ion ◽  
Calibration Procedure ◽  
Layer Growth ◽  
Operating Conditions ◽  
Capacity Fade ◽  
Computationally Efficient ◽  
Reduced Order

Abstract Aging models are necessary to accurately predict the SOH evolution in lithium ion battery systems when performing durability studies under realistic operatings, specifically considering time-varying storage, cycling, and environmental conditions, while being computationally efficient. This paper extends existing physics-based reduced-order capacity fade models that predict degradation resulting from the solid electrolyte interface (SEI) layer growth and loss of active material (LAM) in the graphite anode. Specifically, the physics of the degradation mechanisms and aging campaigns for various cell chemistries are reviewed to improve the model fidelity. Additionally, a new calibration procedure is established relying solely on capacity fade data and results are presented including extrapolation/validation for multiple chemistries. Finally, a condition is integrated to predict the onset of lithium plating. This allows the complete cell model to predict the incremental degradation under various operating conditions, including fast charging.

An explicit algebraic reduced order algorithm for lithium ion cell voltage prediction

2014 ◽  
Vol 248 ◽  
pp. 383-387 ◽  
Author(s):  
V. Senthil Kumar ◽  
Priya Gambhire ◽  
Krishnan S. Hariharan ◽  
Ashish Khandelwal ◽  
Subramanya Mayya Kolake ◽  
...  
Keyword(s):  
Lithium Ion ◽  
Cell Voltage ◽  
Reduced Order ◽  
Lithium Ion Cell ◽  
Order Algorithm
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