Comparison of reduced order lithium-ion battery models for control applications

Speltino, C.; Domênico, Daniela Di; Fiengo, Giovanni; Stefanopoulou, Anna G.

doi:10.1109/cdc.2009.5400816

Cited by 44 publications

(30 citation statements)

References 10 publications

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“…For example, many applications only need model accuracy with low-to-medium charge/discharge currents. In this case, techniques such as the electrode averaging method [88] or volume-averaging technique [89] can be used to produce a fast chemistrybased model. Speltino et al also made several simplifications such as neglecting solid concentration distribution and assuming constant electrolyte concentration.…”

Section: Electrochemical Modelsmentioning

confidence: 99%

“…Speltino et al also made several simplifications such as neglecting solid concentration distribution and assuming constant electrolyte concentration. As a result, 13 their model simulated quickly, but with a heavy loss of information [88,90]. An interesting application of porous electrode theory to EIS is in the papers of Sikha and White [55,56] where they develop analytical expressions for the impedance spectrum of a Li-ion cell.…”

Section: Electrochemical Modelsmentioning

confidence: 99%

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A survey of mathematics-based equivalent-circuit and electrochemical battery models for hybrid and electric vehicle simulation

Seaman

Dao

McPhee

2014

Journal of Power Sources

360

159

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In this paper, we survey two kinds of mathematics-based battery models intended for use in hybrid and electric vehicle simulation. The first is circuit-based, which is founded upon the electrical behaviour of the battery, and abstracts away the electrochemistry into equivalent electrical components. The second is chemistry-based, which is founded upon the electrochemical equations of the battery chemistry.

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Section: Electrochemical Modelsmentioning

confidence: 99%

Section: Electrochemical Modelsmentioning

confidence: 99%

A survey of mathematics-based equivalent-circuit and electrochemical battery models for hybrid and electric vehicle simulation

Seaman

Dao

McPhee

2014

Journal of Power Sources

360

159

View full text Add to dashboard Cite

show abstract

“…The creation of models to support the design and evaluation of Li-ion batteries has been the subject of considerable research [23,[28][29][30][31]. Mathematical models vary widely in complexity, computational requirements and reliability of prediction.…”

Section: Model Structurementioning

confidence: 99%

A Cell-in-the-Loop Approach to Systems Modelling and Simulation of Energy Storage Systems

et al. 2015

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This research is aligned with the engineering challenge of scaling-up individual battery cells into a complete energy storage system (ESS). Manufacturing tolerances, coupled with thermal gradients and the differential electrical loading of adjacent cells, can result in significant variations in the rate of cell degradation, energy distribution and ESS performance. The uncertain transition from cell to system often manifests itself in over-engineered, non-optimal ESS designs within both the transport and energy sectors. To alleviate these issues, the authors propose a novel model-based framework for cell-in-the-loop simulation (CILS) in which a physical cell may be integrated within a complete model of an ESS and exercised against realistic electrical and thermal loads in real-time. This paper focuses on the electrical integration of both real and simulated cells within the CILS test environment. Validation of the CILS approach using real-world electric vehicle data is presented for an 18650 cell. The cell is integrated within a real-time simulation model of a series string of similar cells in a 4sp1 configuration. Results are presented that highlight the impact of cell variability (i.e., capacity and impedance) on the energy available from the multi-cell system and the useable capacity of the physical cell.

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“…2) Electrochemical Models: Electrochemical battery models also called physical models (e.g., [102], [103]), are usually the most accurate ones. This is because the battery behaviour and its important parameters are modelled at the lowest level by taking into consideration the particular characteristics of each battery type being modelled, as well as the chemical, physical and thermodynamic processes taking place inside the battery.…”

Section: ) Empirical Modelsmentioning

confidence: 99%

Energy-Aware Mobile Learning:Opportunities and Challenges

Moldovan

Weibelzahl

Muntean

2014

IEEE Commun. Surv. Tutorials

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Abstract-As mobile devices are becoming more powerful and affordable they are increasingly used for mobile learning activities. By enabling learners' access to educational content anywhere and anytime, mobile learning has both the potential to provide online learners with new opportunities, and to reach less privileged categories of learners that lack access to traditional e-learning services. Among the many challenges with mobile learning, the battery-powered nature of mobile devices and in particular their limited battery life, stands out as one issue that can significantly limit learners' access to educational content while on the move. Adaptation and personalisation solutions have widely been considered for overcoming the differences between learners and between the characteristics of their mobile devices. However, while various energy saving solutions have been proposed in order to provide mobile users with extended device usage time, the areas of adaptive mobile learning and energy conservation in wireless communications failed to meet under the same umbrella. This paper bridges the two areas by presenting an overview of adaptive mobile learning systems as well as how these can be extended to make them energy-aware. Furthermore, the paper surveys various approaches for energy measurement, modelling and adaptation, three major aspects that have to be considered in order to deploy energy-aware mobile learning systems. Discussions on the applicability and limitations of these approaches for mobile learning are also provided.

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Comparison of reduced order lithium-ion battery models for control applications

Cited by 44 publications

References 10 publications

A survey of mathematics-based equivalent-circuit and electrochemical battery models for hybrid and electric vehicle simulation

A survey of mathematics-based equivalent-circuit and electrochemical battery models for hybrid and electric vehicle simulation

A Cell-in-the-Loop Approach to Systems Modelling and Simulation of Energy Storage Systems

Energy-Aware Mobile Learning:Opportunities and Challenges

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