Risk Measures for Particle-Filtering-Based State-of-Charge Prognosis in Lithium-Ion Batteries

Marcos E.  Orchard; Pablo Alejandro Hevia Koch; Bin  Zhang; Liang  Tang

doi:10.1109/TIE.2012.2224079

Risk Measures for Particle-Filtering-Based State-of-Charge Prognosis in Lithium-Ion Batteries

Marcos E. Orchard, Pablo Alejandro Hevia Koch, Bin Zhang, Liang Tang

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Abstract

This paper presents a class of risk measures to be used as damage indicators within particle filtering (PF)-based real-time prognosis algorithms, with application to the case of state-of-charge prediction in lithium-ion batteries. The proposed risk measure not only incorporates the risk of battery failure but
also is a measure for the confidence on the prognosis algorithm itself. In addition, a novel simplified PF-based prognostic method is proposed to estimate the battery discharge time, while providing a computationally inexpensive solution. Computing times for both the novel prognosis routine and the associated risk measure are fast enough to allow their implementation in real-time applications, such as decision-making systems or path-planning algorithms.

Original language	English
Journal	I E E E Transactions on Industrial Electronics
Volume	60
Issue number	11
Pages (from-to)	5260-5269
ISSN	0278-0046
DOIs	https://doi.org/10.1109/TIE.2012.2224079
Publication status	Published - 2013
Externally published	Yes

Keywords

Lithium-ion (Li-ion) battery
Risk management
State-of-charge (SoC) prognosis

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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@article{3342dd162a8e43e1b62dd6a8a2518d6b,

title = "Risk Measures for Particle-Filtering-Based State-of-Charge Prognosis in Lithium-Ion Batteries",

abstract = "This paper presents a class of risk measures to be used as damage indicators within particle filtering (PF)-based real-time prognosis algorithms, with application to the case of state-of-charge prediction in lithium-ion batteries. The proposed risk measure not only incorporates the risk of battery failure butalso is a measure for the confidence on the prognosis algorithm itself. In addition, a novel simplified PF-based prognostic method is proposed to estimate the battery discharge time, while providing a computationally inexpensive solution. Computing times for both the novel prognosis routine and the associated risk measure are fast enough to allow their implementation in real-time applications, such as decision-making systems or path-planning algorithms.",

keywords = "Lithium-ion (Li-ion) battery, Risk management, State-of-charge (SoC) prognosis",

author = "Orchard, {Marcos E.} and {Hevia Koch}, {Pablo Alejandro} and Bin Zhang and Liang Tang",

year = "2013",

doi = "10.1109/TIE.2012.2224079",

language = "English",

volume = "60",

pages = "5260--5269",

journal = "I E E E Transactions on Industrial Electronics",

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publisher = "IEEE Industrial Electronics Society",

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T1 - Risk Measures for Particle-Filtering-Based State-of-Charge Prognosis in Lithium-Ion Batteries

AU - Orchard, Marcos E.

AU - Hevia Koch, Pablo Alejandro

AU - Zhang, Bin

AU - Tang, Liang

PY - 2013

Y1 - 2013

N2 - This paper presents a class of risk measures to be used as damage indicators within particle filtering (PF)-based real-time prognosis algorithms, with application to the case of state-of-charge prediction in lithium-ion batteries. The proposed risk measure not only incorporates the risk of battery failure butalso is a measure for the confidence on the prognosis algorithm itself. In addition, a novel simplified PF-based prognostic method is proposed to estimate the battery discharge time, while providing a computationally inexpensive solution. Computing times for both the novel prognosis routine and the associated risk measure are fast enough to allow their implementation in real-time applications, such as decision-making systems or path-planning algorithms.

AB - This paper presents a class of risk measures to be used as damage indicators within particle filtering (PF)-based real-time prognosis algorithms, with application to the case of state-of-charge prediction in lithium-ion batteries. The proposed risk measure not only incorporates the risk of battery failure butalso is a measure for the confidence on the prognosis algorithm itself. In addition, a novel simplified PF-based prognostic method is proposed to estimate the battery discharge time, while providing a computationally inexpensive solution. Computing times for both the novel prognosis routine and the associated risk measure are fast enough to allow their implementation in real-time applications, such as decision-making systems or path-planning algorithms.

KW - Lithium-ion (Li-ion) battery

KW - Risk management

KW - State-of-charge (SoC) prognosis

U2 - 10.1109/TIE.2012.2224079

DO - 10.1109/TIE.2012.2224079

M3 - Journal article

SN - 0278-0046

VL - 60

SP - 5260

EP - 5269

JO - I E E E Transactions on Industrial Electronics

JF - I E E E Transactions on Industrial Electronics

IS - 11

ER -

Risk Measures for Particle-Filtering-Based State-of-Charge Prognosis in Lithium-Ion Batteries

Abstract

Keywords

UN SDGs

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