Auxiliary health diagnosis method for lead-acid battery

Sun, Yuhua; Jou, Hurng‐Liahng; Wu, Jinn‐Chang; Wu, Kuen-Der

doi:10.1016/j.apenergy.2010.04.013

Cited by 21 publications

(6 citation statements)

References 23 publications

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“…The research on fault diagnosis is based on parameter estimation, state estimation, experience, and other methods [5]. Previous research on fault diagnosis mainly focuses on the capacity estimation of single cells and battery packs [6][7][8][9][10][11], internal resistance prediction [12], the power fading fault mechanism of single cell [13], etc. Gregory J.…”

Section: Introductionmentioning

confidence: 99%

Online Diagnosis for the Capacity Fade Fault of a Parallel-Connected Lithium Ion Battery Group

et al. 2016

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In a parallel-connected battery group (PCBG), capacity degradation is usually caused by the inconsistency between a faulty cell and other normal cells, and the inconsistency occurs due to two potential causes: an aging inconsistency fault or a loose contacting fault. In this paper, a novel method is proposed to perform online and real-time capacity fault diagnosis for PCBGs. Firstly, based on the analysis of parameter variation characteristics of a PCBG with different fault causes, it is found that PCBG resistance can be taken as an indicator for both seeking the faulty PCBG and distinguishing the fault causes. On one hand, the faulty PCBG can be identified by comparing the PCBG resistance among PCBGs; on the other hand, two fault causes can be distinguished by comparing the variance of the PCBG resistances. Furthermore, for online applications, a novel recursive-least-squares algorithm with restricted memory and constraint (RLSRMC), in which the constraint is added to eliminate the "imaginary number" phenomena of parameters, is developed and used in PCBG resistance identification. Lastly, fault simulation and validation results demonstrate that the proposed methods have good accuracy and reliability.

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Section: Introductionmentioning

confidence: 99%

Online Diagnosis for the Capacity Fade Fault of a Parallel-Connected Lithium Ion Battery Group

et al. 2016

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“…The approximation entropy method is described in [80]. This method is an extension of the sample entropy technique used to estimate the SoH for lead-acid batteries and has a better estimation performance.…”

Section: Other Soh/rul Estimation Methodsmentioning

confidence: 99%

Battery state of health estimation: a structured review of models, methods and commercial devices

Ungurean

Cârstoiu

Micea

et al. 2016

Int. J. Energy Res.

200

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Summary Estimating the dynamic status parameters of a battery, such as its state of health (SoH) and remaining useful life (RUL), is still a very difficult and complex task. In this paper we perform a structured review of the most relevant state of the art models, algorithms and commercial devices employed in the estimation of the SoH/RUL battery performance figures, in the context of embedded applications. The models and estimation techniques are thoroughly classified and, for each taxonomy class, a presentation of the working principles is made. A comprehensive set of metrics is then introduced for the evaluation of the SoH/RUL estimation techniques from the perspective of their implementation and operation efficiency in embedded systems. These algorithms are then analyzed and discussed in a comparative manner, with concrete figures and results. The capability and the performance of the different types of off‐the‐shelf fuel gauges to estimate the battery SoH/RUL parameters are also evaluated in this paper. Copyright © 2016 John Wiley & Sons, Ltd.

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“…For a BMS, state-of-charge (SoC) and state-of-health (SoH) are two important parameters indicative of battery health condition; thus, accurately estimating them becomes a paramount task in a BMS development [2]. Tremendous works have been done to improve performance of the BMS technique [3]- [10]. Since most of batteries failure modes involve very complicated internal electrochemical reactions, accurate modeling and analysis of specific battery failure mode is extremely challenging.…”

Section: Introductionmentioning

confidence: 99%

An internal state variable mapping approach for Li-Plating diagnosis

Bai

Wang

2016

Journal of Power Sources

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Li-ion battery failure becomes one of major challenges for reliable battery applications, as it could cause catastrophic consequences. Compared with capacity fading resulted from calendar effects, Li-plating induced battery failures are more difficult to identify, as they causes sudden capacity loss leaving limited time for failure diagnosis. This paper presents a new internal state variable (ISV) mapping approach to identify values of immeasurable battery ISVs considering changes of inherent parameters of battery system dynamics for Li-plating diagnosis. Employing the developed ISV mapping approach, an explicit functional relationship model between measurable battery signals and immeasurable battery ISVs can be developed. The developed model can then be used to identify ISVs from an online battery system for the occurrence identification of Li-plating. Employing multiphysics based simulation of Li-plating using COMSOL, the proposed Li-plating diagnosis approach is implemented under different conditions in the case studies to demonstrate its efficacy in diagnosis of Liplating onset timings.

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Auxiliary health diagnosis method for lead-acid battery

Cited by 21 publications

References 23 publications

Online Diagnosis for the Capacity Fade Fault of a Parallel-Connected Lithium Ion Battery Group

Online Diagnosis for the Capacity Fade Fault of a Parallel-Connected Lithium Ion Battery Group

Battery state of health estimation: a structured review of models, methods and commercial devices

An internal state variable mapping approach for Li-Plating diagnosis

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