Model-based predictive diagnostics for electrochemical energy sources

Kozlowski, J.D.; Byington, Carl S.; Garga, Amulya; Watson, Matthew J.; Hay, Todd A.

doi:10.1109/aero.2001.931332

Cited by 21 publications

(13 citation statements)

References 7 publications

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“…The paper [11] predicts the RUL of a battery by model-based prediction approach. The health of a battery can be indicated by a term named State-of-charge (SOC), which is defined as the ratio between the remaining capacity and the initial or rated capacity.…”

Section: A Battery State-of-charge Predictionmentioning

confidence: 99%

“…x f x u a (11) where x is the state variable vector, a is a vector representing some system's parameters, and u is the system input vector. A meaningful variable, termed damage variable is selected from the state-space model (10) to describe the health of the system.…”

Section: Model-based Fault Prognosis Of Wmrsmentioning

confidence: 99%

“…Thus, a damage measure is defined such that 1 implies system failure. Suppose the system model in the form (11) or (12) is available and can be transferred to the model that is related to base on physical properties of the system. Estimation algorithms are then applied to track the damage variable for predicting the system's RUL.…”

Section: Model-based Fault Prognosis Of Wmrsmentioning

confidence: 99%

“…The system model could range from a simple 1 st order model to a complex nth order state-space model. For instance, in [15], a simple state-space model (11) with the damage variable increasing in a constant rate is used for predicting the system's RUL. a (12) where is a damage measure under a constant excitation.…”

Section: Model-based Fault Prognosis Of Wmrsmentioning

confidence: 99%

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Model-based fault diagnosis/prognosis for wheeled mobile robots: a review

Luo

Wang

Pham

et al. 2005

31st Annual Conference of IEEE Industrial Electronics Society, 2005. IECON 2005.

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This paper presents a survey on model-based fault diagnostics and prognostics for wheeled mobile robots (WMRs). Numerous cases of faults diagnosis and prognosis solutions show that, for a complex machine like a WMR, model-based methods that utilize analytical models of the system are desirable because analytical models can well represent the nominal behaviours of the system. On the other hand, datadriven methods are not practical for WMRs since they require a lot of failure cases and data that are too costly, if possible at all, to obtain. Fault prognosis for a system in general and for WMRs in particular is still at the preliminary stage. System models can also be used to predict the remaining useful life of the system.

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Section: A Battery State-of-charge Predictionmentioning

confidence: 99%

Section: Model-based Fault Prognosis Of Wmrsmentioning

confidence: 99%

Section: Model-based Fault Prognosis Of Wmrsmentioning

confidence: 99%

Section: Model-based Fault Prognosis Of Wmrsmentioning

confidence: 99%

See 2 more Smart Citations

Model-based fault diagnosis/prognosis for wheeled mobile robots: a review

Luo

Wang

Pham

et al. 2005

31st Annual Conference of IEEE Industrial Electronics Society, 2005. IECON 2005.

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“…Model-based predictive diagnostics for electrochemical energy sources are discussed in [9] and [10].…”

Section: Introductionmentioning

confidence: 99%

Fuel-Cell Parameter Estimation and Diagnostics

Forrai

Funato

Yanagita

et al. 2005

IEEE Trans. On Energy Conversion

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In the future, a major role of fuel cells in combined heat and power generation systems is envisaged. It is well known that a fuel cell's efficiency is highly dependent on the operating conditions, such as temperature, humidity, and air flow. In the aim to assure optimal operating conditions as well as to minimize the losses, parameter identification, diagnostics, and control are going to play an important role. The paper deals with parameter identification and diagnostics of a low-power proton-exchange membrane fuel cell considering a system identification approach. This multi-input multi-output electrochemical system can be tested or monitored by identifying its parameters, which has real importance in the manufacturing, exploitation, and maintenance phase. Therefore, parameter identification represents the core of any diagnostics or online monitoring procedure. Furthermore, online parameter estimation might be used for control purposes to maintain the fuel cell in an optimal operating point with minimal losses. The proposed parameter identification method is simple, cost-effective, and can be extended easily for fuel-cell stacks too.

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