A KPI System for small sample sizes based on the Bayesian estimation of Cpkin the production of Lithium-ion batteries

Nan Yang; Thomas Kornas; Rüdigger Daub

doi:10.1016/j.procir.2021.03.111

A KPI System for small sample sizes based on the Bayesian estimation of C_pkin the production of Lithium-ion batteries

Nan Yang, Thomas Kornas, Rüdigger Daub

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Research output: Contribution to journal › Conference article › peer-review

1 Scopus citations

Abstract

The production of lithium-ion batteries (LIBs) is characterized by the high level of complexity and uncertainty, thus a key performance indicator (KPI) system is needed to monitor and control this process. However, up to this point, the existing KPI system based on process capability indices (PCIs) is only applicable to large amounts of production data. Therefore, the prototype production stage at the beginning of ramp-up still needs to be considered separately, as there is not enough production data available to estimate the PCIs via traditional approaches during this period. This paper builds a KPI system for the prototype production stage based on the Bayesian estimation of and the method of adding dummy samples. At the end of this paper, an example is provided as an application result of the KPI system.

Original language	English
Pages (from-to)	526-530
Number of pages	5
Journal	Procedia CIRP
Volume	99
DOIs	https://doi.org/10.1016/j.procir.2021.03.111
State	Published - 2021
Externally published	Yes
Event	14th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME 2020 - Naples, Italy Duration: 15 Jul 2020 → 17 Jul 2020

Keywords

Bayesian estimation
C
Dummy sample
KPI system
Lithium-ion battery
Process capability
Small sample sizes

UN SDGs

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

Access to Document

10.1016/j.procir.2021.03.111

Cite this

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title = "A KPI System for small sample sizes based on the Bayesian estimation of Cpkin the production of Lithium-ion batteries",

abstract = "The production of lithium-ion batteries (LIBs) is characterized by the high level of complexity and uncertainty, thus a key performance indicator (KPI) system is needed to monitor and control this process. However, up to this point, the existing KPI system based on process capability indices (PCIs) is only applicable to large amounts of production data. Therefore, the prototype production stage at the beginning of ramp-up still needs to be considered separately, as there is not enough production data available to estimate the PCIs via traditional approaches during this period. This paper builds a KPI system for the prototype production stage based on the Bayesian estimation of and the method of adding dummy samples. At the end of this paper, an example is provided as an application result of the KPI system.",

keywords = "Bayesian estimation, C, Dummy sample, KPI system, Lithium-ion battery, Process capability, Small sample sizes",

author = "Nan Yang and Thomas Kornas and R{\"u}digger Daub",

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AU - Kornas, Thomas

AU - Daub, Rüdigger

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Y1 - 2021

N2 - The production of lithium-ion batteries (LIBs) is characterized by the high level of complexity and uncertainty, thus a key performance indicator (KPI) system is needed to monitor and control this process. However, up to this point, the existing KPI system based on process capability indices (PCIs) is only applicable to large amounts of production data. Therefore, the prototype production stage at the beginning of ramp-up still needs to be considered separately, as there is not enough production data available to estimate the PCIs via traditional approaches during this period. This paper builds a KPI system for the prototype production stage based on the Bayesian estimation of and the method of adding dummy samples. At the end of this paper, an example is provided as an application result of the KPI system.

AB - The production of lithium-ion batteries (LIBs) is characterized by the high level of complexity and uncertainty, thus a key performance indicator (KPI) system is needed to monitor and control this process. However, up to this point, the existing KPI system based on process capability indices (PCIs) is only applicable to large amounts of production data. Therefore, the prototype production stage at the beginning of ramp-up still needs to be considered separately, as there is not enough production data available to estimate the PCIs via traditional approaches during this period. This paper builds a KPI system for the prototype production stage based on the Bayesian estimation of and the method of adding dummy samples. At the end of this paper, an example is provided as an application result of the KPI system.

KW - Bayesian estimation

KW - C

KW - Dummy sample

KW - KPI system

KW - Lithium-ion battery

KW - Process capability

KW - Small sample sizes

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