Research on the prediction of the state-of-health of starting batteries of diesel engines

Tung Chou Lin; Liang Ruei Chen

doi:10.18494/SAM.2018.1825

Research on the prediction of the state-of-health of starting batteries of diesel engines

Tung Chou Lin, Liang Ruei Chen

Department of Electrical Engineering

Research output: Contribution to journal › Article

Abstract

A study of state-of-health (SOH) prediction for diesel engine generator starting batteries is presented, the main purpose of which is to analyze the battery's state using the relationship between AC impedance and voltage. A sensing system capable of detecting battery open-circuit voltage and discharge current has been designed. The respective AC impedances at 100 and 50% battery SOH were obtained from a monitoring system, and linear regression was used to predict battery SOH. A new GS GTX7A-12B lead acid battery was used in the experiments and the results showed that there was a 11.265% difference between the actual battery SOH and that predicted by linear regression.

Original language	English
Pages (from-to)	551-558
Number of pages	8
Journal	Sensors and Materials
Volume	30
Issue number	3
DOIs	https://doi.org/10.18494/SAM.2018.1825
Publication status	Published - 2018 Jan 1

All Science Journal Classification (ASJC) codes

Instrumentation
Materials Science(all)

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10.18494/SAM.2018.1825

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Lin, T. C., & Chen, L. R. (2018). Research on the prediction of the state-of-health of starting batteries of diesel engines. Sensors and Materials, 30(3), 551-558. https://doi.org/10.18494/SAM.2018.1825

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title = "Research on the prediction of the state-of-health of starting batteries of diesel engines",

abstract = "A study of state-of-health (SOH) prediction for diesel engine generator starting batteries is presented, the main purpose of which is to analyze the battery's state using the relationship between AC impedance and voltage. A sensing system capable of detecting battery open-circuit voltage and discharge current has been designed. The respective AC impedances at 100 and 50% battery SOH were obtained from a monitoring system, and linear regression was used to predict battery SOH. A new GS GTX7A-12B lead acid battery was used in the experiments and the results showed that there was a 11.265% difference between the actual battery SOH and that predicted by linear regression.",

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