Intelligent monitoring system satisfies customer needs for continuous monitoring and assurance on VRLA batteries

Deshpande, Shriprasad; Shaffer, David Williamson; Szymborski, J.; Barling, L.O.; Hawkins, John

doi:10.1109/intlec.1999.794126

Cited by 8 publications

(4 citation statements)

References 6 publications

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“…Under ideal conditions, the trend analysis of certain battery parameters, especially temperature, impedance, capacity, and SOH, would be an excellent tool to observe how the batteries degrade over time, and when a decision can be made to replace them. However, as mentioned above, easy access to all of these parameters is not always available [65,66,67].…”

Section: Supervision and Predictive Fault Diagnosis Of Batteriesmentioning

confidence: 99%

Marine NMEA 2000 Smart Sensors for Ship Batteries Supervision and Predictive Fault Diagnosis

García

Quiles

Correcher

et al. 2019

Sensors

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In this paper, an application for the management, supervision and failure forecast of a ship’s energy storage system is developed through a National Marine Electronics Association (NMEA) 2000 smart sensor network. Here, the NMEA 2000 network sensor devices for the measurement and supervision of the parameters inherent to energy storage and energy supply are reviewed. The importance of energy storage systems in ships, the causes and models of battery aging, types of failures, and predictive diagnosis techniques for valve-regulated lead-acid (VRLA) batteries used for assisted and safe navigation are discussed. In ships, battery banks are installed in chambers that normally do not have temperature regulation and therefore are significantly conditioned by the outside temperature. A specific method based on the analysis of the time-series data of random and seasonal factors is proposed for the comparative trend analyses of both the battery internal temperature and the battery installation chamber temperature. The objective is to apply predictive fault diagnosis to detect any undesirable increase in battery temperature using prior indicators of heat dissipation process failure—to avoid the development of the most frequent and dangerous failure modes of VRLA batteries such as dry out and thermal runaway. It is concluded that these failure modes can be conveniently diagnosed by easily recognized patterns, obtained by performing comparative trend analyses to the variables measured onboard by NMEA sensors.

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Section: Supervision and Predictive Fault Diagnosis Of Batteriesmentioning

confidence: 99%

Marine NMEA 2000 Smart Sensors for Ship Batteries Supervision and Predictive Fault Diagnosis

García

Quiles

Correcher

et al. 2019

Sensors

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“…However, it must be said that the interpretive and diagnostic ability of commercially available devices varies widely. The recent trend has been to consider the integration of the operational status of the power conversion and distribution components as well as the energy storage components into a "whole-of-system" functional monitoring [1][2][3][4]. The scope of interaction of power system components is potentially broad, and the complexity of functional monitoring depends on the degree of system information and control required by the application.…”

Section: New Control Conceptmentioning

confidence: 99%

“…Monitoring technology with sufficient interpretive and diagnostic ability is now available [2,11,12]. Moreover, the generic software characteristics and constructs to support knowledge-based processing for flexible and adaptive control algorithms have already been proposed [5].…”

Section: Intermittent Chargingmentioning

confidence: 99%

Some advances in control for telecommunications DC power systems

Robbins¹,

Hawkins²

2001

Twenty-Third International Telecommunications Energy Conference. INTELEC 2001

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This paper discusses a new control concept that uses the information contained in monitored data to modify telecommunications DC power system operation in a beneficial manner. The application of this concept to common functions of low-voltage battery disconnection, battery condition testing, and recharge current limiting is presented. The control concept can also be applied to the less commonly used intermittent charging technique.

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“…In a perfect world, the trend analysis of some battery parameters, especially temperature, impedance, capacity, and their relationship with SOH, would be a great tool for determining when it is time to replace the batteries and how the batteries degrade over time. However, as was already mentioned, not all of these parameters can be easily assessed [ 73 , 74 , 75 ].…”

Section: Introductionmentioning

confidence: 99%

Distributed Intelligent Battery Management System Using a Real-World Cloud Computing System

García

Quiles

Correcher

2023

Sensors

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In this work, a decentralized but synchronized real-world system for smart battery management was designed by using a general controller with cloud computing capability, four charge regulators, and a set of sensorized battery monitors with networking and Bluetooth capabilities. Currently, for real-world applications, battery management systems (BMSs) can be used in the form of distributed control systems where general controllers, charge regulators, and smart monitors and sensors are integrated, such as those proposed in this work, which allow more precise estimations of a large set of important parameters, such as the state of charge (SOC), state of health (SOH), current, voltage, and temperature, seeking the safety and the extension of the useful life of energy storage systems based on battery banks. The system used is a paradigmatic real-world example of the so-called intelligent battery management systems. One of the contributions made in this work is the realization of a distributed design of a BMS, which adds the benefit of increased system security compared to a fully centralized BMS structure. Another research contribution made in this work is the development of a methodical modeling procedure based on Petri Nets, which establishes, in a visible, organized, and precise way, the set of conditions that will determine the operation of the BMS. If this modeling is not carried out, the threshold values and their conditions remain scattered, not very transparent, and difficult to deal with in an aggregate way.

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Intelligent monitoring system satisfies customer needs for continuous monitoring and assurance on VRLA batteries

Cited by 8 publications

References 6 publications

Marine NMEA 2000 Smart Sensors for Ship Batteries Supervision and Predictive Fault Diagnosis

Marine NMEA 2000 Smart Sensors for Ship Batteries Supervision and Predictive Fault Diagnosis

Some advances in control for telecommunications DC power systems

Distributed Intelligent Battery Management System Using a Real-World Cloud Computing System

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