System-level management of rechargeable lithium-ion batteries

Stefanopoulou, Anna G.; Kim, Yejoong

doi:10.1016/b978-1-78242-090-3.00010-9

Cited by 11 publications

(6 citation statements)

References 62 publications

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“…CC measures the total amount of current supplied to or drawn from the battery over time [22]. The equation to calculate the state of charge (SOC) using CC can be expressed as:…”

Section: A Coulomb Countingmentioning

confidence: 99%

Negative Results in Estimating State of Charge for Lithium Iron Phosphate Batteries with Extended Kalman Filter

Hamid,

Xie

2023

Preprint

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“…CC measures the total amount of current supplied to or drawn from the battery over time [22]. The equation to calculate the state of charge (SOC) using CC can be expressed as:…”

Section: A Coulomb Countingmentioning

confidence: 99%

Negative Results in Estimating State of Charge for Lithium Iron Phosphate Batteries with Extended Kalman Filter

Hamid,

Xie

2023

Preprint

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“…The Coulomb method estimates SoC based on the of integration of the battery current time-dependent function when charging/discharging the battery [33,34]. By considering that the energy consumption is measured in kilowatt hours, SoC is calculated as…”

Section: Acknowledgmentsmentioning

confidence: 99%

Battery Storage at the Secondary Distribution Electricity Grid by Investigating End-Users Load Demand Measurements

Theocharis

Hamanee

2022

Energies

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Energy storage systems is expected to be utilized to cover the increased electrification of energy demands and to alleviate the electrical energy production from intermittent energy sources such as solar and wind. Aggregated and distributed battery energy storage systems may improve electricity grids operability and security by providing smart energy management options and efficient resources allocation. In this paper, battery storage at the secondary distribution level is explored. The investigation is based on the end-user energy demand behavior. As such, the electrical energy consumption patterns are measured and analyzed in a residential area. Measurements were collected and analyzed in order to record the customers’ behaviors aiming to reveal their differences and similarities. Following this, aggregated and distributed battery energy storage systems are computed based on the features of the measured electrical power consumption patterns aiming to estimate the factors that could potentially incentivize the installation of a battery system either as aggregated at the low voltage transformer side or as distributed system at the load side. The parameters that affect the economic viability of the system are qualitatively evaluated with regard to the profitability of the system.

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“…This control technique involves, in particular, state machine control, threshold control and power monitoring to ensure that the key components function in the most efficient environment [35]. This technique is beneficial because of its simplicity of execution and the reliability of temperature control [36]. This type of control can be classified according to flexible objectives, namely, energy reduction, energy cost reduction, load shifting, and provision of ancillary services [37].…”

Section: Rule-based Controlmentioning

confidence: 99%

Sustainability matchmaking: Linking renewable sources to electric water heating through machine learning

Mabina

Mukoma

Booysen

2021

Energy and Buildings

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A high penetration of renewable energy sources such as wind power generation and photovoltaic generation causes some problems in power systems such as the duck curve and unreliability due to environmental variability. An effective solution to this problem is Demand Response (DR). Electric Water Heaters (EWHs) are considered ideal candidates for DR due to their energy storage capability.Due to the benefits, control strategies or techniques for EWHs have received considerable academic attention. The energy sector has recently tapped into the disruptive artificial intelligence world to learn, among other related priorities, how to enhance operations, maintain energy resilience and improve consumer service. Consequently, this paper reviews the use of machine learning (ML) for optimization and scheduling of EWHs. The main contributions of this review paper are, firstly, to identify state of the art of energy optimization and scheduling of EWHs. Secondly, to review the current ML models for energy optimization and scheduling of EWHs in smart grids and smart building environment. While classical control strategies may deliver substantial improvements, optimum efficiency may not be reached. ML has demonstrated clear advantages over classical control. Based on these conclusions, recommendations for further research topics are drawn.

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System-level management of rechargeable lithium-ion batteries

Cited by 11 publications

References 62 publications

Negative Results in Estimating State of Charge for Lithium Iron Phosphate Batteries with Extended Kalman Filter

Negative Results in Estimating State of Charge for Lithium Iron Phosphate Batteries with Extended Kalman Filter

Battery Storage at the Secondary Distribution Electricity Grid by Investigating End-Users Load Demand Measurements

Sustainability matchmaking: Linking renewable sources to electric water heating through machine learning

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