Design and Analysis of Generic Energy Management Strategy for Controlling Second-Life Battery Systems in Stationary Applications

Abdel-Monem, Mohamed; Hegazy, Omar; Omar, Noshin; Trad, Khiem; Breucker, Sven De; Bossche, Peter Van den; Mierlo, Joeri Van

doi:10.3390/en9110889

Cited by 19 publications

(11 citation statements)

References 21 publications

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“…This strategy implies that there is no need to build a new BMS controlling different types or aged cells, as happens when following other strategies [28]. Moreover, as the use of one EV battery pack is enough to provide the power that the system demands, there is no need to develop an energy management system that balances power and capacity among multiple battery packs as reported by other studies [29,30]. Unfortunately, the direct reuse strategy needs to incorporate a gateway to communicate with the power system [31].…”

Section: Methodsmentioning

confidence: 91%

Second-Life Batteries on a Gas Turbine Power Plant to Provide Area Regulation Services

Casals

García

2017

Batteries

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Batteries are used in the electricity grid to provide ancillary services. Area regulation seems to provide substantial revenues and profit, but Li-ion batteries are still too expensive to enter widely into this market. On the other hand, electric vehicle (EV) batteries are considered inappropriate for traction purposes when they reach a state of health (SoH) of 80%. The reuse of these batteries offers affordable batteries for second-life stationary applications. This study analyzes two possible scenarios where batteries may give power and energy support to a gas turbine cogeneration power plant, and how long these batteries may last under different loads.

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Section: Methodsmentioning

confidence: 91%

Second-Life Batteries on a Gas Turbine Power Plant to Provide Area Regulation Services

Casals

García

2017

Batteries

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“…Pre-processing and normalization, enhance the convergence rate and minimize the undesirable effect of raw data. Here, the F I G U R E 9 A, Constant current discharging curve of B0005; B, Constant current discharging time of B0005 normalization range of input and output will be from 0 to 1, and the normalized value will be calculated by Equation (12).…”

Section: Pre-processing Of Datasetsmentioning

confidence: 99%

“…Among these three classifications, lithium-ion (Li-ion) batteries that come under secondary batteries, are considered to be the key battery technologies used in EVs. 12 Due to several characteristics of Li-ion batteries such as high energy density, high power density, a wide range of discharging time, high energy efficiency, low daily selfdischarge, long life cycles, smaller size, and lightweight, vehicle manufacturers give preference to these batteries. [13][14][15] The existence of a battery's second life is only when the capacity of a new battery used in an EV reaches up to 80% of its initial capacity.…”

Section: Introductionmentioning

confidence: 99%

Machinelearning‐basedapproach for useful capacity prediction ofsecond‐lifebatteries employing appropriate input selection

Bhatt

Ongsakul²,

Madhu

et al. 2021

Intl J of Energy Research

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Electric vehicle-discarded second-life batteries still contain 80% of usable capacity and can serve as a low-cost alternative for microgrid storage applications where the battery storage capacity is flexible against transport applications. By accurately predicting the remaining useful capacity or state of health of these batteries, using the data from their first life operation, their costeffectiveness for microgrid energy management can be analyzed. For this purpose, three machine learning models are proposed here. The input parameters for the models are selected from the charging and discharging profiles of batteries, considering both the aging and regeneration phenomenon. Eight different input cases with and without K-fold (K = 10) cross-validation are used for training the proposed models. Based on the comparative analysis it is found that all the models trained with K-fold cross-validation, show minimum error as compared to without K-fold. The forecasting results from multiple approaches showed that the long short term memory model trained with battery discharging profile outperformed other models, quantified by multiple error indices, including root mean square error (0.009147), mean absolute error (0.005841), and R 2 (0.9713). The robustness of the model is validated with multiple battery datasets. Further, the study illustrates the importance of activation functions, in machine learning models used for forecasting.

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“…Due to the potential of obtaining higher specific energy and energy density, the adoption of Li-ion batteries is expected to grow fast in EVs, particularly in PHEVs and BEVs. Therefore, this research study focused on Li-ion batteries [41].…”

Section: V2g and G2v Systems For Light-duty (Ld) Vehiclesmentioning

confidence: 99%

Modelling, Analysis and Performance Evaluation of Power Conversion Unit in G2V/V2G Application—A Review

et al. 2018

Self Cite

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In response to climate change, which is caused by the increasing pollution of the environment and leads to the deterioration of human health, future electricity generations should reduce reliance on fossil fuels by growing the use of clean and renewable energy generation sources and by using clean vehicle technologies. Battery storage systems have been recognized as one of the most promising approaches for supporting the renewable energy generation sources and cleanly powering vehicles instead of burning gasoline and diesel fuel. However, the cost of batteries is still a prominent barrier for their use in stationary and traction applications. As a rule, the cost of batteries can be decreased by lowering material costs, enhancing process efficiencies, and increasing production volume. Another more effective solution is called Vehicle-to-grid (V2G) application. In V2G application, the battery system can be used to support the grid services, whereas the battery is still in the vehicle. To make a battery system economically viable for V2G/G2V applications, an effective power-electronics converter should be selected as well. This converter should be supported by an advanced control strategy. Therefore, this article provides a detailed technical assessment and review of V2G/G2V concepts, in conjunction with various power-electronics converter topologies. In this paper, modeling and detailed control strategies are fully designed and investigated in terms of dynamic response and harmonics. Furthermore, an extensive design and analysis of charging systems for low-duty/high-duty vehicles are also presented.

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Design and Analysis of Generic Energy Management Strategy for Controlling Second-Life Battery Systems in Stationary Applications

Cited by 19 publications

References 21 publications

Second-Life Batteries on a Gas Turbine Power Plant to Provide Area Regulation Services

Second-Life Batteries on a Gas Turbine Power Plant to Provide Area Regulation Services

Machinelearning‐basedapproach for useful capacity prediction ofsecond‐lifebatteries employing appropriate input selection

Modelling, Analysis and Performance Evaluation of Power Conversion Unit in G2V/V2G Application—A Review

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