Modelling of Lithium-ion Battery Ageing for a Local Energy Community

Badigenchala, Raviteja; Naware, Dipanshu; Mitra, Arghya

doi:10.1109/conit51480.2021.9498346

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…Badigenchala et al [44] utilized a Long Short-Term Memory Neural Network (LSTM) to forecast PV generation and load demand. The goal was to model battery aging while also considering ambient temperature, state-of-charge (SOC), and C-rate to minimize the degradation cost of the battery.…”

Section: Neural Network-based Forecasting Methodsmentioning

confidence: 99%

“…Nonlinear models such as neural networks presented in several works in the literature [27,34,44,52,58,59] are data-driven approaches that can perform nonlinear mappings in data patterns. They are popular due to the fact that they are known as universal function approximators and could fit well to data obtained in different applications and scenarios.…”

Section: Strengths and Weaknesses Of Forecasting Modelsmentioning

confidence: 99%

“…For systems using solar energy and BESSs, performing both solar forecasting and load forecasting plays a crucial role. Approaches focused on planning with the aim to provide an optimal system operation, which may involve a smart grid, can benefit from the aforementioned forecasts [44]. It is important to mention that any percentage of prediction errors may increase the chance of wrong battery usage policy, which may degrade battery lifetime [34].…”

Section: Rq2mentioning

confidence: 99%

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Forecasting Methods for Photovoltaic Energy in the Scenario of Battery Energy Storage Systems: A Comprehensive Review

de Oliveira,

de Mattos Neto,

Siqueira

et al. 2023

Energies

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The worldwide appeal has increased for the development of new technologies that allow the use of green energy. In this category, photovoltaic energy (PV) stands out, especially with regard to the presentation of forecasting methods of solar irradiance or solar power from photovoltaic generators. The development of battery energy storage systems (BESSs) has been investigated to overcome difficulties in electric grid operation, such as using energy in the peaks of load or economic dispatch. These technologies are often applied in the sense that solar irradiance is used to charge the battery. We present a review of solar forecasting methods used together with a PV-BESS. Despite the hundreds of papers investigating solar irradiation forecasting, only a few present discussions on its use on the PV-BESS set. Therefore, we evaluated 49 papers from scientific databases published over the last six years. We performed a quantitative analysis and reported important aspects found in the papers, such as the error metrics addressed, granularity, and where the data are obtained from. We also describe applications of the BESS, present a critical analysis of the current perspectives, and point out promising future research directions on forecasting approaches in conjunction with PV-BESS.

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Section: Neural Network-based Forecasting Methodsmentioning

confidence: 99%