Solar technologies for electricity production: An updated review

Aqachmar, Zineb; Sassi, Hicham Ben; Lahrech, Khadija; Barhdadi, Abdelfettah

doi:10.1016/j.ijhydene.2021.06.190

Cited by 30 publications

(10 citation statements)

References 128 publications

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“…The solar-to-electricity conversion can be achieved via three main technologies including thermal, photovoltaic, and hybrid thermal photovoltaic, all of which have been well established and can be directly employed. 317,318…”

Section: Solar Energy Utilizationmentioning

confidence: 99%

Thermo-photo catalysis: a whole greater than the sum of its parts

Fang

2022

Chem. Soc. Rev.

146

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Section: Solar Energy Utilizationmentioning

confidence: 99%

Thermo-photo catalysis: a whole greater than the sum of its parts

Fang

2022

Chem. Soc. Rev.

146

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“…During the tests, the battery has been isolated from any external thermal effect to ensure the reliability of the collected data, then the charge and discharge processes are applied according to a predefined C-rate (denoted as C), which is governing measurement of the battery charge/ discharge current with respect to its rated capacity. In Figure 7A-1, the constant current-constant voltage (CC-CV) charging profile with 1 3 C is adopted until 90% SOC, afterwards, the battery is completely discharged with a wavered current of 2 3 C. The second profile (Figure 7B-1) charges the battery until 97% SOC and discharges it by applying a pulse current of 2C. Regarding the temperature, Figure 7A-3,B-3 depict the measured surface temperature of the battery at room temperature.…”

Section: Data Processingmentioning

confidence: 99%

“…Due to the impact of petroleum‐based fuel on the environment, several governments are conducting numerous Reforms to several sectors in an attempt to reduce their fuel consumption. These sectors include the energy sector, by adopting a new form of energy; solar 1 and wind, 2 as well as the transportation sector. The electrification of the powertrain using electric vehicle (EV) technology, 3 is the key toward lower CO2 emissions and higher energy efficiency.…”

Section: Introductionmentioning

confidence: 99%

State of charge estimation of an electric vehicle's battery using tiny neural network embedded on small microcontroller units

Mazzi

Sassi

Gaga

et al. 2022

Intl J of Energy Research

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Summary The latest breakthroughs in artificial intelligence resulted in their adoption in the electric vehicle's battery management systems. Simultaneously, the remarkable increase in the number of embedded systems in electric vehicles (EVs) has led to the search for new optimized state of charge (SOC) estimation strategies suitable for implementation on small and limited MCUs. In this regard, this paper provides a design and quantization processes of the 1D convolutional neural network (1D CNN) and the GRU‐recurrent neural network (GRU‐RNN), devoted for SOC estimation. Both the NN algorithms are designed and trained using data collected from an 18650 Li‐ion battery. Furthermore, to account for the limited computational resources of the EV MCUs, the pre‐trained models are then converted into highly optimized C‐codes using the latest model quantification techniques. In this regard, this paper investigates, the performances of the STM32Cube.AI and the TensorFlow Lite for microcontroller (TFLite Micro), in quantizing the activation functions and weights of the proposed NN models. This is achieved by converting these latter from 32‐bits floating‐point to 8‐bit integer precision. The obtained experimental results, under different profiles, indicate that the 1D CNN is more accurate than the GRU‐based model with root mean squared error (RMSE) of 2.33% and mean absolute error (MAE) of 1.62%. Furthermore, the impact of the quantization using STM32Cube.AI is compared with that using the TFLite Micro. The obtained results, demonstrated the superiority of the 1D CNN model quantized using the STM32Cube.AI. This latter reduces the flash memory size of the 1D CNN model from 10.82 to 2.89 KB and the RAM size from 2.49 to 1.04 KB, compared to the TFLite Micro, which reduces the RAM size from 7.0107 to 4.23 KB, and the flash from 43.361 to 15.88 KB.

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“…In recent decades, solar energy has been regarded as the most promising source of inexhaustible clean energy. The two main ways of collecting and transforming solar energy are photovoltaic solar energy and solar thermal energy, [ 2 ] of which the latter is the basis of this work. In solar thermal energy, solar radiation is collected by an absorption material and transferred as heat energy to a transfer medium, usually a heat transfer fluid (HTF).…”

Section: Introductionmentioning

confidence: 99%

Experimental Characterization and Statistical Analysis of Water‐Based Gold Nanofluids for Solar Applications: Optical Properties and Photothermal Conversion Efficiency

et al. 2022

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Optimizing optical and photothermal properties of the solar nanofluids (NFs) to be used in direct absorption solar collectors is a key issue to maximize efficiencies in these collector types. This work both experimentally and statistically analyzes the influence of the size (5 and 20 nm) and concentration (5.1, 28.2, and 51.3 ppm) of gold nanoparticles (NPs) on several important variables for collector performance: NF stability, extinction coefficient, photothermal efficiency, etc. The research work shows that the addition of small amounts of gold NPs, whose surface plasmon resonance has wavelengths close to 520 nm, greatly improves the light absorption capacity of the base fluid (water). The statistical analysis confirms the influence of NP size and concentration on photothermal conversion efficiency (PTE), which lead to an increase of up to 121% for the smallest‐sized NF with the highest concentration.

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Solar technologies for electricity production: An updated review

Cited by 30 publications

References 128 publications

Thermo-photo catalysis: a whole greater than the sum of its parts

Thermo-photo catalysis: a whole greater than the sum of its parts

State of charge estimation of an electric vehicle's battery using tiny neural network embedded on small microcontroller units

Experimental Characterization and Statistical Analysis of Water‐Based Gold Nanofluids for Solar Applications: Optical Properties and Photothermal Conversion Efficiency

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