Optimal smart contract for autonomous greenhouse environment based on IoT blockchain network in agriculture

Jamil, Faisal; Ibrahim, Muhammad; Ullah, Israr; Kim, Su Yeon; Kahng, Hyun Kook; Kim, Do‐Hyeun

doi:10.1016/j.compag.2021.106573

Cited by 74 publications

(31 citation statements)

References 41 publications

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“…Based on a dual sliding mode observer, Chen et al proposed a method for estimating the state of charge (SOC) and state of health (SOH) together, taking into account the capacity-fading factor [44]. The state of health of lithium-ion batteries was predicted by a backpropagation neural network optimized through genetic algorithms, which was studied for analysis based on appropriate aging mechanisms and the state of health prediction of lithium-ion batteries over their whole lifespan [45][46][47][48][49][50]. Based on deep reinforcement learning, a hybrid battery system comprising a high-power and high-energy battery pack was described.…”

Section: Other Methodsmentioning

confidence: 99%

Lithium-Ion Battery Health Prediction on Hybrid Vehicles Using Machine Learning Approach

2022

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Efforts to decarbonize the world have shown a quick increase in electric vehicles (EVs), limiting increasing pollution. During this electric transportation revolution, lithium-ion batteries (LIBs) play a vital role in storing energy. To determine the range of an electric vehicle (EV), the state of charge and the state of health (SOH) of the battery pack is essential. Access to high-quality data on battery parameters is a crucial challenge for researchers working in the energy storage domain due primarily to confidentiality constraints on manufacturers of batteries and EVs. This paper proposes a hybrid framework for predicting the state of a lithium-ion battery for electric vehicles (EV). Electric vehicles are growing worldwide because of their environmental and sustainability advantages. Batteries are replacing fossil fuels in electric vehicles. In order to prevent failure, Li-ion batteries in electric vehicles should be operated and controlled in a controlled and progressive manner to ensure increased efficiency and safety. An extreme gradient boosting (XGBoost) algorithm is used in this paper to estimate the state of health (SOH) of lithium-ion batteries used in electric vehicles. The model is subjected to error analysis to optimize the battery’s performance parameter. The model undergoes an error analysis to optimize its performance parameters. Furthermore, a state of health (SOH) estimation method based on the extreme gradient boosting algorithm with accuracy correction is proposed here to improve the accuracy of state of health (SOH) estimation for lithium-ion batteries. To describe the aging process of batteries, we extract several features such as average voltages, voltage differences, current differences, and temperature differences. The extreme gradient boosting (XGBoost) model for estimating the state of health (SOH) of lithium-ion batteries is based on the ensemble learning algorithm’s higher prediction accuracy and generalization ability. Experimental results suggest that the boundary gradient lifting algorithm model is capable of more accurate prediction.

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Section: Other Methodsmentioning

confidence: 99%

Lithium-Ion Battery Health Prediction on Hybrid Vehicles Using Machine Learning Approach

2022

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“…In the battery management system, based on a decreasing capacity and an increase in resistance, the battery's state of health has typically been used to quantify battery degradation. Incremental electrochemical capacitance spectroscopy has been compared with differential voltage spectroscopy electrochemical impedance spectroscopy over the same data set [25,26]. Model electrochemical is complex because it has a complicated identification process; thus, identifying parameters relies on identifiability analysis.…”

Section: Overview Lithium-ion Batteries With Electrochemical Model An...mentioning

confidence: 99%

Lithium-Ion Battery Estimation in Online Framework Using Extreme Gradient Boosting Machine Learning Approach

et al. 2022

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The battery management system in an electric vehicle must be reliable and durable to forecast the state of charge. Considering that battery degradation is generally nonlinear, state of charge (SOC) estimation with lower degradation can be challenging. Lithium-ion batteries are highly dependent on the knowledge of aging, which is usually costly or not available online. In this paper, we suggest the state of charge estimation of lithium-ion battery systems by using an extreme gradient boosting algorithm for electric vehicles application, which acquires the nonlinear relationship model can with offline training. The extreme gradient boosting algorithm is the tree on based learning, which effectively performs and speeds. Voltage-time data used as an input of this system from the partial constant current phase; the proposed algorithm improves the accuracy of predicting the relevant. Additionally, no initial state of charge is required in our proposed method; thus, estimating the state of charge can consider each battery state.

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“…The popularity of cryptocurrency is based on the peer-to-peer network design, transaction cost, and ungoverned nature type [6,7]. These aspects cause increases in the of volume trading, the price of exchange, and volatility, which become key roles in media.…”

Section: Concept Of Cryptocurrencymentioning

confidence: 99%

Knowledge Discovery on Cryptocurrency Exchange Rate Prediction Using Machine Learning Pipelines

Shahbazi

Byun

2022

Sensors

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The popularity of cryptocurrency in recent years has gained a lot of attention among researchers and in academic working areas. The uncontrollable and untraceable nature of cryptocurrency offers a lot of attractions to the people in this domain. The nature of the financial market is non-linear and disordered, which makes the prediction of exchange rates a challenging and difficult task. Predicting the price of cryptocurrency is based on the previous price inflations in research. Various machine learning algorithms have been applied to predict the digital coins’ exchange rate, but in this study, we present the exchange rate of cryptocurrency based on applying the machine learning XGBoost algorithm and blockchain framework for the security and transparency of the proposed system. In this system, data mining techniques are applied for qualified data analysis. The applied machine learning algorithm is XGBoost, which performs the highest prediction output, after accuracy measurement performance. The prediction process is designed by using various filters and coefficient weights. The cross-validation method was applied for the phase of training to improve the performance of the system.

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Optimal smart contract for autonomous greenhouse environment based on IoT blockchain network in agriculture

Cited by 74 publications

References 41 publications

Lithium-Ion Battery Health Prediction on Hybrid Vehicles Using Machine Learning Approach

Lithium-Ion Battery Health Prediction on Hybrid Vehicles Using Machine Learning Approach

Lithium-Ion Battery Estimation in Online Framework Using Extreme Gradient Boosting Machine Learning Approach

Knowledge Discovery on Cryptocurrency Exchange Rate Prediction Using Machine Learning Pipelines

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