Model-Free Non-Invasive Health Assessment for Battery Energy Storage Assets

Sobon, Joanna; Stephen, Bruce

doi:10.1109/access.2021.3070574

Cited by 6 publications

(11 citation statements)

References 83 publications

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“…It is the annual cost of the operation and maintenance that is slotted for a particular project. The cost is given in USD for the working year from the inception of the plant/system [37]. The use of the USD would safeguard against the inflation of the local currencies.…”

Section: Total Operations and Maintenance Cost (Tomc)mentioning

confidence: 99%

“…Total Fuel Cost is a feature that would look at the cost of operation of the fuel for the project the fuels can be fossil-based fuels and gases [37]. This means that working operation costs of the fuel is considered for the monetary terms i.e., cost of fuel.…”

Section: Total Fuel Cost (Tfc)mentioning

confidence: 99%

“…Total electrical production is the peak value of the power produced for the grid system that can be converted to useful power [37]. The total electrical production can be estimated for a plant or grid-based system.…”

Section: Total Electrical Production (Tep)mentioning

confidence: 99%

“…Because of the growing relevance of batteries, researchers have been working hard to develop highly efficient and cost-effective storage devices. The method would require taking non-invasive measurements of the battery in real-time and analyzing the results [3].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

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Analyzing Optimal Battery Sizing in Microgrids Based on the Feature Selection and Machine Learning Approaches

Khan¹,

Nizami²,

Qaisar³

et al. 2022

Preprint

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Microgrids are becoming popular nowadays because they provide clean, efficient, and low-cost energy. To use the stored energy in times of emergency or peak loads, microgrids require bulk storage capacity. Since microgrids are the future of renewable energy, the energy storage technology employed should be optimized to generate electricity. Batteries play a variety of essential roles in daily life and are used at peak hours and during a time of emergency. There are different types of batteries i.e., lion batteries, lead-acid batteries, etc. Optimal battery sizing of microgrids is a challenging problem, that limits modern technologies such as electric vehicles, etc. It is important to know different battery features such as battery life, battery throughput, and battery autonomy to get optimal battery sizing for microgrids. Mixed-integer linear programming (MILP) is an established technique for the integration and optimization of different energy sources and parameters for optimal battery sizing. A new MILP based dataset is introduced in this work. Support vector machine (SVM) is the machine learning application used to estimate the optimum battery size. The impact of feature selection algorithms on the proposed machine learning-based model is evaluated. The performance of the six best-performing feature selection algorithms is analyzed. The experimental results show that the feature selection algorithms improve the performance of the proposed methodology. Ranker search shows the best performance with a Spearman’s rank-ordered correlation constant of 0.9756, linear correlation constant of 0.9452, Kendall correlation constant of 0.8488 and root mean squared error of 0.0525.

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Section: Total Operations and Maintenance Cost (Tomc)mentioning

confidence: 99%

Section: Total Fuel Cost (Tfc)mentioning

confidence: 99%

Section: Total Electrical Production (Tep)mentioning

confidence: 99%

Section: Introduction 1backgroundmentioning

confidence: 99%

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Analyzing Optimal Battery Sizing in Microgrids Based on the Feature Selection and Machine Learning Approaches

Khan¹,

Nizami²,

Qaisar³

et al. 2022

Preprint

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show abstract

“…In order to reduce or eliminate these problems, the battery management system plays a critical role. In the battery management system [ 4 ], the accuracy of battery state of charge (SOC) estimation is an important indicator to measure battery performance. SOC is one of the critical parameters of lithium-ion batteries.…”

Section: Introductionmentioning

confidence: 99%

SOC Estimation of Lithium-Ion Battery for Electric Vehicle Based on Deep Multilayer Perceptron

Li¹,

Jiang²,

Guo

et al. 2022

Computational Intelligence and Neuroscience

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The state of charge (SOC) is one of the main indexes of the lithium-ion battery, which affects the practice range of new energy vehicles and the safety of the battery. Nevertheless, the value of SOC cannot be measured directly. At present, the algorithm for estimating the state of charge is not very satisfactory. The multilayer perceptron algorithm designed during this paper encompasses a sensible impact on state estimation. During this paper, the multilayer network is designed to estimate the charged state of lithium batteries from the three-layer artificial neural network to the eleven-layer artificial neural network. After preprocessing the dataset and comparing several activation functions, the ten-layer fully connected neural network is the most efficient to estimate the SOC. In order to prevent over-fitting of the multilayer perceptron algorithm, the two techniques of the BatchNormalization layer and Dropout layer work together to inhibit over-fitting. At the same time, the accuracy of extended Kalman filter, long and short memory network, and recurrent neural network are compared. The multilayer perceptron network designed during this paper has the highest accuracy. Finally, in the open dataset, both the training and test errors achieve good results. The algorithm developed in this paper has made some progress in SOC estimation.

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Improved NaS Battery State of Charge and State of Health Estimation: A Novel Integration of Temporal Fusion Transformer, Isolation Forest, and Support Vector Regression

Almarzooqi,

Alhusin,

Nikolakakos

et al. 2024

IEEE Trans. on Ind. Applicat.

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Model-Free Non-Invasive Health Assessment for Battery Energy Storage Assets

Cited by 6 publications

References 83 publications

Analyzing Optimal Battery Sizing in Microgrids Based on the Feature Selection and Machine Learning Approaches

Analyzing Optimal Battery Sizing in Microgrids Based on the Feature Selection and Machine Learning Approaches

SOC Estimation of Lithium-Ion Battery for Electric Vehicle Based on Deep Multilayer Perceptron

Improved NaS Battery State of Charge and State of Health Estimation: A Novel Integration of Temporal Fusion Transformer, Isolation Forest, and Support Vector Regression

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