Research Article
Prediction of the Remaining Useful Life of Lithium-Ion Battery Using Multilayer Perceptron
Issue:
Volume 10, Issue 1, December 2024
Pages:
1-17
Received:
5 September 2024
Accepted:
23 September 2024
Published:
10 October 2024
Abstract: Cogitating the reliability of the supply and ensuring continuous delivery of power to the loads, especially in the growing demand for Lithium-Ion batteries in electric vehicle applications, prediction of the remaining useful life of Lithium-Ion batteries is crucial for the timely replacement. For prediction of non-linear and chaotic relationship, experience-based approach, physics-based approach and data driven approach are used among which data driven approach is a model free, accurate and reliable approach. Therefore, a driven approach in predicting remaining useful life can be implemented in the battery management system. This research uses a multilayer perceptron to predict the remaining useful life of the battery. The NASA Ames Prognostics Center of Excellence (PCoE) battery dataset is used to test the proposed methodology. The use of multilayer perceptron for remaining life prediction seems promising despite the significant number of jump points, gaps in data and a small quantity of experimental data in the National Aeronautics and Space Administration (NASA) dataset. The predicted result was obtained with 8.52 % mean absolute error and 9.59 % root mean square error. When compared with the predicted results of different literatures, proposed multilayer perceptron with sliding window approach outperforms most of the existing approach. Incorporation of optimization techniques and hybrid algorithm in proposed approach can further enhance the accuracy of the model.
Abstract: Cogitating the reliability of the supply and ensuring continuous delivery of power to the loads, especially in the growing demand for Lithium-Ion batteries in electric vehicle applications, prediction of the remaining useful life of Lithium-Ion batteries is crucial for the timely replacement. For prediction of non-linear and chaotic relationship, e...
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Research Article
Optimal Sizing and Placement of Distributed Generators and Capacitors in Nepal's Sankhu Feeder Using the Water Cycle Algorithm
Yam Krishna Poudel*
Issue:
Volume 10, Issue 1, December 2024
Pages:
18-32
Received:
25 September 2024
Accepted:
22 October 2024
Published:
22 November 2024
Abstract: Minimizing power loss and improving voltage stability are crucial aspects of power systems, driven by transmission line contingencies, financial losses for utilities, and potential power system blackouts. Optimal allocation comprising the sizing and operating power factor—of Distributed Generation (DG) units and capacitor banks (CBs) significantly enhances power system efficiency. Efforts by power system operators and researchers focus on addressing issues related to power loss, energy loss, voltage profiles, and voltage stability through the strategic placement of DGs and CBs. Additionally, optimal DG and CB allocation protects the distribution system from unforeseen events and enables operators to run the system in islanding mode when necessary. The integration of DG units and CBs in distribution systems aims to enhance overall system performance. This research paper introduces a Water Cycle Algorithm (WCA) for the optimal placement and sizing of DGs and CBs. The proposed method targets both technical and economic benefits, considering multiple objective functions: minimizing power losses, reducing voltage deviation, lowering total electrical energy costs, and improving the voltage stability index. The WCA emulates the natural water cycle, from streams to rivers and rivers to the sea. Five different operational scenarios are evaluated to test the performance of this methodology. Simulations are conducted on distribution systems: the IEEE 69-bus test system and the Sankhu feeder network, a real system. The results demonstrate the superior performance of the proposed WCA compared to other optimization algorithms. The findings highlight the WCA's flexibility, efficiency, and significant improvements in economic benefits, establishing it as a promising approach for optimizing the placement of DG and CB in distribution systems.
Abstract: Minimizing power loss and improving voltage stability are crucial aspects of power systems, driven by transmission line contingencies, financial losses for utilities, and potential power system blackouts. Optimal allocation comprising the sizing and operating power factor—of Distributed Generation (DG) units and capacitor banks (CBs) significantly ...
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