An improved vehicle to the grid method with battery longevity management in a microgrid application
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DOI: 10.1016/j.energy.2020.117374
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Cited by:
- Muhammad Ahsan Khan & Akhtar Hussain & Woon-Gyu Lee & Hak-Man Kim, 2023. "An Incentive-Based Mechanism to Enhance Energy Trading among Microgrids, EVs, and Grid," Energies, MDPI, vol. 16(17), pages 1-23, September.
- Connor Scott & Mominul Ahsan & Alhussein Albarbar, 2021. "Machine Learning Based Vehicle to Grid Strategy for Improving the Energy Performance of Public Buildings," Sustainability, MDPI, vol. 13(7), pages 1-22, April.
- Faris Adnan Padhilah & Kyeong-Hwa Kim, 2021. "A Centralized Power Flow Control Scheme of EV-Connected DC Microgrid to Satisfy Multi-Objective Problems under Several Constraints," Sustainability, MDPI, vol. 13(16), pages 1-37, August.
- Cheng, Gong & Wang, Xinzhi & He, Yurong, 2021. "Remaining useful life and state of health prediction for lithium batteries based on empirical mode decomposition and a long and short memory neural network," Energy, Elsevier, vol. 232(C).
- Shipman, Rob & Roberts, Rebecca & Waldron, Julie & Naylor, Sophie & Pinchin, James & Rodrigues, Lucelia & Gillott, Mark, 2021. "We got the power: Predicting available capacity for vehicle-to-grid services using a deep recurrent neural network," Energy, Elsevier, vol. 221(C).
- Wu, Wei & Lin, Boqiang, 2021. "Benefits of electric vehicles integrating into power grid," Energy, Elsevier, vol. 224(C).
- Martín Antonio Rodríguez Licea & Francisco Javier Pérez Pinal & Allan Giovanni Soriano Sánchez, 2021. "An Overview on Electric-Stress Degradation Empirical Models for Electrochemical Devices in Smart Grids," Energies, MDPI, vol. 14(8), pages 1-23, April.
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Keywords
Electric vehicles; Deep learning; Frequency control; Microgrid; Vehicle to the grid;All these keywords.
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