Energy management optimization in smart railway stations with the ability to charge plug-in hybrid electric vehicles

K., B.; G., S.M. Mousavi

doi:10.1016/j.est.2023.107867

Cited by 1 publication

(1 citation statement)

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“…Besides, utilization of convex formulations or solving problems with convex optimization have been explored although rarely used for predictive purposes. Some applications are not closely related to solving or designing an EMS problem for PHEVs, e.g., charging PHEVs via grid consumption reduction [38] using metaheuristic and convex programming for proposing an intelligent railway station. Accurately forecasting electric vehicle charging station (EVCS) power is challenging due to the unpredictable nature of charging behaviors [39].…”

Section: Recent Advances and Formulations Of Ems For Phevsmentioning

confidence: 99%

Machine Learning and Optimization in Energy Management Systems for Plug-In Hybrid Electric Vehicles: A Comprehensive Review

Recalde,

Cajo,

Velasquez

et al. 2024

Energies

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This paper provides a comprehensive review of machine learning strategies and optimization formulations employed in energy management systems (EMS) tailored for plug-in hybrid electric vehicles (PHEVs). EMS stands as a pivotal component facilitating optimized power distribution, predictive and adaptive control strategies, component health monitoring, and energy harvesting, thereby enabling the maximal exploitation of resources through optimal operation. Recent advancements have introduced innovative solutions such as Model Predictive Control (MPC), machine learning-based techniques, real-time optimization algorithms, hybrid optimization approaches, and the integration of fuzzy logic with neural networks, significantly enhancing the efficiency and performance of EMS. Additionally, multi-objective optimization, stochastic and robust optimization methods, and emerging quantum computing approaches are pushing the boundaries of EMS capabilities. Remarkable advancements have been made in data-driven modeling, decision-making, and real-time adjustments, propelling machine learning and optimization to the forefront of enhanced control systems for vehicular applications. However, despite these strides, there remain unexplored research avenues and challenges awaiting investigation. This review synthesizes existing knowledge, identifies gaps, and underscores the importance of continued inquiry to address unanswered research questions, thereby propelling the field toward further advancements in PHEV EMS design and implementation.

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Section: Recent Advances and Formulations Of Ems For Phevsmentioning

confidence: 99%