A hybrid optimization based energy management between electric vehicle and electricity distribution system

Rajani, B.; Sekhar, D. Chandra

doi:10.1002/2050-7038.12905

Cited by 22 publications

(6 citation statements)

References 71 publications

(89 reference statements)

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“…Cooperative optimal power split method for a cluster of intelligent electric vehicles equipped with battery/ supercapacitor hybrid using modified PSO [143,144] Design strategy for range extended EV An approach for EM control parameters using modified PSO Fuzzy logic [115,[145][146][147][148][149][150][151] Optimized Power Distribution An approach for enhancing overall vehicle performance using fuzzy logic combined neural network…”

Section: Optimization Technique References Application Findingsmentioning

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: Optimization Technique References Application Findingsmentioning

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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“…In recent years, microgrids have been developed as an important part of electrical grids. ese microgrids can also be used for expansion planning [7,8,9]. e capacity of lines and energy resources are less for microgrids when compared to electric power systems.…”

Section: Introductionmentioning

confidence: 99%

Optimized Convolutional Neural Network-Based Capacity Expansion Framework for Electric Vehicle Charging Station

Monikandan¹,

Chellaswamy

Geetha³

et al. 2022

International Transactions on Electrical Energy Systems

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The usage rate of electric vehicles (EVs) is gradually increasing. Recharging of EVs should be carried out repeatedly over time, and the energy needed for this is high and increasing. With the present infrastructure, we cannot supply the required energy, and therefore, we need to implement a model that expands the power grid to satisfy our energy requirements. This paper proposes a convolutional neural network-based dynamic capacity expansion (CNN-DCE) for EV charging. Flower pollination optimization algorithm (FPOA) was used to improve the hyperparameters of CNN during training. The main aim is to reduce the cost of installing additional capacity resources and to reduce the operational cost. To cope with the load growth, different capacity resources are installed at different years of the planning boundary. Five statistical indices, such as mean squared error, mean absolute error, correlation coefficient, and scatter index, are used to evaluate the performance of CNN. The capacity expansion plan in the microgrid is achieved by expanding the energy of battery energy storage systems, microturbines, and solar and wind energy systems. The queuing delay for the EVs waiting in a queue for recharging has been considered. The performance of the proposed CNN-DCE is studied and compared with three other state-of-the-art methods. The results show that the resources reduce the planning cost to 26% for the short-term planning horizon, the long-term plan has 150% of the expansion, and the wind energy system covers 48% of the expansion cost.

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“…However, when analyzing the spatial location of EVs, only the grid loss was considered in Reference 35, not the overall performance of each grid node; moreover, the OPF calculation was highly repeatable and complex. Rajani, et al 36 proposed the firefly algorithm‐Gradient Boosting Decision Tree technology, which takes minimizing system cost and system power loss as the objective function for optimal energy management of the system. The obtained simulation results in Reference 36 show that the energy consumption of the proposed strategy is only 720.34 kJ, which is much lower than the energy consumption of GA and PSO methods.…”

Section: Introductionmentioning

confidence: 99%

“…Rajani, et al 36 proposed the firefly algorithm‐Gradient Boosting Decision Tree technology, which takes minimizing system cost and system power loss as the objective function for optimal energy management of the system. The obtained simulation results in Reference 36 show that the energy consumption of the proposed strategy is only 720.34 kJ, which is much lower than the energy consumption of GA and PSO methods.…”

Section: Introductionmentioning

confidence: 99%

Bi‐level scheduling of large‐scale electric vehicles based on the generation side and the distribution side

Chang

Niu

Jia

2021

Int Trans Electr Energ Syst

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With the intensification of energy shortages and climate warming, how to achieve the coordinated dispatch of electric vehicles (EVs) and high-proportion renewable energy systems (RESs) in the power system has been attracting more and more attentions. From the generation side and the distribution side, this work investigates the problem of large-scale EVs charging and discharging scheduling via the collaborative optimization among thermal power plants, wind and solar power, and EVs. To this end, an energy scheduling system (ESS) including power transmission and distribution is established, based on which a bi-level scheduling strategy is proposed. The upper level coordinately optimizes EVs, thermal power units, wind and solar power generation, and grid base loads to achieve the minimization of power supply costs. By introducing the node loss sensitivity (NLS) and node electricity price (NEP), the lower level allocates EVs to charging nodes to attain the minimization of the grid loss cost and charging cost. Finally, via the IEEE33-node standard power grid and the ESS model, it is verified that the proposed bi-level scheduling strategy can reduce the overall cost (node loss and charging cost) by 28.5%, and the wind curtailment rate and photovoltaic curtailment rate are 0.0% and 0.4%, respectively, making full use of renewable energy and adapting to its uncertainty.

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A hybrid optimization based energy management between electric vehicle and electricity distribution system

Cited by 22 publications

References 71 publications

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

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

Optimized Convolutional Neural Network-Based Capacity Expansion Framework for Electric Vehicle Charging Station

Bi‐level scheduling of large‐scale electric vehicles based on the generation side and the distribution side

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