Efficient Power Management Algorithm Based on Fuzzy Logic Inference for Electric Vehicles Parking Lot

Hussain, Shahid; Ahmed, Mohamed A.; Kim, Young-Chon

doi:10.1109/access.2019.2917297

Cited by 72 publications

(31 citation statements)

References 34 publications

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“…Several techniques such as reinforcement learning, deep neural networks, and fuzzy logic, have been considered for resource allocation and power management in different scenarios [24]- [26]. In [24], the authors proposed a Deep-Q Network-enabled resource allocation and task offloading for edge computing.…”

Section: Related Workmentioning

confidence: 99%

“…[25] proposed the use of deep neural network for computational resource allocation in edge computing scenario. The other work in [26] used a fuzzy logic inference based scheme for efficient power management in electric vehicles in a parking lot. On the other hand, the works in [27]- [30] used decomposition and relaxation-based algorithms to solve the resource allocation problem in wireless networks.…”

Section: Related Workmentioning

confidence: 99%

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Resource Optimized Federated Learning-Enabled Cognitive Internet of Things for Smart Industries

et al. 2020

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Leveraging the cognitive Internet of things (C-IoT), emerging computing technologies, and machine learning schemes for industries can assist in streamlining manufacturing processes, revolutionizing operational analytics, and maintaining factory efficiency. However, further adoption of centralized machine learning in industries seems to be restricted due to data privacy issues. Federated learning has the potential to bring about predictive features in industrial systems without leaking private information. However, its implementation involves key challenges including resource optimization, robustness, and security. In this paper, we propose a novel dispersed federated learning (DFL) framework to provide resource optimization, whereby distributed fashion of learning offers robustness. We formulate an integer linear optimization problem to minimize the overall federated learning cost for the DFL framework. To solve the formulated problem, first, we decompose it into two sub-problems: association and resource allocation problem. Second, we relax the association and resource allocation sub-problems to make them convex optimization problems. Later, we use the rounding technique to obtain binary association and resource allocation variables. Our proposed algorithm works in an iterative manner by fixing one problem variable (for example, association) and compute the other (for example, resource allocation). The iterative algorithm continues until convergence of the formulated cost optimization problem. Furthermore, we compare the proposed DFL with two schemes; namely, random resource allocation and random association. Numerical results show the superiority of the proposed DFL scheme. INDEX TERMS Smart industry, cognitive Internet of Things, federated learning, convex optimization.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Resource Optimized Federated Learning-Enabled Cognitive Internet of Things for Smart Industries

et al. 2020

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“…The main advantage of this issue is that uncertainty can be minimized when expert team could not reach a consensus [24,25]. The α-cut operation on the fuzzy set is defined for categorizing the crisp sets into subsets with membership function values greater than or equal to alpha for one specific purpose [26]. In this context, owing to the alpha cuts, it will be possible to perform many arithmetic operations [27].…”

Section: Introductionmentioning

confidence: 99%

Analyzing the Investments Strategies for Renewable Energies Based on Multi-Criteria Decision Model

Zhong

Yüksel

et al. 2020

IEEE Access

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The purpose of this study is to determine appropriate innovative strategies for the renewable energy investments. For this purpose, a hybrid multi-criteria decision-making (MCDM) model is proposed based on interval type-2 (IT2) fuzzy sets and alpha cut levels. Within this context, hesitant IT2 fuzzy DEMATEL-Based Analytic Network Process (DANP) with alpha cut levels is applied for weighting the customer requirements. Moreover, hesitant IT2 fuzzy technique for order preference by similarity to ideal solution (TOPSIS) with alpha cut levels is used for ranking the TRIZ-based strategies priorities of renewable energy investments based on house of quality technique. These strategies are also ranked with hesitant IT2 fuzzy Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) approach to make a comparative evaluation. The results illustrate that ease of access and security are among the most prominent factors of customer requirements for renewable energy investments. It is also identified that innovative technical requirement entitled cushion in advance has the best choice for the sustainable renewable energy projects. Furthermore, the results of proposed model using hesitant IT2 fuzzy DANP and hesitant IT2 fuzzy TOPSIS are identical for the different levels of alpha cut. In addition, it is also defined that the results of hesitant IT2 fuzzy TOPSIS and hesitant IT2 fuzzy VIKOR are quite coherent. This is a clear evidence that the proposed model is coherent and could provide comprehensive results for the future studies. It is strongly recommended that a detailed analysis is required to identify the risks in renewable energy investment projects. Hence, necessary actions can be taken appropriately so that it can be possible to prevent problems before they grow.

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“…Currently, many studies have investigated the V2G technology for better use of EV penetrations [4]- [6]. Liu et al [7] established a V2G behavior scheduling model based on Blockchain technology to improve grid operation stability.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Bi-Directional V2G Behavior With Active Battery Anti-Aging Scheduling

2020

IEEE Access

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The bi-directional linkage between the power grid and electric vehicles (EVs) enables flexible, cheap and fast-responding use of vehicle batteries in the grid. However, the battery aging effects due to the additional operation cycles caused by Vehicle-to-Grid (V2G) service and the concern of the battery degradation are the main reason that keeps the customer from being the named prosumer of the grid. This paper proposes a novel active battery anti-aging V2G scheduling approach. Firstly, to evaluate the battery aging effect in V2G service, the battery degradation phenomenon is quantified by a novel use of rain-flow cycle counting (RCC) algorithm. Then, the V2G scheduling is modeled as a multi-objective optimization problem, in which the minimal battery degradation and grid load fluctuation are designed as the optimization objectives. Finally, a multi-population collaborative mechanism, which is particularly designed for the V2G scheduling problem, is also developed to improve the practicability and performance of the heuristic optimization based V2G scheduling method. The proposed methodologies are verified by numerical analysis, which highlights that the proposed V2G scheduling method can minimize battery charge/discharge cycles by optimizing the time and scale of each V2G participant while providing the same services to the grid as expected. INDEX TERMS Electric vehicle, vehicle to grid, active battery anti-aging and heuristic algorithm.

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Efficient Power Management Algorithm Based on Fuzzy Logic Inference for Electric Vehicles Parking Lot

Cited by 72 publications

References 34 publications

Resource Optimized Federated Learning-Enabled Cognitive Internet of Things for Smart Industries

Resource Optimized Federated Learning-Enabled Cognitive Internet of Things for Smart Industries

Analyzing the Investments Strategies for Renewable Energies Based on Multi-Criteria Decision Model

Optimization of Bi-Directional V2G Behavior With Active Battery Anti-Aging Scheduling

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