Using Adaptive Fuzzy Logic for Intelligent Energy Management in Hybrid Vehicles

Zand, Mohammad; Nasab, Morteza Azimi; Hatami, Ali; Kargar, Majid; Chamorro, Harold R.

doi:10.1109/icee50131.2020.9260941

Cited by 39 publications

(18 citation statements)

References 12 publications

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“…To analyse the contribution of an SST‐based charging station to instantaneous auxiliary services, a comparison between the proposed charging station and the conventional charging station presented in [33] under the same power order, identical target SOC, and identical parameters of EVs, Are executed. The market price for the adjustment was obtained from the ERCOT website (Texas Electricity Board).…”

Section: Comparison With An Existing Systemmentioning

confidence: 99%

“…The market price for the adjustment was obtained from the ERCOT website (Texas Electricity Board). In [33], the EV power allocation strategy consists of two parts: keeping the battery level mode module level and adjusting the battery level SOC module. Power is adjusted to suit the SOC of each car.…”

Section: Comparison With An Existing Systemmentioning

confidence: 99%

“…Different structures have been proposed for EMS using different optimisation algorithms and different configurations for micro‐networks, to optimise the performance of each of the resources in the micro‐network system to minimise system operating costs. In [28–30], the EMSs have been proposed, the concepts of economic discussion have been explored and developed strategies to maximise profit [31–34]. Due to the limitations of the use of renewable energy sources, various methods have been proposed to restore energy balance in micro‐grids [5, 33, 35, 36].…”

Section: Introductionmentioning

confidence: 99%

“…In [28–30], the EMSs have been proposed, the concepts of economic discussion have been explored and developed strategies to maximise profit [31–34]. Due to the limitations of the use of renewable energy sources, various methods have been proposed to restore energy balance in micro‐grids [5, 33, 35, 36]. To eliminate the problems caused by energy imbalances, renewable systems are combined with energy storage [37–39].…”

Section: Introductionmentioning

confidence: 99%

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Energy management strategy for solid‐state transformer‐based solar charging station for electric vehicles in smart grids

Zand

Nasab

Padmanaban

et al. 2020

IET Renewable Power Generation

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This study introduces a type of solid‐state transformer (SST) for solar power station design and an energy management strategy (EMS) for the SST. The purpose of this study is to design a real efficient EMS for the photovoltaic‐assisted charging station in smart grid ancillary services and apply the optimal decision method. Also, the energy bound calculation (EBC) model is proposed to find the upper and lower bounds of flexible sources. Also, taking into account the EBC results and the power order from the aggregator, a charge power allocation algorithm is designed to distribute the power of flexible electric vehicles (EVs). With the help of a case study and laboratory analysis, the proposed EMS strategy is effective in real‐time energy management and is suitable for practical applications. The obtained results show the stable performance in the calculation of the energy range and real‐time power allocation which improves the efficiency of the photovoltaic‐based charging station. Also, the SST improves the operation of charge stations for supplying the sustaining power.

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Section: Comparison With An Existing Systemmentioning

confidence: 99%

Section: Comparison With An Existing Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Energy management strategy for solid‐state transformer‐based solar charging station for electric vehicles in smart grids

Zand

Nasab

Padmanaban

et al. 2020

IET Renewable Power Generation

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“…Digital two-way communication between power companies and conventional home appliances facilitates the joint operation of intelligent energy management systems. Advanced smart grid components enable users to improve their energy efficiency and to take part in different programs such as {time-of-day} pricing to reduce their energy costs [16,17]. Many electricity scheduling schemes have been proposed in both indoor and residential areas [18].…”

Section: Introductionmentioning

confidence: 99%

Optimal Planning of Electrical Appliance of Residential Units in a Smart Home Network Using Cloud Services

Nasab¹,

Zand²,

Eskandari

et al. 2021

Smart Cities

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One of the important aspects of realizing smart cities is developing smart homes/buildings and, from the energy perspective, designing and implementing an efficient smart home area energy management system (HAEMS) is vital. To be effective, the HAEMS should include various electrical appliances as well as local distributed/renewable energy resources and energy storage systems, with the whole system as a microgrid. However, the collecting and processing of the data associated with these appliances/resources are challenging in terms of the required sensors/communication infrastructure and computational burden. Thanks to the internet-of-things and cloud computing technologies, the physical requirements for handling the data have been provided; however, they demand suitable optimization/management schemes. In this article, a HAEMS is developed using cloud services to increase the accuracy and speed of the data processing. A management protocol is proposed that provides an optimal schedule for a day-ahead operation of the electrical equipment of smart residential homes under welfare indicators. The proposed system comprises three layers: (1) sensors associated with the home appliances and generation/storage units, (2) local fog nodes, and (3) a cloud where the information is processed bilaterally with HAEMS and the hourly optimal operation of appliances/generation/storage units is planned. The neural network and genetic algorithm (GA) are used as part of the HAEMS program. The neural network is used to predict the amount of workload corresponding to users’ requests. Improving the load factor and the economic efficiency are considered as the objective function that is optimized using GA. Numerical studies are performed in the MATLAB platform and the results are compared with a conventional method.

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