Possibilities and Challenges for the Inclusion of the Electric Vehicle (EV) to Reduce the Carbon Footprint in the Transport Sector: A Review

Ghosh, Aritra

doi:10.3390/en13102602

Cited by 237 publications

(112 citation statements)

References 164 publications

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“…Besides, Perovskite integrated into a building in northern latitude place where snow and rain are the most common phenomenon that should be considered before integration [255,256]. In the case of a hot climate and dust environment, how this technology will perform can be a significant investigating area before building integration [257][258][259][260][261][262][263] that must be a vital fact when considering PSC integration in BIPV.…”

Section: Commercial and Building Integration Confronts Of Pscsmentioning

confidence: 99%

Perovskite Solar Cells for BIPV Application: A Review

et al. 2020

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The rapid efficiency enhancement of perovskite solar cells (PSCs) make it a promising photovoltaic (PV) research, which has now drawn attention from industries and government organizations to invest for further development of PSC technology. PSC technology continuously develops into new and improved results. However, stability, toxicity, cost, material production and fabrication become the significant factors, which limits the expansion of PSCs. PSCs integration into a building in the form of building-integrated photovoltaic (BIPV) is one of the most holistic approaches to exploit it as a next-generation PV technology. Integration of high efficiency and semi-transparent PSC in BIPV is still not a well-established area. The purpose of this review is to get an overview of the relative scope of PSCs integration in the BIPV sector. This review demonstrates the benevolence of PSCs by stimulating energy conversion and its perspective and gradual evolution in terms of photovoltaic applications to address the challenge of increasing energy demand and their environmental impacts for BIPV adaptation. Understanding the critical impact regarding the materials and devices established portfolio for PSC integration BIPV are also discussed. In addition to highlighting the apparent advantages of using PSCs in terms of their demand, perspective and the limitations, challenges, new strategies of modification and relative scopes are also addressed in this review.

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Section: Commercial and Building Integration Confronts Of Pscsmentioning

confidence: 99%

Perovskite Solar Cells for BIPV Application: A Review

et al. 2020

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“…The AHP allows an intuitive procedure for the users to easily define their preferences assigning the appropriate values to the criteria matrix. Afterwards, the values of the criteria are normalised in the [1,9] scale that is used by the AHP during the evaluation of the alternatives (16) (Equal importance = 1, Extremely Important = 9).…”

Section: Price Criterion =mentioning

confidence: 99%

“…Subjects related to electric vehicles (EVs) have gained the attention of both academia and industry. Ever-increasing environmental concerns have triggered research on new technologies that will make EVs an environmental-friendly alternative to conventional vehicles [1]. In addition, the integration of renewable energy sources (RES) in the electric grid enables the distributed energy production and management, following the philosophy of the smart grid [2].…”

Section: Introductionmentioning

confidence: 99%

A Decision-Making Framework for the Smart Charging of Electric Vehicles Considering the Priorities of the Driver

2020

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During the last decade, the technologies related to electric vehicles (EVs) have captured both scientific and industrial interest. Specifically, the subject of the smart charging of EVs has gained significant attention, as it facilitates the managed charging of EVs to reduce disturbances to the power grid. Despite the presence of an extended literature on the topic, the implementation of a framework that allows flexibility in the definition of the decision-making objectives, along with user-defined criteria is still a challenge. Towards addressing this challenge, a framework for the smart charging of EVs is presented in this paper. The framework consists of a heuristic algorithm that facilitates the charge scheduling within a charging station (CS), and the analytic hierarchy process (AHP) to support the driver of the EV selecting the most appropriate charging station based on their needs of transportation and personal preferences. The communications are facilitated by the Open Platform Communications–Unified Architecture (OPC–UA) standard. For the selection of the scheduling algorithm, the genetic algorithm and particle swarm optimisation have been evaluated, where the latter had better performance. The performance of the charge scheduling is evaluated, in various charging tasks, compared to the exhaustive search for small problems.

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“…In recent years, electric vehicles (EV) are considered a promising eco-friendly means of transportation that alleviate the environmental pollution problems caused by the use of traditional fossil fuel sources [1]. Although EVs aim to provide zero fossil fuel consumption and emits no greenhouse gases, the additional charging power generated by the increase in the use of EVs may be concentrated in a specific time period, depending on the user's charging patterns, which significantly result in high peak demand.…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning Based Optimal Route and Charging Station Selection

et al. 2020

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This paper proposes an optimal route and charging station selection (RCS) algorithm based on model-free deep reinforcement learning (DRL) to overcome the uncertainty issues of the traffic conditions and dynamic arrival charging requests. The proposed DRL based RCS algorithm aims to minimize the total travel time of electric vehicles (EV) charging requests from origin to destination using the selection of the optimal route and charging station considering dynamically changing traffic conditions and unknown future requests. In this paper, we formulate this RCS problem as a Markov decision process model with unknown transition probability. A Deep Q network has been adopted with function approximation to find the optimal electric vehicle charging station (EVCS) selection policy. To obtain the feature states for each EVCS, we define the traffic preprocess module, charging preprocess module and feature extract module. The proposed DRL based RCS algorithm is compared with conventional strategies such as minimum distance, minimum travel time, and minimum waiting time. The performance is evaluated in terms of travel time, waiting time, charging time, driving time, and distance under the various distributions and number of EV charging requests.

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Possibilities and Challenges for the Inclusion of the Electric Vehicle (EV) to Reduce the Carbon Footprint in the Transport Sector: A Review

Cited by 237 publications

References 164 publications

Perovskite Solar Cells for BIPV Application: A Review

Perovskite Solar Cells for BIPV Application: A Review

A Decision-Making Framework for the Smart Charging of Electric Vehicles Considering the Priorities of the Driver

Deep Reinforcement Learning Based Optimal Route and Charging Station Selection

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