A technological overview &amp; design considerations for developing electric vehicle charging stations

Narasipuram, Rajanand Patnaik; Mopidevi, Subbarao

doi:10.1016/j.est.2021.103225

Cited by 106 publications

(45 citation statements)

References 75 publications

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“…The financial benefits obtained by laying charging piles in different regions are also diverse [35]. Furthermore, optimizing the site selection of charging piles [125,126,170] and analyzing charging transaction data [151] can further improve environmental efficiency. Under mature charging information interaction, the environmental efficiency can be optimized by 1.16~2.90% by 2030 and 0.89~5.36% by 2040 [151].…”

Section: Operation Equipment Settling and Usingmentioning

confidence: 99%

A Review on Environmental Efficiency Evaluation of New Energy Vehicles Using Life Cycle Analysis

Wang

Tang

2022

Sustainability

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New energy vehicles (NEVs), especially electric vehicles (EVs), address the important task of reducing the greenhouse effect. It is particularly important to measure the environmental efficiency of new energy vehicles, and the life cycle analysis (LCA) model provides a comprehensive evaluation method of environmental efficiency. To provide researchers with knowledge regarding the research trends of LCA in NEVs, a total of 282 related studies were counted from the Web of Science database and analyzed regarding their research contents, research preferences, and research trends. The conclusion drawn from this research is that the stages of energy resource extraction and collection, carrier production and energy transportation, maintenance, and replacement are not considered to be research links. The stages of material, equipment, and car transportation and operation equipment settling, and forms of use need to be considered in future research. Hydrogen fuel cell electric vehicles (HFCEVs), vehicle type classification, the water footprint, battery recovery and reuse, and battery aging are the focus of further research, and comprehensive evaluation combined with more evaluation methods is the direction needed for the optimization of LCA. According to the results of this study regarding EV and hybrid power vehicles (including plug-in hybrid electric vehicles (PHEV), fuel-cell electric vehicles (FCEV), hybrid electric vehicles (HEV), and extended range electric vehicles (EREV)), well-to-wheel (WTW) average carbon dioxide (CO2) emissions have been less than those in the same period of gasoline internal combustion engine vehicles (GICEV). However, EV and hybrid electric vehicle production CO2 emissions have been greater than those during the same period of GICEV and the total CO2 emissions of EV have been less than during the same period of GICEV.

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Section: Operation Equipment Settling and Usingmentioning

confidence: 99%

A Review on Environmental Efficiency Evaluation of New Energy Vehicles Using Life Cycle Analysis

Wang

Tang

2022

Sustainability

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“…The biggest challenge of EV cluster charging [19] is to make charging choices for each charging stake while maintaining load balancing and minimizing user usage costs. Since there are different cooperative or competitive relationships between each charging stake, it is difficult for ordinary reinforcement learning to satisfy its conditions, while multi-agent reinforcement learning coordinates the strategies between each intelligence through a central processor, ensuring the independence of the intelligent body's decisions while maintaining its cooperative communication capabilities.…”

Section: Ev Cluster Charging Strategy: Markov Decision Processmentioning

confidence: 99%

Online Charging Strategy for Electric Vehicle Clusters Based on Multi-Agent Reinforcement Learning and Long–Short Memory Networks

Shen¹,

Zhang²,

Wang³

2022

Energies

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The electric vehicle (EV) cluster charging strategy is a key factor affecting the grid load shifting in vehicle-to-grid (V2G) mode. The conflict between variable tariffs and electric-powered energy demand at different times of the day directly affects the charging cost, and in the worst case, can even lead to the collapse of the whole grid. In this paper, we propose a multi-agent reinforcement learning and long-short memory network (LSTM)-based online charging strategy for community home EV clusters to solve the grid load problem and minimize the charging cost while ensuring benign EV cluster charging loads. In this paper, the accurate prediction of grid prices is achieved through LSTM networks, and the optimal charging strategy is derived from the MADDPG multi-agent reinforcement learning algorithm. The simulation results show that, compared with the DNQ algorithm, the EV cluster online charging strategy algorithm can effectively reduce the overall charging cost by about 5.8% by dynamically adjusting the charging power at each time period while maintaining the grid load balance.

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“…Zhang et al studied the best patent licensing strategy in a supply chain consisting of the joint research and development (R&D) investments of an original equipment manufacturer (OEM) and a contract manufacturer [9]. Narasipuram and Mopidevi described optimization algorithms that could create optimal designs for EV charging stations [10]. Yuan analyzed the threshold on firms' patent pool number and found a negative correlation between the threshold and patent licensing fee [11].…”

Section: Literature Backgroundmentioning

confidence: 99%

“…Based on the existing literature regarding electric vehicle cell innovation diffusion without a patent pool [3], we extended our analysis to consider patent pool strategy by following the evolutionary game and optimization algorithm approaches, which sets our study apart from others in the literature that consider different industries, different factors, different supply chain structures, or different approaches [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. This study contributes to the existing literature on innovation diffusion in the electric vehicle cell industry since it considered a patent pool strategy, established an innovation diffusion channel model, conducted an evolutionary game analysis and simulation, identified the key factors and the interplay between these factors, and developed an optimization algorithm for use by decision-and policymakers.…”

Section: Introductionmentioning

confidence: 99%

The Evolutionary Game Analysis and Optimization Algorithm of Electric Vehicle Cell Innovation Diffusion Based on a Patent Pool Strategy

Sun

Yuan

Zhang

2021

WEVJ

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A patent pool strategy was proposed for use in the electric vehicle cell industry to manage patent licensing disputes and litigation. How to promote EV cell innovation diffusion under a patent pool scenario is unclear. We introduced an innovation diffusion channel model comprising different players with patent licensing relationships and market competition relationships following evolutionary game analysis and simulation. We found the interlinked factors that influenced evolutionary stable strategies with a sensitivity test on all factors to identify the important and unimportant factors. To achieve the maximum return for the players, an optimization algorithm was introduced to find the maximum weighted object function. The decision and policy makers could focus on important factors such as improving the technology’s competitive advantages, delivering more profits to its licensees with reasonable licensing fees, and finding the best patent pool strategy with the support of the optimization algorithm to balance the competition relationships and patent licensing relationships between players.

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A technological overview & design considerations for developing electric vehicle charging stations

Cited by 106 publications

References 75 publications

A Review on Environmental Efficiency Evaluation of New Energy Vehicles Using Life Cycle Analysis

A Review on Environmental Efficiency Evaluation of New Energy Vehicles Using Life Cycle Analysis

Online Charging Strategy for Electric Vehicle Clusters Based on Multi-Agent Reinforcement Learning and Long–Short Memory Networks

The Evolutionary Game Analysis and Optimization Algorithm of Electric Vehicle Cell Innovation Diffusion Based on a Patent Pool Strategy

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