Rechargeable battery technologies

Kularatna, Nihal

doi:10.1016/b978-0-12-407947-2.00002-x

Cited by 8 publications

(1 citation statement)

References 13 publications

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“…In order to evaluate the (economic profit of the V2G system operation/impact of the V2G system on MG operation cost), V2G energy selling revenues and buying cost have to be estimated in the first place. 44 In the present study, the price of V2G energy considering battery wear cost will be addressed.…”

Section: Economic Assessment Of the V2g Operationmentioning

confidence: 99%

Implementation of cost benefit analysis of vehicle to grid coupled real Micro-Grid by considering battery energy wear: Practical study case

Koubaa¹,

Yoldaş

Goren

et al. 2020

Energy & Environment

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The proposed research represents a spin-off of the Malta College of Arts, Science and Technology (MCAST) Micro-Grid (MG) project. Particularly, economic impact of Electric Vehicles (EV) integration into the MG is investigated in this paper. The MCAST MG consists of photovoltaic generation unit, a diesel generator and a battery storage system. In this paper, a Vehicle-to grid (V2G) concept is considered where utilities can profit from controlled energy trading operations according to EVs availability. EVs are categorized under different profiles considering energy and time availability of owners typical work hours. V2G energy cost is estimated based on battery energy wear due V2G extra cycling and refunded to EVs owners. As most of developed V2G studies don’t consider real world input data or/and EV battery aging cost in system modeling and evaluation, the present paper presents a reliable study as it considers a real life MG with in field measurement input data and appropriate battery degradation model. The adopted model represents a linear approximation with a minimum error value to make a suitable tradeoff of computational complexity and accuracy of obtained results. Economic assessment of the system according to the proposed energy management is performed, where results indicate that the V2G system assisted the MG operation during high electricity price period and achieved economic profit to EVs owners. According to numerical results, V2G energy trading achieved 29.90 EUR of gross selling revenues with only 4.46 EUR as battery degradation cost which makes a 16.41% average cost reduction of daily MG operation cost.

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Section: Economic Assessment Of the V2g Operationmentioning

confidence: 99%

Implementation of cost benefit analysis of vehicle to grid coupled real Micro-Grid by considering battery energy wear: Practical study case

Koubaa¹,

Yoldaş

Goren

et al. 2020

Energy & Environment

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Convolutional neural network–bagged decision tree: a hybrid approach to reduce electric vehicle’s driver’s range anxiety by estimating energy consumption in real-time

2020

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A system for electric vehicle’s energy-aware routing in a transportation network through real-time prediction of energy consumption

Modi

Bhattacharya

2022

Complex Intell. Syst.

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To tackle the problem of range anxiety of a driver of an electric vehicle (EV), it is necessary to accurately estimate the power/energy consumption of EVs in real time, so that drivers can get real-time information about the vehicle’s remaining range. In addition, it can be used for energy-aware routing, i.e., the driver can be provided with information that on which route less energy consumption will take place. In this paper, an integrated system has been proposed which can provide reliable and real-time estimate of the energy consumption for an EV. The approach uses Deep Auto-Encoders (DAE), cross-connected using latent space mapping, which consider historical traffic speed to predict the traffic speed at multiple time steps in future. The predicted traffic speed is used to calculate the future vehicle speed. The vehicle speed, acceleration along with wind speed, road elevation, temperature, battery’s SOC, and auxiliary loads are used as input to a multi-channel Convolutional Neural Network (CNN) to predict the energy consumption. The prediction is further fine-tuned using a Bagged Decision Tree (BDT). Unlike other existing techniques, the proposed system can be easily generalized for other vehicles as it is independent of internal vehicle parameters. Comparison with other benchmark techniques shows that the proposed system performs better and has a least mean absolute percentage error of 1.57%.

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Rechargeable battery technologies

Cited by 8 publications

References 13 publications

Implementation of cost benefit analysis of vehicle to grid coupled real Micro-Grid by considering battery energy wear: Practical study case

Implementation of cost benefit analysis of vehicle to grid coupled real Micro-Grid by considering battery energy wear: Practical study case

Convolutional neural network–bagged decision tree: a hybrid approach to reduce electric vehicle’s driver’s range anxiety by estimating energy consumption in real-time

A system for electric vehicle’s energy-aware routing in a transportation network through real-time prediction of energy consumption

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