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http://localhost:8081/jspui/handle/123456789/20950| Title: | MATHEMATICAL MODELLING FOR THE FORECASTING ADOPTION PATTERN OF ELECTRIC VEHICLES USING BASS FORECASTING METHOD |
| Authors: | Singh, Lalit |
| Issue Date: | May-2022 |
| Publisher: | IIT Roorkee |
| Abstract: | Contamination of the climate is as of now a worldwide concern. Poisonous discharge from gas
powered motors is one of the essential air toxins. To moderate the impacts of petroleum product
emanation and address ecological worries, electric vehicles (EVs) are being advanced
forcefully from one side of the planet to the other. Different legislatures are empowering
individuals to change to EVs by boosting the change. Past investigations demonstrate that the
EV’s significant expenses, issues related to charging, tension of arriving on time go about as
hindrances to buyer reception. Thirty percent target of EVs of government is also there in the
line.
The concern of this work is to develop a mathematical modelling technique to study or to
forecast the patterns for adopting electric vehicles for a region specifically, the approach may
be extended further with the advancement in studies with time in future.
The hypothetical system of Bass model utilized here gives a reasoning to long-go estimating.
The hypothesis stems numerically from the disease models which have tracked down such
boundless application in various areas such as epidemiology (BARTLETT, 1960).
To anticipate electric car sales, this study uses the Bass diffusion model. Because historical
data is scarce, a variety of methodologies is used to forecast goods/product demand. It appears
that analysis is being used to compare similar products and sales trends in order to choose the
product that is most similar to electric automobiles. The variables of the model of diffusion by
Bass are then determined, and possible genesis predictions are created by utilizing the
approaches. According to the findings, electric vehicle sales will peak between 2030 and 2032
in Delhi NCT region. Contamination of the climate is as of now a worldwide concern. Poisonous discharge from gas powered motors is one of the essential air toxins. To moderate the impacts of petroleum product emanation and address ecological worries, electric vehicles (EVs) are being advanced forcefully from one side of the planet to the other. Different legislatures are empowering individuals to change to EVs by boosting the change. Past investigations demonstrate that the EV’s significant expenses, issues related to charging, tension of arriving on time go about as hindrances to buyer reception. Thirty percent target of EVs of government is also there in the line. The concern of this work is to develop a mathematical modelling technique to study or to forecast the patterns for adopting electric vehicles for a region specifically, the approach may be extended further with the advancement in studies with time in future. The hypothetical system of Bass model utilized here gives a reasoning to long-go estimating. The hypothesis stems numerically from the disease models which have tracked down such boundless application in various areas such as epidemiology (BARTLETT, 1960). To anticipate electric car sales, this study uses the Bass diffusion model. Because historical data is scarce, a variety of methodologies is used to forecast goods/product demand. It appears that analysis is being used to compare similar products and sales trends in order to choose the product that is most similar to electric automobiles. The variables of the model of diffusion by Bass are then determined, and possible genesis predictions are created by utilizing the approaches. According to the findings, electric vehicle sales will peak between 2030 and 2032 in Delhi NCT region. |
| URI: | http://localhost:8081/jspui/handle/123456789/20950 |
| Research Supervisor/ Guide: | Anbanandam, Ramesh |
| metadata.dc.type: | Dissertations |
| Appears in Collections: | MASTERS' THESES (C-TRANS) |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 20554009_Lalit Singh.pdf | 1.03 MB | Adobe PDF | View/Open |
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