Forecasting Battery Electric Vehicles

Kim, Jisun; Yu, Chih-Jen; Khammuang, Monticha; Lui, Jonathan; Almujahid, Abdullah; Daim, Tuğrul U.

doi:10.1109/temscon.2017.7998377

Cited by 6 publications

(5 citation statements)

References 3 publications

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“…It shows that the SISO modes are the most critical in the design and control of the TAB converter, where the phase-shift angles are the largest. 4 4 The experimental waveform of each port in operation mode 1-2 is shown in Figure 9 to explain the characteristic in one of the most critical operation modes. The figure shows that the root mean square (RMS) and peak current of port 2, i 2 , are much higher in the voltage variation condition.…”

Section: Operation Mode and Characteristicsmentioning

confidence: 99%

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Applications of Triple Active Bridge Converter for Future Grid and Integrated Energy Systems

Pham

Wãda

2020

Energies

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Renewable energy systems and electric vehicles (EVs) are receiving much attention in industrial and scholarly communities owing to their roles in reducing pollutant emissions. Integrated energy systems (IES), which connect different types of renewable energies and storages, have become common in many applications, such as the grid-connected photovoltaic (PV) and battery systems, fuel cells and battery/supercapacitor in EVs. The advantages of all energy sources are maximized by utilizing connection and control strategies. Because many storage systems and household loads are mainly direct current (DC) types, the DC grid has considerable potential for increasing the efficiency of distribution grids in the future. In IES and future DC grid systems, the triple active bridge (TAB) converter is an isolated bidirectional DC-DC converter that has many advantages as a core circuit. Therefore, this paper reviews the characteristics of the TAB converter in current applications and suggests next-generation applications. First, the characteristics and operation modes of the TAB converter are introduced. An overview of all current applications of the TAB converter is then presented. The advantages and challenges of the TAB converter in each application are discussed. Thereafter, the potential future applications of the TAB converter with an adaptable power transmission design are presented.

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Section: Operation Mode and Characteristicsmentioning

confidence: 99%

“…In current years, renewable energy systems and electric vehicles (EVs) have been increasingly used [1][2][3][4][5][6]. Renewable energy systems include, but are not limited to, photovoltaic (PV), wind power, biomass, hydro, and geothermal systems.…”

Section: Introductionmentioning

confidence: 99%

Applications of Triple Active Bridge Converter for Future Grid and Integrated Energy Systems

Pham

Wãda

2020

Energies

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“…Gough found that the key parameters are the electric vehicle electricity sale price, the battery degradation costs, and the infrastructure costs (2017). Kim, Yu, Khammuang, Liu and Almujahid (2017) presented a multicriteria model for forecasting the amount of battery electric vehicles (BEV). Using the exponential smoothing method, Peng, Yu, Wang, Yang (2014) elaborated on the EV demand forecast, focusing on developed countries.…”

Section: Introductionmentioning

confidence: 99%

European Green Deal Implications on Country Level Energy Consumption

Jaržemskis

Jaržemskienė

2022

Folia Oeconomica Stetinensia

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Research background: The European Green deal set by the European Commission has launched new business models in sustainable development. Major contributions are expected in the road transport sector; as far as conventional internal combustion creates significant input in Green House Gas emission inventories. Each EU member state has an obligation to reduce GhG emission by accelerating Electric Vehicle development. In order to foster growth of EVs, there is the need of significant investment into charging infrastructures. The article propose the model of forecasting of investment based on the forecast of the growth of the amount of electric vehicles and their demand on energy. The model includes the behaviouristic approach based on the total cost of ownership model as well as calculations of efficient usage of EV charging points. The model takes into account all types of vehicles including personal and commercial, freight and passenger. Purpose: The aim of this article is to present a complex model for forecasting the required investments based on the fore-cast of the increase in the number of electric vehicles and their demand on energy and investments. Research methodology: The general algorithm of forecasting consists of several consecutive phases: (1) Forecasting the number of electric vehicles, (2) Forecasting the energy needed for electric vehicles, based on the forecast (1) and the predicted usage level of these vehicles. (3) Forecasting the charging station number with the expected technical capacities and characteristics of these charging stations based on the forecasts (1) and (2). (4) Forecasting the need to upgrade the low-voltage grid based on the forecast (3). (5) Calculating the total investment needed based on the results of the forecasts (3) and (4). The main limitations of the study are related to the statistics available for modelling and human behaviour uncertainty, especially in the evaluation impact of measures to foster use of electric vehicles. Results: The findings of the Lithuanian case analysis, which is expressed in three scenarios, focuses on two trends. The most promising scenario projects 319,470 electric vehicles by 2030 which will demand for 1.09 TWh of electricity, representing 8.4–9.9 percent of the total energy consumption in the country. It requires EUR 230, million in the low-voltage grid and EUR 209, million in the charging stations. Novelty: The scientific problem is that the current approach on the forecasting of electric vehicles is too abstract, forecast models cannot be transferred from country to country. This article proposes a model of forecasting investments based on the forecast of the increase in the number of electric vehicles and their demand on energy. The model includes the behaviouristic approach based on the total cost of ownership model as well as calculations of efficient usage of EV charging points. The model takes into account all types of vehicles including personal and commercial, freight and passenger. The article has proven that statistics-based forecasting gives very different results compared to the objective function and to the evaluation of the effects of measures. This has not been compared in previous studies.

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“…Gough found that the key parameters are the electric vehicle electricity sale price, the battery degradation costs, and the infrastructure costs (2017). Kim et al (2017) presented a multicriteria model for forecasting the amount of battery electric vehicles (BEV). Using the exponential smoothing method, Peng et al (2014) elaborated on the EV demand forecast, focusing on developed countries.…”

Section: Introductionmentioning

confidence: 99%

Forecast Methods for Investment of Country Wide Electric Vehicle Charging Stations: Lithuanian Case

Jaržemskis

Jaržemskienė

2022

12th International Scientific Conference “Business and Management 2022”

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The aim of this article is to present a complex model for forecasting the required investments based on the forecast of the increase in the number of electric vehicles and their demand for energy and investments. Scientific problem is that current approach on forecasting of electric vehicles is to abstract, forecast models can’t be transferred from country to country. This article proposes a model of forecasting investments based on the fore-cast of the increase in the number of electric vehicles and their demand on energy. The findings of the Lithuanian case analysis, which is expressed in three scenarios, focuses on two trends. The most promising scenario projects 319 470 electric vehicles by 2030 which will demand for 1.09 TWh of electricity, representing 8.4–9.9 percent of the total en-ergy consumption in the country. It demands EUR 230.0 million in the low-voltage grid and EUR 209.0 million in the charging stations. Main limitations are related to statistics available for modelling and human behaviour uncertainty, especially in evaluation impact of measures to foster use of electric vehicles.

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Forecasting Battery Electric Vehicles

Cited by 6 publications

References 3 publications

Applications of Triple Active Bridge Converter for Future Grid and Integrated Energy Systems

Applications of Triple Active Bridge Converter for Future Grid and Integrated Energy Systems

European Green Deal Implications on Country Level Energy Consumption

Forecast Methods for Investment of Country Wide Electric Vehicle Charging Stations: Lithuanian Case

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