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Engineering and Technology Management Faculty Publications and Presentations
Engineering and Technology Management

2013

Forecasting Hybrid Electric Vehicles Using TFDEA

Shabnam Razeghian Jahromi

Portland State University

Anca-Alexandra Tudori

Delft University of Technology

Timothy R. Anderson

Portland State University, [email protected]

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Jahromi, Shabnam Razeghian; Tudori, Anca-Alexandra; and Anderson, Timothy R., Forecasting Hybrid Electric Vehicles using TFDEA, Portland International Conference on Management of Engineering and Technology (PICMET), Portland, OR, 2013.

This Article is brought to you for free and open access. It has been accepted for inclusion in Engineering and Technology Management Faculty Publications and Presentations by an authorized administrator of PDXScholar. Please contact us if we can make this document more accessible: [email protected].

2013 Proceedings of PICMET '13: Technology Management for Emerging Technologies.

Forecasting Hybrid Electric Vehicles using TFDEA

Shabnam Razeghian Jahromi1, Anca-Alexandra Tudori2, Timothy R. Anderson1

1Dept. of Engineering and Technology Management, Portland State University, Portland, OR - USA
2Delft University of Technology, Delft, Holland

Abstract--The Toyota Prius was introduced in Japan fifteen years ago and over 60 additional hybrid electric vehicles automobiles and redesigns have been brought to the market around the world since that time. There is major interest in future of the electric cars as using “the alternative fuel” can significantly decrease the environmental and fuel dependency concerns. This work has used data envelopment analysis to forecast the future of hybrid electric cars. It is based on a previous research on the same subject with an improvement in input-output model that has enhanced the outcomes to a great extent. The former study applied technology forecasting for both full-battery and hybrid electric cars. This research focuses on hybrid electric vehicles only and improving the model for battery electric cars will be a subject for future research. The dataset is the same as the one used in former research with some additional parameters that are gathered from manufacturers’ websites and other relevant resources.

improved in plug-in hybrids that have the possibility of recharging their battery by an external grid.
The significant contribution of Electric vehicles in decreasing both oil dependency and CO2 emission have made them a hot topic and their technological trend in future is now one of the main discussions [6],[7],[8]. Although there are different opinions on when EVs will reach the point to replace gas engine vehicles in a greater scale, a common view point is that once EVs become less expensive, charging batteries become more convenient and performance factors like driving range improves, they will with no doubt have a disruptive impact on society and on the whole car manufacturing market [9].
What is the technology trend of electric vehicles and does any of the existing designs have the potential to dominate the electric vehicle market in future? The answers to these questions are now very critical for the main game players in the car industry.
I. INTRODUCTION
In 2012, Alexandra Tudorie, as her Master thesis, applied
Technology Forecasting using Data Envelopment Analysis (TFDEA) to predict the future of EV technology and determine whether any of the existing designs will be preferred over the others in market [16]. In her study the TFDEA model failed to provide acceptable results and the forecasts did not seem to be realistic. Similar to ill-chosen independent variables causing problems in linear regression [10], the accuracy of prediction in TFDEA is highly dependent on choosing the right parameters in the model [11]. However, unlike regression that has well-defined procedures like step wise regression to build the right model and also to diagnose its accuracy, there is no step by step method in selecting the proper parameters in TFDEA. Building the proper model in TFDEA is an iterative process in which the different sets of parameters proposed by the experts in the field are selected until the right model that yields acceptable results is built. The current study revises the model that Tudorie used, to find out whether enhancing the model can improve the accuracy of the TFDEA forecasts in EV industry. The results indicate that some changes in input and output parameters can significantly affect the accuracy of predictions and TFDEA can in fact be a reliable forecasting tool for the technological progress of electric vehicles.
Pressing environmental concerns and increased dependency on oil imports have revived interest in developing electric vehicles in the 1960s and 1970s [1].

  • Increased
  • manufacturing
  • possibilities
  • enabled
  • by

technological breakthroughs over the past two decades, combined with stringent environmental regulations, such as the ‘Zero Emissions Vehicles (ZEV) Act’ released by the California Air Resources Board in 1990, brought electric cars back on the market in the mid 1990s [2], [3]. Recent policies and goals set by U.S government like putting one million electric vehicles on the road by 2015 and significant tax credit for EV owners have brought attention to these vehicles more than ever [4].
Electric vehicles (EVs) can be broadly categorized as
“full-electric” (i.e. using only a battery and an electric motor for propulsion) and “hybrid-electric” (i.e. combining the conventional internal combustion engine with an electric motor and battery). Most of the big car manufacturers will have released over the past 15 years battery electric (BEV) and/ or hybrid electric vehicles (HEV) which are commercially available under different purchasing and leasing conditions. Despite the increasing trend in number of electric vehicles on the market, the overall percentage of electric vehicles on the road is still very small [5] due, mostly, to price related factors, performance, infrequency in charging stations, and consumers’ reluctance to embrace the new technology [2]. Electric vehicle technology is progressing every year in terms of better driving range and higher fuel economy. The range anxiety caused by pure electric vehicles has been eliminated by advent of hybrid electric cars. The fuel economy of the hybrid cars are greatly
II. RESEARCH METHODOLOGY
Technology forecasting is a method done by companies to predict the future trend of a specific product or technology in order to ensure their dominance in the market [11]. TFDEA is a forecasting tool that uses Data Envelopment Analysis

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2013 Proceedings of PICMET '13: Technology Management for Emerging Technologies.

(DEA) and leverages the benefits that DEA brings. DEA is a number of transistors [14]. Since then, TFDEA has been powerful method for performance measurement and applied in broad range of fields including telecommunication benchmarking using frontier method [11], [12] and has been protocols [15], [16], fighter jets [17], commercial airplanes extensively applied in organizational benchmarking [13]. [18], and LCDs [19]. There are also other studies in DEA uses a dataset including data points or Decision Making improving and extending TFDEA [20], [21].

  • Units (DMU), as in DEA terminology, and identifies the best
  • While DEA looks at the performance of the DMUs at one

performers by calculating the relative efficiency of the DMUs point in time, TFDEA looks into the change in the in the dataset. It then evaluates the performance of the other performance of the frontiers over a time period. TFDEA entities by comparing them against the best performers. This calculates a Rate of Change (RoC) by comparing the frontiers is a strong advantage compared to conventional methods like of each year with the ones in the previous year. The RoC is regression analysis that averages over all observations and then used to predict the performance of the frontiers or State does not allow identifying extreme points[11]. .Moreover, of the Art (SoA) in the future. The first step in this DEA can incorporate multiple parameters in measuring the methodology is to build a model using the proper set of performance which is very useful in fields like EVs where inputs and outputs through research and consults of experts in there are several performance measurements that are the field. This step is typically an iterative process until an important e.g. high acceleration rate and high fuel economy. appropriate set of parameters is ascertained. Once the Such characteristics have made TFDEA a strong forecasting accuracy of the model is determined, the calculated RoC will tool that can be applied in predicting the trend of products be used to project the trend of technology in the future years. where multiple performance indicators are imposed by The detailed mathematics of TFDEA can be found in [17].

  • different markets of the technology under study. TFDEA is,
  • Most of the data used in this study is from the original

however, very sensitive to selection of input and output dataset used in the former research by Tudorie. Additional parameters. Therefore it is essential to build a model with the input and output parameters are obtained from vehicle proper set of parameters [11]. Figure 1 illustrates a general manufactures’ websites and other resources. The dataset DEA model with multiple input and outputs and Equation 1 includes two types of hybrid electric cars; the regular hybrid

  • shows how efficiency is calculated.
  • and the plug-in hybrid electric vehicles. A hybrid electric

vehicle combines a conventional propulsion unit with a rechargeable energy storage system for a better fuel economy; yet batteries are charged using the internal fuel of the vehicle. A plug-in hybrid has the characteristics of a conventional hybrid electric car and in addition to the onboard electricity generation like HEVs, Plug-in hybrids can be plugged into electric outlets for recharging. They normally have a larger battery which allows them to drive in electric mode for a longer period of time. As such, PHEVs provide higher fuel efficiency [22].

Figure 1 General DEA/TFDEA model with multiple input and output parameters

III. TFDEA MODEL AND DATA COLLECTION

ꢊꢋꢌꢍꢎꢏꢋꢐ ꢑꢒꢓ ꢔꢕ ꢔꢒꢏꢖꢒꢏꢑ ꢗ

  • ꢀꢁꢁꢂꢃꢂꢄꢅꢃꢆ ꢇꢈꢉ
  • =

(1)

ꢘꢋꢌꢍꢎꢏꢋꢐ ꢑꢒꢓ ꢔꢕ ꢌꢙꢖꢒꢏꢑ ꢚ

A. Initial   Model used

Table 1 illustrates the initial TFDEA model that was used by Tudorie to forecast the future of hybrid electric vehicles and Table 2 defines each input and output parameter that was used in Tudorie’s TFDEA model.
In 2001, Anderson et al. first used TFDEA to forecast the trend of microprocessors and extended Moore’s law by recognizing that the production and performance tradeoffs for state of the art microprocessors is richer than simply the

TABLE 1 INITIAL TFDEA MODEL USED BY TUDORIE

Number of HEVs

  • Period
  • Initial TFDEA Model

  • Inputs
  • Outputs

  • 64
  • 1997-June 2012

Combined

output power
Battery capacity
CO2 emissions
Fuel economy

  • Weight
  • Acceleration rate

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2013 Proceedings of PICMET '13: Technology Management for Emerging Technologies.

TABLE 2 INPUT/OUTPUT PARAMETER DEFINITION OF THE INITIAL MODEL

  • Parameter
  • Definition

  • Weight (kg)
  • Total weight of the vehicle in kilogram

  • Combined output power (kw)
  • Combined output power delivered by electric motor and combustion engine together in

kilowatts
Battery capacity(kwh) Acceleration rate (km/h/s) CO2 emissions
The amount of electric charge a battery can store in kilowatthour. Indicates the speed in km/h a vehicle can reach in one second. The amount of CO2 released while traveling 1 kilometer in grams per kilometer Distance traveled per unit of fuel consumed on standard drive cycles in kilometers per liter
Fuel economy (km/l)

Tudorie thoroughly discusses the physics behind EVs’ model. Moreover, CO2 and fuel economy are both addressing dynamics, the internal combustion unit and the battery similar aspects in performance of hybrid vehicles.

  • technology. In her study, acceleration rate is selected as the
  • Weight of the car, battery capacity and combined output

main indicator of HEV’s performance. Fuel economy and power were the main variables affecting the acceleration rate CO2 emission are the performance measurements imposed by of the car based on physical and mechanical principles of

  • government regulations and were included in the model.
  • internal engines. While this argument is technically valid,

The current paper extends this work to reflect the varying whether these parameters are the right choices as input purposes of the different vehicles in the dataset. By parameters in a TFDEA model is under question. The initial definition the purpose of most vehicles is to transport people inputs might be valid choices in forecasting the technology of and goods and vehicles range from the large Chevy Tahoe main subsystems in the vehicle like battery technology.

  • sport utility HEV to small commuter vehicles. It is important
  • The main trade off that electric vehicle industry is facing

to include an output that reflects the Tahoe’s greater ability to to provide higher performance is the cost of manufacturing. transport people and goods – using a smaller commuter Tudorie also recognized the importance of incorporating the vehicle would require multiple trips. While interior volume cost of the car in the model, but she excluded that due to the would be a good measure, consistent measurement is complexities involved in obtaining such parameter. However, difficult. As a proxy for this greater capacity, total seating excluding the cost of manufacturing in the model results in capacity is used. For example, the Chevy Tahoe has a seating unfair comparisons where a company that makes an capacity of nine people while the Chevy Volt can only inexpensive car has it deemed obsolete or uncompetitive accommodate four. It is an admittedly imperfect proxy compared to a much more expensive car. Many customers though as the two new variations of the Toyota Prius, the may enjoy driving a Fisker Karma with its luxury design and compact Prius C and the larger Prius V both are listed as high acceleration rate, but they also should be willing to being five passenger vehicles despite the Prius V being much spend 100,000 US Dollars to purchase it. A solid model is the

  • larger.
  • one that properly reflects such trade-offs by its input and

Fuel economy and CO2 emissions are two factors that can output parameters. Manufacturers do not reveal their vary in different driving styles and weather conditions. The manufacturing costs and access to such information is not US Environmental Protection Agency (EPA) announces the feasible, therefore this study has used Manufacturer’s fuel efficiency of vehicles in miles per gallon and the study Suggested Retail Price (MSRP) as a proxy for manufacturing uses EPA values for fuel efficiency of cars available in US cost.

  • market. The fuel economy value for cars outside of US
  • Figure 2 illustrates the initial TFDEA model and the

market is obtained from manufacturer websites and test drive revised one with different inputs and outputs. The revised reviews. To avoid adding more inconsistency in parameter model is thoroughly explained in the next section. values, CO2 emission parameter is not included in the revised

Figure 2 Initial TFDEA Model vs. Revised Model

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2013 Proceedings of PICMET '13: Technology Management for Emerging Technologies.

TABLE 3 REVISED TFDEA MODEL FOR HYBRID ELECTRIC VEHICLES

Number of HEVs

  • Period
  • Revised TFDEA Model

  • Inputs
  • Outputs

Fuel economy
1997-June
2012
MAX of MPG and MPGequivalent
(mpg)
Seating capacity

  • 64
  • Price (MSRP)

(2012 equivalent US Dollars)
Acceleration rate

  • (km/h per second)
  • (mpg)

B. The   revised TFDEA Model

Fuel economy values from the original thesis are
Table 3 illustrates the revised TFDEA model and the new converted to their miles per gallon equivalent and are used in input and output parameters based on the discussion in the this study with an adjustment to the plug-in hybrid values.

  • previous section.
  • The concept of fuel economy is more complicated in plug-

in hybrids as they can drive in pure electric mode from having being charged off the grid. While this pure electric

Input parameters

Price: price is the only input parameter in the revised mode range is limited, it is often sufficient for commuting model. Manufacturer’s Suggested Retail Price (MSRP) is city vehicles. The original values in the dataset for plug-in considered as a reasonable proxy for manufacturing cost due hybrid vehicles have been replaced by their fuel economy in

  • to a high presumed correlation.
  • hybrid mode only. To recognize the additional dimension of

The vehicles in the dataset are from five different plug-in hybrids’ performance when driving in pure electric countries and are released in different years therefore the mode, another output of fuel economy is included in the actual MSRP for each car was converted into 2012 US Dollar model as described in the following. equivalent through the following steps: 1. The car’s MSRP in the year of release is found through Mile per Gallon equivalent (MPGe) for the plug-in hybrids the manufacturers’ website or car review websites. like Opel Ampra (also known as Chevrolet Volt in US). This
2. If the MSRP is in foreign currency, the value is converted value is based on the gasoline-equivalent energy of to the equivalent amount in US Dollars using the electricity [23]. Equation 5 is showing the kilo wattexchange rate of the year of release. This study used the hours of energy per gallon of gasoline:

Max of MPG and MPGequivalnet: EPA is announcing a

historical exchange rates provided by OANDA to do the conversions [18]. Equation 2 shows the formula to convert the MSRPs in the original currency to the US Dollar equivalent:

1 gallon of gasoline = 33.7 kwh .

(5)
For vehicles like BYD F3DM that are not available in US market and EPA has not announced an MPGe value for them, the MPGe is calculated using the electricity driving range in mile, the battery capacity in kwh and Equation 5. Equation 6 shows the calculation:

MSRPꢛꢜ ꢝꢔꢞꢞꢟꢠ ꢋꢡꢒꢌꢢꢟꢞꢋꢙꢏ

=

Exchange rateꢣꢋꢟꢠ ꢔꢕ ꢠꢋꢞꢋꢟꢑꢋ ∗ MSRP

.

(2)

ꢌꢙ ꢔꢠꢌꢍꢌꢙꢟꢞ ꢤꢒꢠꢠꢋꢙꢤꢣ

3. To inflate a past dollar value into present value the formula in Equation 3 is used by applying the historical Consumer Price Index (CPI) and the CPI of the current year. The CPI values are obtained from the Bureau of Labor Statistics [19] and the formula is described in [20]:

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    Status of LFP industry and market in China & its readiness to support the transport electrification Xiaoyu ZHANG, SynPLi Consulting Email: [email protected] Agenda Up-stream: LFP in China Mid-stream: LIB in China • LIB market: LIB for xEV application • Key LIB players assessment Down-stream: transport electrification • xEV market • Policy and strategy trend of main OEMs Conclusions and outlook Up-stream: LFP in China 54,000 ton in 2013 Pylon Hangsheng LFP in 2013: 3500 ton Up-stream: LFP in China China 17% More optimistic Less optimistic Cathode active materials in 2025: > 330 000 Tons ASSUMPTIONS : Portable devices: 2010-2025: +11% per year in volume World HEV 4,8 M HEV/year in 2020 - 35% LIB 6,8 M HEV in 2025 90% LIB P-HEV 0,4 M P-HEV/year in 2020, 0,7 M in 2025 100% LIB EV 1M EV/year in 2020, 1,5M/year in 2025 100% LIB Courtesy of Avicenne Energy LIB business revenues (10 8 RMB) 90 Mid-stream: LIB in China 80 70 Lishen 60 World: LIB demand @13CY ( 51,500 MWh) 50 ATL 40 BAK Others 30 BYD Nalon 20 Coslight Wisewod 10 Mcnair 0 Great power 2008 2009 2010 2011 2012 2013 TCL Optimum China LIB supply @13CY ( 17,650 MWh) Veken Lishen Gxgk Po B&K ATL Oceansun Pr BYD UTL BAK HYB Cy AEE Coslight Tianmao 0,00 200,00 400,00 600,00 800,00 1000,00 First First Coslight BAK China LIB export BYD 4,0% @13CY: 7% ATL Japan Lishen Korea LEJ 25,9% 27,2% AESC Cy Pr Po&La China Hitachi Others Sony 42,9% Panasonic LG Chem SDI Courtesy of RealLi Research China LIB demand @13CY: 14000MWh Mid-stream: LIB in China China LIB production @13CY (unit:
  • Electric Power Research Institute, Inc

    Electric Power Research Institute, Inc

    Plug-In Electric Vehicles and Infrastructure Florida State Projections 2010 – 2030 Mark Duvall Director, Electric Transportation and Energy Storage Florida PSC Workshop on EV Charging Stations September 6, 2012 Nationwide Plug-In Electric Vehicle Sales (As of June 30, 2012) To Do- Add Tesla Roadster into Other Sources: Automotive News, hybridcars.com © 2012 Electric Power Research Institute, Inc. All rights reserved. 2 The Introduction of PEVs is Outpacing the Intro of HEVs (More models, greater sales numbers) Source: US Department of Energy © 2012 Electric Power Research Institute, Inc. All rights reserved. 3 BMW ActiveE trial BMW i3 Launch 2013 Audi A1 E-Tron Chevrolet Volt since begins 2012 Launch 2014 2010 BYD e6 Launch 2013 CODA Sedan Launch BMW i8 Launch 2014 Nissan LEAF since BYD F3DM Launch Early 2012 Cadillac ELR Launch 2010 2013 Ford Focus Electric Ford C-MAX Energi Chevrolet Spark 2014 Tesla Roadster since Launch Early 2012 Launch Late 2012 Launch 2013 Hyundai BlueOn 2008 Launch 2014 Fisker Karma Honda Fit EV Launch Ford Fusion Energi Launch 2011 Late 2012 Launch 2013 Kia Ray Launch 2014 2012 2013 2014 Mitsubishi I Launch Tesla Model X Launch Mitsubishi Px-MiEV Early 2012 Scion iQ Launch 2013 Launch 2014 Late 2012 smart fortwo electric Toyota FT-EV Launch Volkswagen E-Bugster drive Launch 2012 2013 Launch 2014 Tesla Model S Toyota RAV4 EV Volkswagen E-Golf Launches Mid-2012 Launch 2013 Launch 2014 Toyota Plug In Prius Volvo C30 Electric Volvo V70 PHEV Source: Launches Early 2012 Launch 2013 Launch 2014 EEI © 2012 Electric Power Research Institute, Inc.
  • Betting on Chinese Electric Cars? – Analysing BYD's Capacity For

    Betting on Chinese Electric Cars? – Analysing BYD's Capacity For

    Int. J. Automotive Technology and Management, Vol. 10, No. 1, 2010 77 Betting on Chinese electric cars? – analysing BYD’s capacity for innovation Hua Wang* and Chris Kimble Euromed Management, Marseille, Domaine de Luminy, BP 921, 13288 Marseille, Cedex 9, France E-mail: [email protected] E-mail: [email protected] *Corresponding author Abstract: This article will examine some of the reasons why the automobile industry in China has become the subject of so much interest in recent years. In particular, it will focus on its capacity for innovation through an in-depth study of one company: the BYD group. The article will examine the growth of the group and trace the development of the innovative strategies that have helped it to become a significant player in the electric car market. It will highlight three particular levels at which innovation has taken place, the organisational, human resource management and technological levels, and will analyse how these innovations interrelate to the overall breakthrough strategy of BYD. The article concludes with some observations about the capacity of BYD to continue to innovate, prosper and grow using its existing strategy. Keywords: breakthrough strategy; BYD; China; electric cars; innovation; innovative capabilities. Reference to this paper should be made as follows: Wang, H. and Kimble, C. (2010) ‘Betting on Chinese electric cars? – analysing BYD’s capacity for innovation’, Int. J. Automotive Technology and Management, Vol. 10, No. 1, pp.77–92. Biographical notes: Hua Wang is an Associate Professor of Innovation Management and Managerial Economics at Euromed Management, Marseille, France.
  • Electric Vehicles | CHINA

    Electric Vehicles | CHINA

    Electric vehicles | CHINA INDUSTRIALS / AUTOS & AUTO PARTS NOMURA INTERNATIONAL (HK) LIMITED NEW Yankun Hou +852 2252 6234 [email protected] THEME Action Stocks in focus We believe that various electric vehicles (xEV) are the ultimate solution for the We believe the EV theme will sustainability of the global auto industry. We think current EV technology is not support BYD’s share price, although sophisticated enough to compete with the internal combustion engine, but can be we find it difficult to see upside from applied to niche markets. Penetration in niche markets will probably depend on here without clearer milestones; CSR government policy. We are cutting our rating for BYD to NEUTRAL (from Buy) on could benefit due to its strong R&D possible slower sales of EV products in 2011 and a demanding valuation. We think ability in EV buses. WATG, Tianneng Power, A123, Ningbo Yunsheng and CSR (NEUTRAL) have exposure to the EV theme. Price Catalysts Stock Rating Price target BYD (1211 HK) NEUTRAL 42.75 40.00 Government policies on EV; auto sales volume. CSR (1766 HK) NEUTRAL 10.78 11.20 Anchor themes Downgrading from Buy. Cutting PT. We think the niche auto market, including buses, taxis, and LSEVs, provides the Closing prices as of 12 January 2011; local currency first entry point for EV producers. So near and yet so far Analysts Yankun Hou +852 2252 6234 Technology ready to take off as a niche product [email protected] We believe the current EV technology cannot compete with the conventional internal combustion engine (ICE) on driving experience, but that it is ready to be Ming Xu applied to niche markets, though the speed of penetration depends on government +852 2252 1569 commitment.
  • Analysis of Integration of Plug-In Hybrid Electric Vehicles in the Distribution Grid

    Analysis of Integration of Plug-In Hybrid Electric Vehicles in the Distribution Grid

    Analysis of Integration of Plug-in Hybrid Electric Vehicles in the Distribution Grid Ahmad Karnama Master of Science Thesis Stockholm, Sweden 2009 Analysis of Integration of Plug-in Hybrid Electric Vehicles in the Distribution Grid Ahmad Karnama Thesis for rewarding Master of Science Degree in Electric Power Engineering from School of Electrical Engineering at Royal Institute of Technology (KTH), Stockholm, Sweden. Involved Academic Divisions: Electric Power Systems Electrical Machines and Power Electronics Academic Supervisors: Professor Lennart Söder Professor Stefan Östlund Industrial Partner: Fortum Distribution AB Industrial Supervisors: Mr. Olle Hansson Mr. Jonas Tosting Examiner: Professor Lennart Söder [II] در ر وزه ری رم دوش ددم دو ھزار وزه و و وش وزه رآورد روش و وزه ر و وزه ر و وزه روش Once, in a potter's shop, a company Of cups in converse did I chance to see, And lo! One lifted up his voice, and cried, "Who made, who sells, who buys this crockery? " 1 ,born 1048 AD, Neyshapur, Iran—1123 AD, Neyshapur) ,( ر م :From Omar Khayyam (Persian 1 Iran), a Persian polymath, mathematician, philosopher, astronomer and poet. [III] [IV] Abstract The new generation of cars are so-called Plug-in Hybrid Electric Vehicles (PHEVs) which has the grid connection capability. By the introduction of these vehicles, the grid issues will be connected to the private car transportation sector for the first time. The cars from the gird perspective can be considered as a regular load with certain power factor. The effects of this type of new load in distribution grid are studied in this thesis.