Forecasting Hybrid Electric Vehicles Using TFDEA

Portland State University <a href="/goto?url=https://pdxscholar.library.pdx.edu/" target="_blank">PDXScholar </a><a href="/goto?url=https://pdxscholar.library.pdx.edu/etm_fac" target="_blank">Engineering and Technology Management </a><a href="/goto?url=https://pdxscholar.library.pdx.edu/etm_fac" target="_blank">Faculty Publications and Presentations </a> <a href="/goto?url=https://pdxscholar.library.pdx.edu/etm" target="_blank">Engineering and Technology Management </a>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] Follow this and additional works at: <a href="/goto?url=https://pdxscholar.library.pdx.edu/etm_fac?utm_source=pdxscholar.library.pdx.edu%2Fetm_fac%2F38&utm_medium=PDF&utm_campaign=PDFCoverPages" target="_blank">https:</a><a href="/goto?url=https://pdxscholar.library.pdx.edu/etm_fac?utm_source=pdxscholar.library.pdx.edu%2Fetm_fac%2F38&utm_medium=PDF&utm_campaign=PDFCoverPages" target="_blank">/</a><a href="/goto?url=https://pdxscholar.library.pdx.edu/etm_fac?utm_source=pdxscholar.library.pdx.edu%2Fetm_fac%2F38&utm_medium=PDF&utm_campaign=PDFCoverPages" target="_blank">/</a><a href="/goto?url=https://pdxscholar.library.pdx.edu/etm_fac?utm_source=pdxscholar.library.pdx.edu%2Fetm_fac%2F38&utm_medium=PDF&utm_campaign=PDFCoverPages" target="_blank">pdxscholar.library.pdx.edu/etm_fac </a> Part of the <a href="/goto?url=http://network.bepress.com/hgg/discipline/217?utm_source=pdxscholar.library.pdx.edu%2Fetm_fac%2F38&utm_medium=PDF&utm_campaign=PDFCoverPages" target="_blank">Engineering Commons </a><a href="/goto?url=http://library.pdx.edu/services/pdxscholar-services/pdxscholar-feedback/?ref=https://pdxscholar.library.pdx.edu/etm_fac/38" target="_blank">Let us know how access to this document beneﬁts you. </a>Citation Details 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: <a href="mailto:[email protected]" target="_blank">[email protected]</a>. 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]. <ul style="display: flex;"><li style="flex:1">Increased </li><li style="flex:1">manufacturing </li><li style="flex:1">possibilities </li><li style="flex:1">enabled </li><li style="flex:1">by </li></ul>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 2098 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]. <ul style="display: flex;"><li style="flex:1">Units (DMU), as in DEA terminology, and identifies the best </li><li style="flex:1">While DEA looks at the performance of the DMUs at one </li></ul>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]. <ul style="display: flex;"><li style="flex:1">different markets of the technology under study. TFDEA is, </li><li style="flex:1">Most of the data used in this study is from the original </li></ul>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 <ul style="display: flex;"><li style="flex:1">shows how efficiency is calculated. </li><li style="flex:1">and the plug-in hybrid electric vehicles. A hybrid electric </li></ul>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 <ul style="display: flex;"><li style="flex:1">ꢀꢁꢁꢂꢃꢂꢄꢅꢃꢆ ꢇꢈꢉ </li><li style="flex:1">=</li></ul>(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 <ul style="display: flex;"><li style="flex:1">Period </li><li style="flex:1">Initial TFDEA Model </li></ul><ul style="display: flex;"><li style="flex:1">Inputs </li><li style="flex:1">Outputs </li></ul><ul style="display: flex;"><li style="flex:1">64 </li><li style="flex:1">1997-June 2012 </li></ul>Combined output power Battery capacity CO2 emissions Fuel economy <ul style="display: flex;"><li style="flex:1">Weight </li><li style="flex:1">Acceleration rate </li></ul>2099 2013 Proceedings of PICMET '13: Technology Management for Emerging Technologies. TABLE 2 INPUT/OUTPUT PARAMETER DEFINITION OF THE INITIAL MODEL <ul style="display: flex;"><li style="flex:1">Parameter </li><li style="flex:1">Definition </li></ul><ul style="display: flex;"><li style="flex:1">Weight (kg) </li><li style="flex:1">Total weight of the vehicle in kilogram </li></ul><ul style="display: flex;"><li style="flex:1">Combined output power (kw) </li><li style="flex:1">Combined output power delivered by electric motor and combustion engine together in </li></ul>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. <ul style="display: flex;"><li style="flex:1">technology. In her study, acceleration rate is selected as the </li><li style="flex:1">Weight of the car, battery capacity and combined output </li></ul>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 <ul style="display: flex;"><li style="flex:1">government regulations and were included in the model. </li><li style="flex:1">internal engines. While this argument is technically valid, </li></ul>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. <ul style="display: flex;"><li style="flex:1">sport utility HEV to small commuter vehicles. It is important </li><li style="flex:1">The main trade off that electric vehicle industry is facing </li></ul>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 <ul style="display: flex;"><li style="flex:1">larger. </li><li style="flex:1">one that properly reflects such trade-offs by its input and </li></ul>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. <ul style="display: flex;"><li style="flex:1">market. The fuel economy value for cars outside of US </li><li style="flex:1">Figure 2 illustrates the initial TFDEA model and the </li></ul>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 2100 2013 Proceedings of PICMET '13: Technology Management for Emerging Technologies. TABLE 3 REVISED TFDEA MODEL FOR HYBRID ELECTRIC VEHICLES Number of HEVs <ul style="display: flex;"><li style="flex:1">Period </li><li style="flex:1">Revised TFDEA Model </li></ul><ul style="display: flex;"><li style="flex:1">Inputs </li><li style="flex:1">Outputs </li></ul>Fuel economy 1997-June 2012 MAX of MPG and MPGequivalent (mpg) Seating capacity <ul style="display: flex;"><li style="flex:1">64 </li><li style="flex:1">Price (MSRP) </li></ul>(2012 equivalent US Dollars) Acceleration rate <ul style="display: flex;"><li style="flex:1">(km/h per second) </li><li style="flex:1">(mpg) </li></ul>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. <ul style="display: flex;"><li style="flex:1">previous section. </li><li style="flex:1">The concept of fuel economy is more complicated in plug- </li></ul>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 <ul style="display: flex;"><li style="flex:1">to a high presumed correlation. </li><li style="flex:1">hybrid mode only. To recognize the additional dimension of </li></ul>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]:

Forecasting Hybrid Electric Vehicles Using TFDEA

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