Hybrid-Electric Passenger Car Carbon Dioxide and Fuel Consumption

Jul 14, 2015 - Hybrid-electric vehicles (HEVs) have lower fuel consumption and carbon dioxide (CO2) emissions than conventional vehicles (CVs), on ave...
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Hybrid-Electric Passenger Car Carbon Dioxide and Fuel Consumption Benefits Based on Real-World Driving Britt A. Holmén*,† and Karen M. Sentoff‡ †

School of Engineering, University of Vermont, 33 Colchester Ave, Burlington, Vermont 05405, United States Transportation Research Center, University of Vermont, 210 Colchester Ave, Burlington, Vermont 05405, United States

‡

S Supporting Information *

ABSTRACT: Hybrid-electric vehicles (HEVs) have lower fuel consumption and carbon dioxide (CO2) emissions than conventional vehicles (CVs), on average, based on laboratory tests, but there is a paucity of real-world, on-road HEV emissions and performance data needed to assess energy use and emissions associated with real-world driving, including the effects of road grade. This need is especially great as the electrification of the passenger vehicle fleet (from HEVs to PHEVs to BEVs) increases in response to climate and energy concerns. We compared tailpipe CO2 emissions and fuel consumption of an HEV passenger car to a CV of the same make and model during real-world, on-the-road network driving to quantify the in-use benefit of one popular full HEV technology. Using vehicle specific power (VSP) assignments that account for measured road grade, the mean CV/HEV ratios of CO2 tailpipe emissions or fuel consumption defined the corresponding HEV “benefit” factor for each VSP class (1 kW/ton resolution). Averaging over all VSP classes for driving in all seasons, including temperatures from −13 to +35 °C in relatively steep (−13.2 to +11.5% grade), hilly terrain, mean (±SD) CO2 emission benefit factors were 4.5 ± 3.6, 2.5 ± 1.7, and 1.4 ± 0.5 for city, exurban/suburban arterial and highway driving, respectively. Benefit factor magnitude corresponded to the frequency of electric-drive-only (EDO) operation, which was modeled as a logarithmic function of VSP. A combined model explained 95% of the variance in HEV benefit for city, 75% for arterial and 57% for highway driving. Benefit factors consistently exceeded 2 for VSP classes with greater than 50% EDO (i.e., only city and arterial driving). The reported HEV benefits account for real-world road grade that is often neglected in regulatory emissions and fuel economy tests. Fuel use HEV benefit factors were 1.3 and 2 for the regulatory highway (HWFET) and city (FTP) cycles, respectively, 18% and 31% higher than the EPA adjusted fuel economy values. This study establishes the significant need for high-resolution vehicle activity and road grade data in transportation data sets to accurately forecast future petroleum and GHG emissions savings from hybridization of the passenger vehicle fleet.

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INTRODUCTION The on-road vehicle fleet accounts for over 30% of U.S. greenhouse gas emissions, with major contributions from lightduty gasoline vehicles.1,2 Facilitated by fuel economy, energy, and climate legislation,1−3 light-duty hybrid-electric vehicle (HEV) sales have increased to 3% of the on-road passenger car fleet since their large-scale U.S. introduction in 2000.4,5 The number of HEVs and other alternative vehicles are expected to increase to nearly 50% in 20406 to meet ever more stringent energy and climate policies.7 HEVs reduce fuel consumption and, correspondingly, tailpipe emissions, using strategies such as electric assist of the internal combustion engine (ICE), ICEoff idle, electric-drive-only (EDO) low-speed propulsion and regenerative braking.8 There is very little quantitative data on the fuel and emissions benefits of HEVs during real-world driving. Quantitative data on actual, real-world, in-use HEV carbon dioxide (CO2) emissions and fuel consumption are essential to validate projections on transportation energy use6,9 and for accurate regulatory mobile source emission models such as EPA’s Motor Vehicle Emission Simulator (MOVES).10 Prior studies are not © 2015 American Chemical Society

well-suited to forecast the magnitude of current-technology HEVs on transportation energy use because the data were from laboratory studies of one or two early model HEVs (i.e., Toyota Prius, Honda Insight) compared to conventional vehicles (CVs) of dissimilar make/model and vehicle size. Quantifying the actual in-use gains attainable from implementing HEV technology is enabled by on-road emissions data collected comparatively for CV/HEV passenger cars of identical make/ model on the same route in consistent traffic conditions in order to study the real-world advantages of the hybrid platform itself without complication of aerodynamic and engine/weight/ materials differences. These measurements only became possible with the availability of portable emissions measurement equipment and model year 2007 vehicles available in both the conventional and hybrid platform for the same vehicle chassis and body aerodynamics. Received: Revised: Accepted: Published: 10199

March 12, 2015 July 1, 2015 July 14, 2015 July 14, 2015 DOI: 10.1021/acs.est.5b01203 Environ. Sci. Technol. 2015, 49, 10199−10208

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Environmental Science & Technology

emissions and VSP are reflected in the latest EPA regulatory model for mobile source emissions, MOVES, which uses VSP and vehicle speed to forecast emissions from the on-road vehicle fleet.10 Laboratory measurements of fuel consumption from various model HEVs have shown a dependency on HEV technology,40 drive cycle11,41 and ambient temperature.11,14,42,43 For example, HEVs showed up to three times higher fuel consumption for aggressive, stop-and-go drive cycles compared to highway driving.11 Federal Test Procedure (FTP) fuel consumption rate comparisons for 7 MY 2000 vehicles showed the Toyota Prius rate (3.97 L/100km) was about half that of the 3 tested gasoline direct-injection vehicles (7.62, 8.53, and 10.4 L/ 100km) and comparable to that for 3 diesel direct-injection vehicles (3.38, 4.08, and 5.32 L/100km).44 Thus, HEVs offer up to two times the fuel consumption improvement compared to gasoline CVs, and HEV fuel consumption varies with operating mode. Four prior studies (See Supporting Information (SI) Table S-1) compared the performance of CV to HEV,11,14,40,44 but each of these studies had shortcomings that do not enable quantification of HEV benefits that can be used for modeling any type of real-world driving. These shortcomings included: (i) the compared CV was of different make/model/model year or aerodynamics;11,44 (ii) compared CV was manual transmission40 and thus too dependent on driver behavior; and (iii) comparative analysis was only conducted at the trip or full drive-cycle level and therefore not amenable to quantifying HEV benefit as a function of operating modes such as those based on VSP.11,14,42,44−46 Thus, data are still lacking to quantify the benefit of HEVs for various modes of operation during real-world driving, especially for HEV comparison to the same make and model CV. To fill this gap, this study examines one full HEV compared to its CV counterpart under real-world driving over all seasons in northern Vermont.

We compare real-world tailpipe emissions data for one HEV to the same manufacturer and model year CV counterpart to quantify the CO2 emissions and fuel consumption benefits of the HEV during real-world driving across all northern Vermont seasons on terrain that includes steep and rolling hills. Steep positive road grade and cold ambient temperatures both adversely affect HEV operation.11,12 Our results with one vehicle pair demonstrate that similar studies can be used to develop robust models of all types of HEV platforms under actual driving conditions and improve quantitative estimates of the future fleet contributions to the U.S. CO2 emissions inventory and petroleum consumption. Studies such as these are critical as HEVs and other electrified vehicles (i.e., PHEVs and BEVs) comprise a larger portion of the on-road fleet. Measuring Real-World Emissions. For decades it has been recognized that laboratory testing of vehicle emissions does not accurately reflect the full range of real-world emissions due to laboratory equipment constraints11−13 and shortcomings in accounting for real-world environmental conditions. Realworld testing is especially important for HEVs where engine-off and battery energy storage technologies are affected by ambient conditions such as temperature14 and road grade12,15 over a trip. Portable emission measurement systems (PEMS) enable second-by-second, on-board measurements of tailpipe exhaust emissions during real-world vehicle operation on the road network.13,16 In addition to tailpipe exhaust measurements, PEMS typically collect data on vehicle location, engine/vehicle operating parameters and driving conditions. Thus, PEMS data is the only reliable way to connect real-world driving activity with associated tailpipe emissions. Prior PEMS studies have quantified regulated, gas-phase emissions of carbon monoxide, hydrocarbons and NOx,15,17,18 speciated gases by Fourier transform infrared (FTIR) spectroscopy,19−27 and ultrafine particles,28,29 from CV and some HEV passenger cars. These studies associated hard acceleration events, uphill road grade, aggressive driver behavior, and low ambient temperature with increased short duration, high concentration emissions events.19,20 For HEVs, high hydrocarbon11 and particle number28,30 emissions occurred during engine reignition after ICE-off operation. Engine-off HEV operation resulted in tailpipe CO2 concentrations equivalent to ambient background CO2 and the frequency of ICE-off operation varied with drive cycle, ranging from 7 to 66% for the Toyota Prius.11 High power demand events during simulated drive cycles were associated with increased emission rates,12 including elevated CO2 emission rates for both Prius and Ford Escape HEVs.11 Prior emissions studies identified road grade as an important contributing factor to accurately quantifying real-world emissions, but few prior PEMS studies reported real-world road grade, and the majority of grade effect studies tested CVs,31−35 with few on-road emission results for HEVs.12 Grade was identified as an important contributing factor to computing vehicle specific power (VSP),12 the instantaneous (1 Hz) power required to overcome all driving resistances.36 One HEV modeling study used 2001 Toyota Prius dynamometer data to estimate order of magnitude increases in CO2 emission rates (0.5−6 g/sec) for VSP from less than −2 to 19 kW/ton, but this study lacked data for VSP above 19 kW/ton due to laboratory equipment limitations.12 High temporal resolution PEMS data are well-suited for analysis by VSP especially when combined with accurate global position system (GPS) data that enables joining of location-specific road grade37−39 for use in the VSP calculation. The relationships between fuel use,

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MATERIALS AND METHODS A brief summary of the methods used in this study is provided here, more detailed information regarding the instrumentation and sampling plan can be found in the SI. Our total on-board tailpipe emissions measurement system (TOTEMS) instrumentation suite25,27−30 was used to collect 1-Hz vehicle performance and emissions data from two 2010 Toyota Camry vehicles (one CV, one HEV; see SI Table S-3 for vehicle specifications) over an 18-month sampling period. A single driver (24 year old female) operated both vehicles along the same figure-eight shaped route, comprised of 25 km “outbound” driving through downtown Burlington, Vermont (“city”) and interstate highway I-89 to Richmond, VT (“highway”) and returning via 26.5 km of rural and suburban arterial roads (“inbound” section, all arterial) driving.47 The route covered 51.5 km (32 miles) in Chittenden County, Vermont (SI Figure S1) over terrain with road grades varying from −13.2 to 11.5% with the majority of travel between ±4% grade (SI Figure S2). Air conditioning and auxiliary power (cabin temperature, fan) settings were identical for both vehicles throughout the study. Daily quality control procedures involved collection of instrument and tunnel blanks prior to and after running the test route, a warm-up route with a steep grade section to ensure the vehicles were under hot stabilized operation prior to testing, and routine instrument calibration checks using certified gases. 10200

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ambient air for each pollutant, p. Pollutant emission rates, ERp, were computed from background-corrected FTIR concentrations, the temperature-corrected exhaust flow rate (Q, L/s), sample temperature (T, K), and pressure (P, atm), the molecular weight of each pollutant (MWp, g/mol), and the universal gas constant (R = 0.0821 L atm mol−1 K−1), according to eq 2 (see detail in reference27).

A Toyota Techstream scantool connected to the vehicle’s ECU acquired vehicle speed, acceleration, engine speed (RPM), and HEV battery state-of-charge (SOC); an Onset HOBO U23−001 logger recorded ambient temperature and relative humidity; the MKS Inc. MultiGas 2030 high-speed FTIR spectrometer, a high-speed instrument validated for onvehicle applications48 measured gas-phase pollutant concentrations (see Tables S-4 and S-5 for instrument settings); a pitot tube and four differential pressure transducers quantified exhaust flow rate; and Geologger and Garmin GPS receivers recorded vehicle location. Tailpipe flow rate and raw emissions data were time-aligned with GPS and scantool data based on the highest Pearson’s correlation coefficient for temporally lagged data up to ±60 s, computed using Matlab version 7.10 (2010a). Road grade was measured to the nearest 0.1% at 3 m intervals by the Vermont Agency of Transportation (VTrans) using gyroscope and sonar systems of the Fugro Roadware Automated Roadway Analyzer (ARAN) and joined to the second-by-second TOTEMS data based on GPS location using ESRI ArcGIS version 10.0 software. The recorded 1 Hz data for vehicle speed (v), acceleration (a), road grade (grade) were combined with vehicle constants− coefficient of drag (CD), vehicle cross-sectional area (A), and vehicle mass (m), a rotating mass factor (ε), coefficient of rolling resistance (CR) − as well as air density (ρa), computed from measured air temperature, to quantify instantaneous VSP using light-duty vehicle constants from Jimenez (1999)36 to enable comparisons between vehicles in VSP classes of 1 kW/ ton resolution. The mass used in eq 1 was the sum of the curb weight of each vehicle (CV 1533 kg; HEV 1673 kg) plus the mass of TOTEMS instrumentation and personnel (463 kg total), but did not account for removal of the rear seats, spare tire, jack and trunk floor. Both vehicles were tested at their full load capacity. VSP(kW/ton) = (1 + ε) ·v ·a + g · + 1/2·ρa ·

C D·A 3 ·v + g ·CR ·v m

⎛ g ⎞ [Cp − C bkgP]MWp·P ER p⎜ ⎟ = ·Q ⎝s⎠ R·T

(2)

Based on carbon balance, the instantaneous (1 Hz) fuel consumption rate (FCR, liters per second, eq 3) was computed using background-corrected tailpipe emission rates (g/s) of carbon dioxide (CO2), carbon monoxide (CO), and propane (C3H8) assuming 639.6 g of carbon per liter of gasoline.49 The distance-based FCR (volume of fuel used per 100 kilometer driven) was computed by dividing FCR by the measured instantaneous vehicle speed (kps) and converting to units of L/ 100 km. FCR = fuel consumption rate(L /s) =

(12/44)ER CO2 + (12/28)ER CO + (36/44)ER C3H8 639.6 (3)

( Ls ) 100(km) ( kms ) 100(km)

FCR ⎛ L ⎞ ⎟ = FCR⎜ ⎝ 100km ⎠ speed

(4)

The “HEV benefit” was computed as the ratio of the mean CV emission rate (or FCR) to the mean HEV emission rate (or FCR) within a given VSP class. Thus, for a CV/HEV ratio greater than 1, the “HEV benefit” represented the factor by which CO2 emissions (or fuel consumption) from the CV exceeded that of the HEV. The two vehicles were alternately instrumented and 75 total runs were completed. Data preprocessing was performed to remove runs with missing data due to instrument malfunction. The resulting analysis data set consisted of 73,307 1 Hz records for the CV and 118,675 HEV records. For this subset of data, temperature and relative humidity (%) ranged from −13 to 35 °C and 19 to 82% for the HEV and −6 to 34 °C and 25 to 80% for the CV. Statistical analyses were conducted using JMP 10.0 and curvefitting using Matlab 8.1 (2013a) software and errors were propagated (SI eq S4) for the HEV benefit ratio calculations based on the standard deviations measured in each VSP class for each vehicle over all runs on a specific road type.

grade ·v 100 (1)

The Camry HEV can shutdown the ICE to achieve fuel consumption and emissions benefits during low-speed, lowload operation. This ICE-off condition was indicated by zero values of the HEV mass air flow rate (MAF) estimate computed from scantool RPM measurements.27 In practical terms, the ICE-off condition was assigned for RPM < 775.4 and included instances of ICE operation that led up to complete shutdown (at RPM = 0). The percent electric-drive-only operation (%EDO) was determined by computing the percent time in instantaneous ICE-off status for each VSP class (1 kW/ton). Carbon Dioxide Emission Rate. Exhaust sampled from a tailpipe probe was transported via heated line (191 °C) into the sampling cell of the MKS MultiGas 2030 high-speed FTIR at 12 L/min to enable 1 Hz records. The FTIR simultaneously quantified 31 gas-phase pollutant concentrations based on manufacturer calibration curves using select absorbance regions. Before and after each sampling run, an instrument blank was acquired while the sample cell was purged with dry nitrogen gas to verify proper signal alignment and maximal signal-to-noise instrument response across the 500 to 5000 cm−1 wavenumber region. A 10 min tunnel blank (TB) was collected before and after each sampling run to obtain background concentration (CbkgP, % or ppm) in the sampling system and surrounding

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RESULTS AND DISCUSSION Vehicle Activity and Ambient Conditions Comparison. We confirmed that use of a single driver and test route resulted in repeatable, similar operation of each vehicle across all runs of the real-world driving route (Figure 1). Slight differences observed (0.5−1%) between vehicles were due to traffic signal timing and the number of runs per vehicle (Figure 1B). Based on one-way analyses of practical equivalence between vehicle types, mean ambient temperatures were within 4 °C (see SI Figure S2) and relative humidity within 2% despite sampling over different numbers of days each season. While temperature is known to affect HEV performance and emissions, hot-stabilized operation may be less sensitive to 10201

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Figure 1. (A) Example second-by-second driving route vehicle speed, acceleration, road grade and VSP from a single test run of the CV (blue) and HEV (orange) vehicles. (B) VSP histograms for the CV (blue) and the HEV (orange) over all test runs by inbound and outbound segments. The ±30 VSP classes include all operation greater/less than 30 kW/ton, the VSP class with limited numbers of observations. Inset table shows full route VSP descriptive statistics. Error bars are 95% confidence intervals.

Figure 2. Mean ERCO2 (g/s), distance-based fuel consumption rate (FCR, L/km), and HEV battery state-of-charge (SOC, %) for the CV (blue, open circles) and HEV (orange, filled circles) by VSP (1 kW/ton class resolution) and driving route road types. Error bars represent one standard error. Both ±30 VSP classes include all operation greater/less than 30 kW/ton. The extreme values in FCR (L/km) at VSP = 0 for all road types are due dividing FCR (L/s) by vehicle speed (see eq 3).

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Environmental Science & Technology Table 1. HEV Operation on Each Road Type Including Percent Electric-Drive-Only (%EDO) Model speed (kph)

a

HEV ICE operation

VSP > 0 %EDO modela

VSP < 0 %EDO

facility

mean ± std dev

off

restart

stable

city arterial highway

22.9 ± 16.8 46.5 ± 21.0 100.2 ± 19.0

55% 43% 4%

25% 22°% 2%

20% 35% 94%

8.1 3.6 1.6

a

b

c

R2

RMSE

0.763 0.729 0.041

−1.21 −1.11 −0.06

0.469 0.4149 0.025

0.948 0.958 0.972

0.041 0.035 0.002

EDO [%] = a + b (LogVSP) + c (LogVSP)2

temperature.43,47 The near equivalency in vehicle activity data indicate that one would not be sacrificing vehicle performance to achieve the fuel or CO2 emission benefits anticipated for driving the HEV even in this steep terrain. Further, any measurable differences between HEV and CV emissions or fuel consumption are unlikely to be due to bias in sampling conditions (i.e., road grade, temperature, relative humidity, traffic, idle time, etc.) along the driving route during testing of each vehicle, despite driving on different days. With comparable activity and driving conditions data for the two vehicles over the study, the CO2 emissions and fuel consumption benefit of driving the HEV was quantified with confidence that the data were not biased toward one vehicle’s performance. For modeling purposes, the VSP distributions for the real-world highway, city and arterial portions of the driving route may be considered somewhat similar to the HWFET, FTP and US06 regulatory dynamometer drive cycles, respectively (SI Figure S3). Carbon Dioxide Emission and Fuel Consumption Rates. Carbon dioxide emission rates (ERCO2, mean g/s ± standard error) were on average higher for the CV (4.18 ± 0.02) than the HEV (2.80 ± 0.01) when compared over the full data set. For both vehicles, the expected relationship between ERCO2 and VSP was observed: ERCO2 was generally low and constant (30, 28, and 21 kW/ton, respectively. Thus, as the road type speed limit increased (Table 1), the HEV provided a CO2 emissions advantage over a narrower range of VSP operation. Only at high VSP > 20 kW/ ton for highway operation, where HEV battery state-of-charge (SOC) dropped to the lowest measured values (about 60%, see Figure 2), did the HEV ERCO2 and FCR exceed that for the CV with statistical significance (SI Table S-6). From the HEV specifications (SI Table S-3), the computed maximum VSP operating point for operating the vehicle solely on ICE was 65 kW/ton for the test vehicle weight, well in excess of the observed crossover points for the full route. Different crossover points likely reflect the relative degree to which the electric motor is either contributing to propulsion or to recharging the HEV battery at a given VSP class in order for the HEV to operate the ICE most efficiently (i.e., minimize fuel

consumption). The maximum VSP based on electric motor horsepower alone was 13.9 kW/ton at the test vehicle weight. This value corresponds quite well to the maximum VSP with observed EDO behavior, as described below. HEV Benefits. For each VSP class, we computed the CV/ HEV ratio of ERCO2; values greater than 1.0 indicate there was a CO2 emissions benefit associated with driving the HEV in place of the CV (Figure 3). Over the full route, the mean HEV CO2 benefit ranged from a factor of 0.9 (a 10% “disbenefit” compared to the CV) at high VSP to a factor of 6.4 (an improvement over the CV) at idle operation (0 kW/ton). Dividing the route by road type sections (Figure 3), the mean HEV CO2 benefits, computed by averaging over all VSP classes, were 4.5 ± 3.6 for city, 2.5 ± 1.7 for arterial and 1.4 ± 0.5 for highway driving. The higher CO2 benefit for city driving is expected due to the higher proportion of stop-and-go driving that favors any full HEV platform capable of turning off the ICE.11,12,41,42 The actual real-world HEV benefits over any trip would be adjusted to account for the proportion of time an individual driver spends in different VSP operating classes. The CO2 emissions benefit, as expected, was highest for VSP < 0 operation (deceleration or downgrades), exceeding a factor of 2 for operating conditions with greater than 50% EDO operation (city and arterial driving only, Figure 3A). The mean ± standard deviation CO2 benefits accrued over all negative VSP < 0 operation were 8.1 ± 2.0, 3.6 ± 1.7, and 1.6 ± 0.6, for the city, arterial and highway portions of the route, respectively. For positive VSP ≥ 0, the CO2 benefit decreased as a log function of VSP from VSP=0 to the crossover VSP (Figure 3A) and the CO2 benefit increased with the HEV’s electric-driveonly (EDO) status regardless of road type (Figure 3B). As expected, both GHG emissions and fuel consumption benefits accrued under VSP modes where the ICE was more likely to shut down to save fuel. The mean percent operating time that the HEV’s ICE was off (percent electric-drive-only operation, %EDO) varied with VSP and road type (Figure 3): EDO driving occurred 55%, 43%, and 4%, on average, respectively for city, arterial, and highway driving (Table 1). With increasing VSP, HEV benefits decreased until the crossover VSP where the HEV’s slightly smaller engine, even when assisted by the electric motor to achieve equivalent operating power, apparently burns more fuel and emits more CO2 than the CV. Other noteworthy HEV operation were ICE “restart” events (defined by RPM exceeding 775 after prior ICE-off status) associated with significant particle number emission rates.28,30,50 Restarts comprised over 20% of the city and arterial driving where vehicle stops were common (Table 1) and 2% on the highway due to inclusion of ramps. Table 1 also summarizes the best-fit segmented model of %EDO as a function of Log10 VSP for each road type where %EDO is assigned the mean measured value for VSP < 0 and a polynomial function for positive VSP. The last column in Table 1 indicates root-mean-square error (RMSE) of 0.04% for city and arterial fits, and 0.002% for the highway model. Combining 10203

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Figure 3. Mean CV/HEV ratio of ERCO2 (HEV “benefit”) by (A) VSP class for each road type (color = %EDO, with purple circles indicating no ICE-off operation occurred; note y-axis log scale and scatter in the negative VSP classes < −20 kW/ton was due to the small number of observations) and by (B) mean percent HEV electric-drive-only (EDO) percent operation in each VSP class (color = road type) with best-fit model shown for each road type in inset table.

than ICE-off operation: electric-assist at high speed and regenerative braking at low speed/deceleration, the frequency of both vary with road type. Also notable were different SOC patterns between road types (Figure 2). The HEV battery SOC was generally highest for stop-and-go city driving, decreased below the data set’s mean value (65%, line in Figure 2) for arterial driving, and decreased further still for the highway portion of the route. The decreasing SOC trends with VSP for each road type (Figure 2) is consistent with charge-depleting operation of the HEV as the power demand on the vehicle increased. Surprisingly, city driving on this route enabled some significant recharging of the battery at relatively high VSP between 15 and 28 kW/ton. This result may be route-specific due to steep road grade and warrants further investigation to evaluate whether different drivers and/or combinations of road

models for %EDO as a function of VSP and CO2 benefit as a function of %EDO (Table 1 and Figure 3B, respectively) one could estimate HEV CO2 emissions given data for a comparable CV and representative vehicle activity (i.e., distribution of VSP by road type). We found the goodness-of-fit to be higher for city (95% variance explained) and arterial (75%) driving than for highway (57%) driving by applying the combined models to TOTEMS data (See SI Figure S-6). The EDO behavior of the Toyota Synergy Drive HEV only partially explains the CO2 benefits as a function of VSP. Consistent with the calculated maximum EDO operating point of 13.9 kW/ton, above 9, 10, and 7 kW/ton on city, arterial and highway, respectively, the HEV ICE was always on (Figure 3A). Therefore, the HEV benefits that accrued between 9 kW/ton and 20 kW/ton resulted from HEV platform advantages other 10204

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Figure 4. (A) Distance-normalized fuel consumption (L/km) for CV and HEV by VSP class for each road type and (B) fuel consumption benefit factor by VSP (note log scale on y-axis). Refer to Figure 1B for the fraction of time spent in each VSP class.

alternative fuel-efficiency strategies such as aerodynamics and light-weighting. However, these HEV benefit results apply only to one Toyota HEV platform. Thus, while the data provide a baseline on a single widely used HEV design (Hybrid Synergy Drive) that is currently available on a number of HEVs, extrapolation of these results to other HEV designs should acknowledge this limitation. Instantaneous fuel consumption rate (L/s), estimated by the carbon balance method (eq 4), followed similar patterns to ERCO2 (Figure 4). Mean distance-normalized fuel consumption (L/km) HEV benefit factors were ∼10, 5, and 2, for VSP < 0 operation on city, arterial and highway, respectively, but decreased logarithmically for VSP > 0 (Figure 4). The fuel consumption results are consistent with reports by Graham (2005), where the HEV fuel economy (mpg) was two or more times higher than that of the baseline gasoline CV in a dynamometer study.44 Over the entire data set, the HEV instantaneous fuel consumption averaged a benefit of 2.4 compared to the CV. These HEV fuel consumption improvements measured during real-world driving over all seasons in Vermont are comparable to the fuel consumption benefits of operating a diesel light-duty vehicle.44

grade result in similar relationships. While our SOC data for city driving suggest the ICE operated to recharge the HEV battery on steep uphill driving at low speeds, future studies should evaluate the degree to which road grade may alter the reported trip-level importance of driver aggressiveness on HEV battery SOC and regenerative braking energy.41 At VSP > 21 for the highway driving only, the HEV’s electric assist benefits were outweighed by ICE power demand, resulting in benefit factors