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`SAE TECHNICAL
`PAPERSERIES970289
`
`Analysis of the Fuel Economy
`Benefit of Drivetrain Hybridization
`
`Matthew R. Cuddy and Keith B. Wipke
`National Renewable Energy Lab
`
`Reprinted from: Electric and Hybrid Vehicle Design Studies
`(SP-1243)
`
`400 Commonwealth Drive, Warrendale, PA 15096-0001 U.S.A.
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`i
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`ISSN0148-7191
`Copyright1997SocietyofAutomotive Engineers, Inc.
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`970289
`Analysis of the Fuel Economy
`Benefit of Drivetrain Hybridization
`Matthew R. Cuddy and Keith B. Wipke
`National Renewable Energy Lab
`
`Copyright 1997 Society of Automotive Engineers, Inc.
`
`ABSTRACT
`
`Parallel- and series-configured hybrid vehicles likely
`feasible in next decade are defined and evaluated using
`NREL's flexible ADvanced Vehicle SimulatOR, ADVISOR.
`Fuel economies of these two diesel-powered hybrid vehicles
`are compared to a comparable-technology diesel-powered
`internal-combusüon-engine vehicle.
`Sensitivities of these
`fuel economies to various vehicle and component parameters
`are determined and differences among them are explained.
`The fuel economy of the parallel hybrid defined here is 24%
`better than the internal-combustion-engine vehicle and 4%
`better than die series hybrid.
`
`INTRODUCTION
`
`Automobile drivetrain hybridization (using two types
`of energy converters radier than just one, as conventional
`drivetrain vehicles do) is considered an important step to high
`fuel economy. The Department of Energy has established
`cost-shared programs with Chrysler, Ford, and General
`Motors under die Hybrid Vehicle Propulsion System Program
`to double the fuel economy of midsized automobiles, without
`sacrificing performance and consumer acceptability, by
`hybridizing their drivetrains.
`The government/industry
`Partnership for the New Generation of Vehicles (PNGV)
`effort has also identified hybridization as an important step
`toward tripling mid-sized sedan fuel economy. Recent and
`ongoing work seeks both to identify die likely fuel economy
`gains hybrid vehicles can deliver, and to ascertain die hybrid
`configuration that will lead to the best fuel economy [1-6].
`Tamor, of Ford Motor Co., uses energy throughput
`spectra of
`current
`internal-combustion-engine
`vehicles
`(ICEVs) along with Ford Ecostar electric-drive data and an
`idealized battery model
`to estimate the greatest possible
`benefit of drivetrain hybridization to be 50% [6]. Given a
`100% efficient energy storage system and ideal control
`stragegy, Tamor estimates a parallel hybrid will have a
`combined federal fuel economy of roughly 1.5 times the fuel
`economy of an ICEV of similar mass and engine technology.
`
`(Combined federal fuel economy is computed assuming 55%
`of miles are driven on the USEPA Federal Urban Drive
`Schedule (FUDS) and 45% on the USEPA Federal Highway
`Drive Schedule (FHDS).)
`Further, Tamor concludes that
`engine and road loads being equal, a parallel hybrid is more
`fuel-efficient than a series hybrid. Mason and Kristiansson,
`however, assert that series hybrids are likely to be more fuel
`efficient than parallel hybrids [7].
`Initial studies at NREL
`using current and projected component data indicated that
`series and parallel hybrids have similar
`fuel economy
`potential [8].
`This analysis predicts the fuel economy differences
`among a series hybrid, a parallel hybrid, and an ICEV of
`levels of advancement and performance, using
`similar
`component
`and
`vehicle
`data
`adapted
`from current
`technologies.
`The methods of analysis and assumptions
`required are presented. The dependence of the fuel economy
`of each vehicle upon the assumptions are presented, allowing
`an understanding of the various projections of hybrid fuel
`economy made in the literature.
`Sensitivity coefficients,
`required for
`the fuel economy sensitivity analysis and
`analogous to the "influence coefficients" discussed by Sovran
`and Bohn are
`also presented [9].
`These
`sensitivity
`coefficients may be used to estimate the fuel economy of
`derivatives of the vehicles presented.
`The National Renewable Energy Laboratory's
`(NREL's) ADvanced VehIcle SimulatOR (ADVISOR), was
`used along with data from the literature and from industry
`contacts to define and evaluate charge-sustaining hybrids
`(which may operate without wall-charging, as long as there is
`fuel in their tank) and a ICEV for comparison. ADVISOR
`was also used to determine numerically the sensitivity of each
`vehicle's fuel economy to changes in vehicle and component
`parameters. These sensitivities were then used to analyze the
`predicted fuel economy differences among die three vehicles.
`The only vehicle figure of merit being considered
`here is fuel economy. We recognize that there are many other
`important
`issues to be resolved in die development of a
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`based on the AC induction system being developed by
`Westinghouse.
`The batteries modeled here are advanced
`lead-acid, with characteristics adapted from Optima [11].
`Vehicle drag parameters were chosen to define a Partnership
`for the New Generation of Vehicles (PNGV)-like vehicle with
`an aluminum-intensive body, heavy by Moore's standards but
`deemed achievable by those in industry interviewed by
`Duleep [2,4]. The "regenerative braking fraction" in the
`table is defined here as the fraction of braking energy during
`a given cycle that is provided to the electric drivesystem, with
`the balance, 60% in this case, handled by friction brakes.
`
`vehicle such as cost, reliability, and emissions. Our focus
`here, however, is solely on the likely potential to improve fuel
`economy by drivetrain hybridization. With that focus, we
`found the series hybrid defined here is 18% more fuel
`efficient than the ICEV and die parallel hybrid is 24% more
`fuel-efficient
`than the ICEV.
`A 10% drop in battery
`turnaround efficiency (from ~88% to ~80%) causes a 1.5%
`drop in series hybrid fuel economy (for this particular control
`strategy) and a 1.3% drop in parallel hybrid fuel economy.
`The sensitivity to regenerative braking effectiveness
`is
`likewise small:
`a 10% drop in regenerative braking
`effectiveness causes a 0.7% drop in parallel hybrid fuel
`economy and a 1.0% drop in series hybrid fuel economy.
`
`BASELINE VEHICLES
`
`The vehicles used in this study were defined using
`current and projected vehicle and component data. Using
`NREL's vehicle performance simulator, ADVISOR, (which
`has been benchmarked against industry simulation tools,) the
`components were sized to meet performance goals, and
`transmission and hybrid control strategies were optimized for
`fuel economy subject to performance constraints. ADVISOR
`was then used to evaluate the vehicles' fuel economy. The
`vehicles in this study are shown schematically in Figure 1.
`
`VEHICLE SPECIFICATION
`lists the main
`Component Spedfication - Table 1
`component efficiencies and road load parameters assumed for
`this effort.
`The heat engine used here is a direct-injection (DI)
`diesel, with a fuel-use map from the 5-cylinder, 85 kW Audi
`engine [10], scaled to peak efficiencies given in Table 1. See
`the "Scaling" section below for discussion of efficiency
`versus-size-and-year considerations. The generator coupled
`to the diesel in the series hybrid vehicle (HV) is based on a
`permanent magnet motor/controller
`set
`from Unique
`Mobility. The traction motor/inverter set modeled here is
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`Table 1 also indicates propulsion component size
`and average energy efficiency over the combined federal
`cycle. Propulsion system components for each of the three
`vehicles were sized, using ADVISOR,
`in order to meet
`performance requirements set out by the US Consortium for
`Automotive Research (USCAR) for the PNGV effort [12]:
`1.0 to 96.5 km/h (0 to 60 MPH) in 12 s
`2.64.4 to 96.5 km/h (40 to 60 MPH) in 5.3 s
`3.0 to 136.8 km/h (0 to 85 MPH) in 23.4s,and
`4.6.5% gradeability at 88.5 km/h (55 MPH).
`The PNGV targets listed as 1-3 above must be
`attained at curb weight plus 136 kg for the driver and
`passenger, while the gradeability requirement is prescribed at
`gross vehicle weight with full accessory load for 20 minutes.
`We have differed from theNGV specifications in that the
`gradeability requirement placed on thevehicles in this study
`is 6.5% at 88.5 km/h indefinitely (until the fuel runs out),
`with average accessory load, at curb weight plus 136 kg.
`
`The HPU size for the hybrids is determined by the
`continuous gradeability requirement. Note that the HPU for
`the series HV is smaller than for the lighter parallel HV.
`This is because the 88.5 km/h requirement, for a given gear
`ratio, requires the parallel HV's HPU to provide adequate
`climbing power at a certain speed which, in this case, is not
`the speed at which it develops maximum power. The series
`HV's HPU may operate at maximum power regardless of the
`vehicle speed; thus, its maximum power can be set to exactly
`die climbing power requirement. With the HPU sized for
`both vehicles, the motor and batteries are sized to meet the
`acceleration requirements, numbered one through three
`above.
`
`Scaling - In this study, the efficiency map for an
`85-kW engine introduced in 1990 is used to describe the
`behavior of 32-, 35- and 62-kW engines [10]. For the 32- and
`35-kW engines, the original map shape and peak efficiency
`value were maintained, while the torque axis was compressed.
`We acknowledge the significant technical challenge involved
`in achieving such high peak efficiencies with a small engine,
`but are encouraged by continued progress by VW/Audi
`(which introduced a 66 kW DI diesel with 41.8% peak
`efficiency two years after the 85 kW benchmark) [17]. We
`believe 43% peak efficiency in 32- to 35-kW diesel engines in
`2005 is consistent with the Office of Advanced Automotive
`Technology's 2004 target of 45% peak efficiency for 40- to
`55-kW engines, which is a goal at
`least
`some diesel
`manufacturers find reasonable [13,18]. We have assumed a
`peak efficiency of 46.5%, the peak efficiency of current state
`of-the-art heavy-duty diesel engines, for the 62-kW engine in
`2005, with its higher peak efficiency due to its larger size
`[13]. We expect the smaller (HV) engines to have lower
`specific power; the effect of changes in engine specific power
`can be derived using the data in Table 5.
`
`The tractive motors in this study, at outputs of 53
`and 27 kW, are significantiy smaller than the 75-kW motor
`
`from which their maps come. However, motors of these
`lower power levels with peak efficiencies of over 92% are
`available now [14]. We have not attempted to scale the
`efficiencies up,
`as would
`likely
`result
`from further
`development, for lack of data.
`Series Hybrid Control Strategy - The strategy chosen
`here was a close-power-follower strategy where the hybrid
`power unit (HPU) power output closely follows the tractive
`motor output. Figure 2 shows the behavior of the vehicle
`propulsion system following this strategy over the first 315 s
`of the FUDS. The HPU power (represented by the dots)
`varies direcdy with the tractive motor power (represented by a
`solid line), but is higher by a state-of-charge-dependent factor
`to allow for losses in the generator and battery.
`In this
`strategy,
`the HPU power is given by (K1*(tractive_motor
`_power) + K2)*(SOChi-SOC)/(SOChi-SOC1o), where SOChi
`and SOC1o are threshold SOCs.
`As the third chart in the figure indicates, this control
`strategy leads to nearly constant battery pack state-of-charge
`(See the "Results and Discussion" section for a more
`(SOC).
`detailed discussion of the control strategies considered here.)
`
`We have chosen a power-follower strategy where the
`HPU power follows the motor power's second-by-second
`variation, which defines a vehicle that achieves 29.5 km/L
`(69.4 MPG) on the combined federal drive schedule. This
`strategy was chosen because:
`1)it leads to the best fuel economy in the control strategy
`design space of power-follower approaches considered here,
`and
`2)it requires the HPU to immediately follow tractive power
`requirements, as occurs in the parallel hybrid and ICEVs.
`This leads the fuel economy estimate of each vehicle to be
`overestimated roughly equally due to the consistent neglect of
`transient effects, minimizing the effect of transient fuel use on
`die differences among the three vehicles' fuel economy.
`This approach likely has no emissions benefit over
`ICEVs,
`and
`is
`chosen
`only
`for
`its
`fuel
`economy
`characteristics. Alternative control strategies with potentially
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`better emissions characteristics are presented later in this
`paper.
`
`Parallel Hybrid Control Strategy - The parallel
`control strategy can be defined as follows:
`
`HPU does not idle (it turns off when not needed).
`• The
`• The
`
`motor performs regenerative braking regardless of
`die batteries' SOC.
`• The
`
`HPU generally provides the power necessary to meet
`the trace, and
`• the
`
`motor helps if necessary by providing additional
`torque, or accepting extra torque provided by the HPU for
`recharging the batteries.
`The parallel hybrid control strategy was defined by
`two parameters:
`a vehicle speed below which the HPU is
`turned off,
`to allow for electric launch and no idling, and
`minimum HPU operating torque defined as a function of
`engine speed, which is the lowest torque output at which the
`HPU would operate whenever the tractive torque requirement
`is positive. When the minimum-allowed HPU operating
`torque exceeds that required to meet the trace, the balance of
`torque is used to drive the motor as a generator, recharging
`the batteries.
`
`Figure 3 shows a sample portion of FUDS for this
`parallel vehicle. Note that the motor provides power when
`the vehicle needs additional torque to meet the driving, as
`described above. During the constant velocity portion of the
`driving cycle (from ~90 to ~115 s),
`the motor power is
`negative as the HPU provides recharging torque to the motor.
`As soon as the vehicle begins decelerating, the HPU shuts off,
`and the SOC increases while the motor captures regenerative
`braking energy. Note that during the final, slow deceleration,
`the motor power is negative but small in magnitude, leading
`to only a slight rise in SOC.
`
`VEHICLE EVALUATION
`Vehicle Performance Simulator - ADVISOR is an
`empirical, physics-
`and map-based ADvanced Vehicle
`SimulatOR developed
`in
`the
`Simulink®/MATLAB®
`
`environment, the flexibility of which allows the development
`and evaluation of arbitrarily complex control strategies.
`ADVISOR uses fundamental equations of vehicle dynamics
`coupled with efficiency and/or power loss component maps to
`predict hybrid,
`internal-combustion-engine,
`and electric
`vehicle performance, range, and fuel economy.
`It has been
`compared to industry vehicle simulation programs and has
`been used by engineers in the hybrid program at the Chrysler
`Corporation. Reference eight provides more detail on this
`tool's application to vehicle system analysis.
`Fuel Economy Calculations - The fuel economy
`results presented here must be understood as estimates: some
`uncertainty is introduced both by the model and the input data
`to the model.
`Additionally,
`the
`test procedure
`for
`determining hybrid vehicle fuel economy is a difficult
`problem on which debate on proper methodology continues.
`Due to the two energy-storage-modes of hybrid vehicles, the
`fuel economy calculation approach taken also may introduce
`uncertainty.
`A Society of Automotive Engineers (SAE) task force
`has developed a draft hybrid vehicle test procedure which
`aims to properly account for changes in stored energy in
`charge-sustaining hybrids [19]. The unique feature of this
`approach is the running of two series of consecutive FUDS
`(for example) to determine fuel economy. The first series is
`started at the highest expected battery pack SOC and ends at
`some lower SOC,
`resulting in a fuel use and associated
`change in SOC. The second cycle is started at the lowest
`expected SOC and ends at some higher SOC, resulting in
`another fuel use and associated change in SOC. Linear
`interpolation is then used to predict the fuel economy estimate
`for the vehicle if the batteries had no net change in SOC.The
`benefit of using a test procedure such as this is that the
`uncertainty due to stored or discharged energy can be
`estimated by comparing the two fuel economies which bound
`die interpolated zero-?SOC fuel economy.
`Because the hybrids in this study tend to reach some
`nearly steady SOC after a number of cycles, a different
`approach was used to estimate their zero-?SOC fuel
`economy. The vehicles were run over four repeated cycles
`(one set of FUDS and one set of FHDS) with fuel economy
`and SOC measured only over the last cycle, and any fuel
`economy measurement associated with a |?SOC| > 1% (on the
`last cycle) was discarded. Because an exact match (?SOC=O)
`is not
`required,
`some uncertainty is
`introduced by this
`method. However, we found it to be more repeatable than the
`SAE draft
`test
`procedure
`described
`above
`for
`the
`computational evaluation of these vehicles.
`It should be
`noted that our approach only works for hybrids whose SOC
`reaches some steady-state mean value over the cycle,
`is
`expected to be useful only for computational evaluation of
`hybrids, and is not as generally applicable as the draft SAE
`procedure.
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`RESULTS AND DISCUSSION
`
`FUEL ECONOMY ESTIMATES - The above
`defined vehicles were evaluated as described, and the
`resulting fuel economy estimates are in Table 2.
`From
`previous
`comparisons with
`automobile manufacturers'
`models, we estimate the model to be accurate to ±10%, while
`uncertainty in the component data projections is difficult to
`estimate.
`Thus these fuel economy estimates should be
`
`treated with care. On the other hand,inasmuch asthevehicle
`model over- or underestimates each vehicle's fuel economy
`equally,
`the model's uncertainty contribution is bias
`uncertainty and does not affect the accuracy of the computed
`fuel economy differences among the vehicles.
`
`and braking frequency than fuel economy of the hybrids
`considered here.
`
`VEHICLE DRAG PARAMETER AND COMP
`ONENT EFFICIENCY EFFECTS - The sensitivity of the fuel
`economy of each of the three baseline vehicles to various
`vehicle and component parameters were calculated using
`ADVISOR, and are presented in Table 3.
`The values
`presented indicated the percentage change in fuel economy
`due to a 1% increase in the given parameter (only). We took
`care to accurately estimate constant-performance sensitivity
`coefficients for each vehicle. Detailed discussion of the
`sensitivity coefficients is in the Appendix.
`
`DRIVE-CYCLE EFFECTS - The parallel HV is
`30% more fuel-efficient than the ICEV on the urban cycle
`(FUDS), 14% more fuel-efficient on the highway cycle
`(FHDS), and 24% more fuel-efficient on the combination.
`The series hybrid follows the same trend, being 26% more
`fuel-efficient than the ICEV on the FUDS, 7% more fuel
`efficient on the FHDS, and 18% more fuel-efficient on the
`combination. Note that both the parallel and series hybrids
`get similar fuel economy improvement on the FUDS, but that
`the parallel hybrid gets over
`twice the fuel
`economy
`improvement of the series on the FHDS. This is due in part
`to the parallel hybrid's HPU efficiently (especially on the
`FHDS) supplying power directly to the wheels, rather than
`having its output converted from mechanical to electrical and
`back to mechanical power, as occurs in the series hybrid.
`The dependence of the fuel economy benefit of
`hybridization on drive-cycle is due to differences between the
`cycles in the number and nature of braking events, amount of
`idling time, and average power requirement. As braking
`frequency increases,
`so does the opportunity to recover
`braking energy. The more time an ICEV's engine idles in a
`given cycle, the more fuel may be saved by disallowing idling,
`as is done in both hybrid control strategies here.
`(Recall that
`the parallel vehicle powers its accessories electrically, and
`does not idle its heat engine). Also, the lower the average
`power requirement, in general, the lower the ICEV's average
`efficiency, and the greater the opportunity for hybridization to
`improve upon that efficiency [6].
`The fuel economy improvement of the series and
`parallel hybrids over the ICEV is strongly dependent upon the
`driving cycle being considered. This is largely because the
`ICEV's fuel economy is much more sensitive to idling time
`
`Three of the sensitivity coefficients in Table 3
`require some explanation.
`The sensitivity coefficient for
`average motor efficiency indicates the percent change in fuel
`economy due to a 1% improvement in the tractive motor's
`efficiency while acting as a motor rather than as a generator.
`The sensitivity coefficient for the motor-as-a-generator refers
`to the dependence of fuel economy upon the tractive motor's
`efficiency when driven as a generator by the HPU (which
`occurs only in the parallel vehicle).
`The sensitivity
`coefficient labeled "regenerative braking fraction" refers to
`the dependence of fuel economy upon the tractive motor's
`efficiency when driven as a generator by the brakes.
`
`We can use these sensitivities to estimate the effects
`of uncertain component efficiency assumptions. For example,
`the estimated 87.6% average turn-around efficiency for a
`high-power lead-acid battery in a hybrid vehicle may be
`optimistic, and we may choose a more conservative estimate
`of 80% average efficiency. The result of this revision is to
`change the fuel economy of the series vehicle by [(0.80 0.876)
`/ (0.876)] x 0.15 = -0.013 = -1.3% = -0.38 km/L (-0.9
`MPG), and the parallel hybrid's fuel economy changes by
`[(0.80 - 0.876) / (0.876)] x 0.13 = -0.011 = -1.1% = -0.34
`km/L (-0.8 MPG). Of course, similar analyses can be performed
`with any of the parameters presented in Table 3, with
`good accuracy at changes of up to ±10% and reasonable accuracy
`at changes of upto ±20% in size. size.
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`To summarize, with the exception of mass, road load
`parameters have a roughly equal effect on the fuel economy of
`all three vehicles, and therefore do not significantly affect the
`differences among them. Because this series hybrid uses a
`close-power-follower control strategy,
`its fuel economy is
`nearly as insensitive to battery efficiency as is the parallel
`hybrid's fuel economy. Hybrid fuel economy is less sensitive
`to regenerative braking effectiveness
`than to all other
`parameters considered here except the motor efficiency for
`the parallel vehicle.
`to previous
`Comparing fuel economy estimates
`work - Using the sensitivities presented in Table 3, we can
`develop an estimate of
`the fuel
`economy benefit of
`hybridization assuming 100% efficient components,
`for
`comparison with Tamor's estimates. We use
`
`where FE is the new, estimated fuel economy, FEtable_2 is the
`fuel economy presented in Table 2, ? are the sensitivity
`coefficients, and Xi are the associated vehicle parameter
`values. Equation 1
`implies that sensitivities may be used
`independentiy, which is true to a limited extent. Because the
`power flows in hybrid vehicles depend upon each other and
`the states of the vehicle components in complicated ways,
`hybrid vehicle fuel economy is a non-linear function of drive
`cycle and vehicle and component parameters. The effects of
`small vehicle-parameter changes may be combined reasonably
`with Equation 1, but using such an approach to combine large
`changes will lead to significant uncertainty in the result.
`Equation 1, along with the combined federal fuel
`economies for the series HV and ICEV presented in Table 2,
`and the baseline vehicle mass, motor and battery efficiency,
`was used to predict the "Current" series-hybrid normalized
`fuel economy presented in Table 4.
`Tamor used energy-throughput spectra (along with
`component data) to estimate the fuel economy of series and
`parallel hybrids relative to a lightweight Taurus using a
`"perfect" CVT. The hybrids' fuel economies were calculated
`for a range ofvehicle masses and battery pack efficiencies.
`In
`this comparison, we consider one point in Tamor's range of
`estimates:
`the series HV with a 100%-efficient energy
`storage
`system and the
`same mass
`as
`the baseline
`conventional-drivetrain vehicle in his study. We choose the
`100%-efficient energy storage for comparison to reduce the
`effect of uncertainty about Tamor's control strategy.
`(See tiie
`Appendix
`for
`a
`discussion of
`the battery-efficiency
`sensitivity/control-strategy relationship.)
`To
`develop
`a
`fuel-economy-improvement-from
`hybridization estimate for comparison to Tamor's estimate,
`we begin with the series HV defined in Table 1. We then
`apply Equation 1 to account for the differences between the
`point in Tamor's space chosen above and the Table 1 vehicle.
`Tamor's vehicle had all braking energy done by the electric
`drivetrain, while in our case, only 40% of braking energy was
`
`availablefor regeneration. Also, the point we chose from
`Tamor's calculations has
`a
`100%-efficient
`(round-trip)
`battery pack, while that of Table 1 is 87.6% efficient. Finally,
`our conventional-drivetrain vehicle has lower mass and a
`higher peak-efficiency-engine than does the series HV. The
`result of these modifications to the series HV computed here
`is shown in Table 4. We have used second-by-second
`simulation, component maps, and sensitivity coefficients to
`derive, within 5%,
`the same fuel economy benefit of
`hybridization as did Tamor using different methods, for a
`somewhat heavier vehicle. This comparison is not a valid
`ation of either work, but indicates reasonable agreement using
`different approaches.
`
`COMPONENT SPECIFIC POWER EFFECTS - It
`was noted above that neither hybrid vehicle's fuel economy
`(using the given control strategies) is particularly dependent
`upon battery efficiency. Let us examine the effect of battery
`specific power.
`The batteries in this comparison were
`assumed to have a power density of 800 W/kg, which results
`in a baseline battery mass of 78.4 kg for the series hybrid and
`39.7 kg for the parallel hybrid.
`If we make the more
`conservative assumption of a 400 W/kg battery pack, both
`pack masses double. We can estimate the fuel economy effect
`of this change using the mass sensitivity coefficients for the
`two vehicles and their baseline data from Table 1 . The series
`vehicle's mass would change by (78.4/1243)=6.3%, and with
`a mass sensitivity coefficient of -0.60, the series hybrid's fuel
`economy would change by 6.3% x (-0.60) = -3.8% =
`-1.12 km/L (-2.6 MPG). The parallel vehicle's fuel economy
`change can likewise be estimated:
`(39.7/1218) x (-0.63) =
`-2.1% = -0.65 km/L (-1.5 MPG). Thus, the fuel economy of
`the series vehicle defined here is significantiy more sensitive
`to battery specific power than the parallel hybrid because of
`the series hybrid's larger battery pack. Similar analyses can
`be performed with the specific power of other components as
`shown in Table 5.
`
`These results indicate that hybrid fuel economy, and
`thus the fuel economy benefit of drivetrain hybridization, does
`not depend strongly upon any one of
`the drivetrain
`
`Tamor's vehicles had a mass of 1020 kg, while the ICEV mass here, to which
`we "corrected" the series hybrid mass using sensitivity coefficients, was 1214
`kg.
`
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`
`It does indicate that the fuel
`components' specific power.
`economy of the series hybrid, with its more powerful motor
`and battery pack, is significantly more sensitive to changes in
`specific power of these components than is the fuel economy
`of the parallel hybrid.
`
`TRANSMISSION EFFECTS
`Number of gears - A 5-speed transmission was used
`here.
`It has been shown that increasing the number of gears
`in a transmission can improve fuel economy [4]. This is
`principally because the engine associated with a transmission
`with a greater number of gears may be able operate more of
`its time in good thermal efficiency regions, reducing the
`opportunity for hybridization, and reducing the fuel economy
`benefit of hybridization.
`The continuously-variable trans
`mission (CVT) represents the limit of an infinite number of
`gears.
`As commercially available CVTs become more
`efficient, so that their losses do not negate the benefit of
`improved engine efficiency they offer, they may be used to
`make ICEV fuel economy more competitive with hybrid
`vehicle fuel economy.
`
`Drivetrain configuration - A reasonable modification
`of the parallel hybrid analyzed here would be to move the
`motor closer to the wheels. That is, we might expect greater
`fuel economy by not having the motor
`transmit
`torque
`through the transmission, but rather directly to the vehicle's
`differential (through a single speed-reducing gear).
`This
`would reduce losses in the transmission by reducing the
`energy passed through it, while increasing losses in the motor
`by forcing its speed to be a fixed fraction of the tire speed.
`See Figure 4 for a diagram ofthis alternate configuration.
`
`We can estimate the fuel economy value of the
`motor-differential configuration over
`the one previously
`analyzed using the sensitivity coefficients presented above.
`Three energy-transfer modes will be affected:
`1) motoring-
`mechanical energy provided by the motor will be subjected to
`fewer
`losses
`by
`circumventing
`the
`transmission,
`2)
`regenerative braking-regenerative braking
`torque will
`likewise suffer fewer losses, and 3) HPU-to-motor charging-
`using the HPU to drive the motor as a generator will be
`significantly complicated by the interposition of the 5-speed
`transmission,
`but
`that
`effect
`notwithstanding,
`the
`transmission will
`incur losses on the charging torque it
`transmits. We assume that the parallel motor's motoring and
`regenerative efficiencies are unchanged from the baseline,
`although its torque and speed output are tied to the wheel
`requirements by the motor-differential setup and not by the
`baseline motor-transmission-differential
`setup. We also
`
`assume that HPU-to-motor charging through the 5-speed
`transmission is
`feasible, despite the control challenges
`involved.
`
`the motor-to-wheels
`In the motoring regime,
`efficiency is improved by the ratio (1-spd transmission
`efficiency)/(5-spd transmission efficiency)=98%/92%=1.065,
`for a 6.5% improvement. Likewise, the regenerative braking,
`wheels-to-motor efficiency is improved by 6.5%. The HPU
`to-motor
`efficiency
`is
`decreased
`by
`a
`factor
`of
`92%/98%=0.939, for a 6.1% decrease. The combined effect
`of
`the motoring