University of Washington
ECOCAR3 VEHICLE POWERTRAIN MODELING AND DESIGN
PROBLEM PROPOSAL (DRAFT)
Nate Steinbock, Jake Garrison, Ajay Gowda, Brian Magnuson, Megan
Cawley, Sylvie Troxel, Wade Schultz, Myann Refai,
ECOCAR ADVANCED VEHICLE WORKS | MECHANICAL ENGINEERING - ENGINEERING ANNEX, STEVENS
WAY, BOX 352600SEATTLE, WA 98105, PHONE: (206) 616-8245
Executive Summary
1
Table of Contents
Executive Summary....................................................................................................................................... 1
Data Acquisition and Drive Cycles Used ................................................................................................... 5
Wheel-to-Well Greenhouse Gas Emissions Calculations .......................................................................... 5
1
Power and Energy Requirements at the Wheels .................................................................................. 6
2
Conventional Vehicle Performance and Fuel Consumption ................................................................. 9
3
4
5
2.1
E10 Vehicle Plant Model Description ............................................................................................ 9
2.2
Drive Cycle Results with E10 Ethanol Fuel .................................................................................. 10
2.3
Fuel Consumption Targets Compare-and-Contrast .................................................................... 11
2.4
Limited-Power Model to Meet Minimum Acceleration.............................................................. 12
2.5
Drive Cycle Result with B20 Biodiesel Fuel ................................................................................. 13
Battery Electric Vehicle Performance and Energy Consumption........................................................ 14
3.1
Modeling an Electric Powertrain ................................................................................................ 14
3.2
Model Assumptions, Limitations, and Sensitivities: ................................................................... 16
3.3
Energy Flow ................................................................................................................................. 16
3.4
Performance Tests ...................................................................................................................... 17
3.5
Energy balance: ........................................................................................................................... 18
Series Hybrid Electric Vehicle Performance and Energy Consumption .............................................. 19
4.1
Engine-Generator System ........................................................................................................... 19
4.2
Battery and general Hybrid Characteristics: ............................................................................... 20
4.3
Model Assumptions, Limitations, and Sensitivities: ................................................................... 20
4.4
Modeling a Series Hybrid ............................................................................................................ 21
4.5
Thermostatic-on/off and Load-Following Engine-Control Strategies ......................................... 24
4.6
Energy balance within the vehicle .............................................................................................. 26
4.7
Meeting Design Targets .............................................................................................................. 26
4.8
Trade-offs between battery sizing, mass, and losses versus engine generator losses ............... 27
Innovative Technologies to Reduce Fuel Consumption ..................................................................... 28
5.1
Potential Technologies ................................................................................................................ 28
5.2
Explored Innovative Technology – Automated Driving .............................................................. 29
Study Parameters ................................................................................................................ 29
Feed Forward Coast-down Driver Justification and Limitations ......................................... 29
Results ................................................................................................................................. 29
Limitations of Results .......................................................................................................... 30
2
6
Proposed Powertrain Design to Meet EcoCAR3 Design Targets......................................................... 30
6.1
PHEV Parallel Through the Road with BAS – E85 (Megan) ...................................................... 30
Trade-Offs ........................................................................................................................................... 33
Cost Increases ..................................................................................................................................... 33
Innovative Design Features ................................................................................................................. 33
6.2
Series PHEV 2-Speed w/ 200 kW motor and B20 Diesel (Brian) ................................................. 33
Trade-Offs ........................................................................................................................................... 33
Innovative Design Features ................................................................................................................. 33
Cost Increases ..................................................................................................................................... 33
Powertrain Configuration ................................................................................................................... 34
6.3
PTTR HEV + BASS (Megan and Brian) .......................................................................................... 35
Trade-Offs ........................................................................................................................................... 35
Cost Increases ..................................................................................................................................... 35
Innovative Design Features ................................................................................................................. 35
Powertrain Configuration ................................................................................................................... 35
6.4
Proposed Vehicle Architecture ................................................................................................... 37
Justification ......................................................................................................................................... 37
Energy and Emissions.......................................................................................................................... 37
7
Summary and Conclusions .................................................................................................................. 37
Appendix A: Problem 1: Simulink Model .................................................................................................... 38
Appendix B .................................................................................................................................................. 39
Appendix C: WTW Energy Consumption Calculator ................................................................................... 40
Tables
Table 1: EC3 Modeling Targets ..................................................................................................................... 6
Table 2: Results at the Wheels for Drive Cycles ............................................................................................ 7
Table 3: Average Power Results ................................................................................................................. 8
Table 4: E10 Vehicle Model Architecture ..................................................................................................... 9
Table 5: E10 Ethanol Fuel Drive Cycle Results ............................................................................................ 10
Table 6: Minimum Acceleration Model Results .......................................................................................... 12
Table 7: Template for Reporting Results and Powertrain Sizing ................................................................ 13
Table 8: B20 Biodiesel Drive Cycle Energy Consumption Results ............................................................... 13
Table 9: Reporting Results and Powertrain Sizing ...................................................................................... 17
Table 10: Drive Cycle Energy Consumption Results.................................................................................... 18
Table 11: Component Masses ..................................................................................................................... 20
3
Table 13 Results and Powertrain Sizing for a Baseline Load-Following Series HEV.................................... 22
Table 14: Drive Cycle Energy Consumption Results.................................................................................... 23
Table 15: Series Hybrid Model Specifications to meet Design Targets ...................................................... 27
Table 15: Tuned Series Hybrid Model vs. Design Targets ........................................................................... 27
Table 16: Coast Down Automated Driver vs. Normal Driver UDDS Results ............................................... 29
Table 17: Powertrain Sizing......................................................................................................................... 31
Table 18: EC3 Modeling Targets ................................................................................................................. 32
Table 19: Drive Cycle Energy Consumption Results.................................................................................... 32
Table 20: Results and Powertrain Sizing for a Baseline Load-Following Series HEV................................... 34
Table 21: EC3 Modeling Targets ................................................................................................................. 34
Table 22: Drive Cycle Energy Consumption Results.................................................................................... 35
Table 23: Results and Powertrain Sizing for a Baseline Load-Following Series HEV................................... 35
Table 24: EC3 Modeling Targets ................................................................................................................. 36
Table 25: Drive Cycle Energy Consumption Results.................................................................................... 36
Figures
Figure 1: Problem 1, Simulink Road Load Model Overview.......................................................................... 7
Figure 2: Problem 2, Vehicle Power Flow Diagram ..................................................................................... 10
Figure 3: BEV System Components ............................................................................................................. 16
Figure 4 : Powertrain configuration and power flow of a series hybrid ..................................................... 23
Figure 5: Engine Efficiency .......................................................................................................................... 24
Figure 6: Engine Power Loss ....................................................................................................................... 24
Figure 7: Engine Power Output ................................................................................................................... 25
Figure 8: Instantaneous Fuel Usage ............................................................................................................ 25
Figure 9: Cumulative Fuel Usage................................................................................................................. 25
Figure 10: Energy Storage State of Charge ................................................................................................. 26
Figure 11: PTTR PHEV with BAS Powertrain Diagram ................................................................................. 31
Figure 12: Problem 1 Vehicle Model; Simulink UPDATE!!! ......................................................................... 38
Figure 13: Torque, efficiency, and peak power point ................................................................................. 39
Figure 14: Engine for Model 2 (Autonomie) ............................................................................................... 39
Equations
(Eqn. 1) .......................................................................................................................................................... 6
(Eqn. 2) .......................................................................................................................................................... 8
(Eqn. 3) .......................................................................................................................................................... 8
(Eqn. 4) ........................................................................................................................................................ 15
4
Overview
The following sections discuss the development, testing, and data analysis of six vehicle modeling
problems. Using a combination of MATLAB 2010b, Simulink, and Autonomie (Version 1210) vehicle
plant models are parameterized per the EcoCAR3 Request for Proposal: Vehicle Powertrain Modeling
and Design Problem. All models are tested against a wide variety of drive cycles where results are
compared to industry standards and market vehicle performance indices. In all, we hope to
demonstrate our ability not only in using Autonomie but in designing and testing plant models using our
best engineering judgment in MATLAB and Simulink.
Data Acquisition and Drive Cycles Used
Data is acquired using various drive cycles in Autonomie and MATLAB Simulink in order to test vehicle
architecture fuel efficiency and dynamic performance. The four primary tests are the US06, UDDS,
HwFET, and Combined cycles in which a speed value is associated with a time value in a vector of values.
Based off of the speed at which a vehicle is traveling, energy and performance of the vehicle are
calculated.
In the four primary tests, calculated values pertaining to vehicle architecture are produced including fuel
and charge depleting range, initial energy inputting into a vehicle, final energy outputted from a vehicle,
mechanical energy consumed, electrical energy consumed, component efficiencies and wheel-to-well
greenhouse gas emissions. The UDDS cycle is used to represent in-city driving conditions in order to test
fuel economy for light and medium size vehicles. This test can also be used to determine the charge
depleting range of an electric vehicle. The HwFET drive cycle, on the other hand, represents highway
driving conditions in order to test fuel economy and charge depleting range as well. The Combined
(capital C ?) drive cycle represents a blend of the UDDS and the HwFET cycles in which 55 percent is
weighted by the HwFET drive cycle and 45 percent is weighted by the UDDS cycle. Finally, the US06 test
represents a more aggressive combination of in-city and highway driving. This cycle includes high speed
and high acceleration behavior with rapid changes in speed.
Other tests are used in order to determine gradeability, 0-to-60mph acceleration, and top speed. In
order to determine gradeability, a parametric study is run using the steady state drive cycle provided in
Autonmie where grade is changed iteratively until the maximum grade is achieved at a maximum speed
of 60 mph for 20 minutes. Using the built in acceleration test within Autonomie, 0-to-60mph time of
each vehicle architecture can be determined. Furthermore, the acceleration test is run for a prolonged
period of time to examine the top speed of the vehicle, for vehicle speed is commanded at maximum
throughout the test.
Wheel-to-Well Greenhouse Gas Emissions Calculations
To determine the wheel-to-well greenhouse gas emissions of each vehicle, an excel spreadsheet has
been made accounting for emissions seen in both the fuel and electric energy generation processes and
in the vehicle energy consumption processes. Parameters, descriptions, and equations pertaining to
greenhouse gas emissions seen in fuel production can be found in Appendix C.
5
Table 1: EC3 Modeling Targets
Performance/Utility Category
Vehicle Modeling Design Targets*
Energy consumption (unadjusted energy use on combined Better than 370 Wh/km (600 Wh/mi)
Federal Test Procedure [FTP] city and highway cycles)
combined city and highway (55%/45%,
respectively)
GHG emissions (WTW combined city and highway cycles)
Less than 120 g of carbon dioxide equivalent
(CO2 eq)/km (200 g CO2 eq/mi)
Interior size/number of passengers
Minimum of four passengers
Luggage capacity
More than 230 L (8 ft3)
Range
Greater than 320 km (200 mi) combined city
and highway
Top speed
Greater than 135 kph (85 mph)
Acceleration time of 0 to 97 km per hour or kph (0 to 60
mi per hour or mph)
Less than 11 seconds
Highway gradeability (at gross vehicle weight rating
[GVWR])
Greater than 3.5% grade at a constant
97 kph (60 mph) for 20 minutes
1 Power and Energy Requirements at the Wheels
Given the standard road load equation regarding mass, drag, area, and rolling resistance, we were asked
to find the net road load energy applied to 3 drive cycles, as well as the positive and negative energy.
Additionally, values were required for the average positive propulsion, peak power output, peak tractive
force, and percent idle time. All of these values, presented in Table 2, were calculated from our
Simulink model for a glider vehicle.
Instead of relying on the advanced solving ability of Autonomie, we utilized engineering design
principles to assemble the glider vehicle from a blank slate in the more basic Simulink. As a result, the
model could be simplified in variable count, allowing for a more straightforward output analysis.
The majority of the model was generated from applying the following road load equation and the
constant variables given below in Error! Reference source not found.:
𝐹𝑇𝑟 = (𝑚𝑔) 𝐶𝑟𝑟 + ½ 𝜌 𝐶𝑑 𝐴𝑓 𝑣 2 + 𝐹𝑖𝑛𝑒𝑟𝑡𝑖𝑎𝑙
(Eqn. 1)
This equation solves for the tractive effort force at the wheels (N), where m is the vehicle test mass
(1500 kg), g is the acceleration due to gravity (9.81 m/s2), and ρ is the density of air (1.2 kg/m3). Crr is
the coefficient of rolling resistance (0.009), and CdAf is the product of drag and area (0.75 m2). The
inertial force, Finertial = mia where mi is the vehicle inertial mass (kg) and a is the vehicle acceleration
6
(m/s2) (Δv/Δt from the drive schedule). All other factors, including grade, gear ratio and fuel efficiency
are considered to be negligible in this model given the provided initial constraints.
Alternative road load equations were given as options to use, but we pursued the aforementioned
equation because it is more specific to an automotive application. Moreover, this equation matches the
default equation used by Autonomie. in addition to matching the equation used by default in
Autonomie. This method of model development was utilized in order to facilitate a logical evolution
from Simulink to the more complex Autonomie.
Shown below in is the top level view of our model:
Figure 1: Problem 1, Simulink Road Load Model Overview
This Simulink road load model specifies our relevant constant values (i.e. mass), keeps track of our
minimum and maximum values, and also allows us to independently select the appropriate drive cycle
for testing. The inputs are sent to the road load equation model and the outputs are displayed in our
MATLAB workspace, as well as the “scope.”
The road load equation shown in detail in the appendix contains the operations based off the given load
equations that output all of the required data in applicable units. Though the road load equation is
straightforward, its application to our drive cycle model was not without challenge. Our biggest
challenge was developing an efficient way to scan the given drive cycle and convert the velocity values
into the various power and force outputs required. This required several coding blocks to be added for
the specific purposes of converting units and calculating averages. The complete model was checked via
application of sound physics principals principles and further mathematical verification, and was proven
to be sufficiently optimized.
After inputting the supplied drive cycles into our model, the data shown in Table 2 was obtained:
Table 2: Results at the Wheels for Drive Cycles
7
Metric
UDDS
HwFET
US06
Positive propulsion energy required at the wheels
(Wh/km)
121.9
114.3
191.2
Negative (braking) energy required at the wheels
(Wh/km)
-48.2
-10.7
-47.6
Net (road load) energy required at the wheels (Wh/km)
73.8
103.7
143.6
Average positive propulsion power at the wheels (kW)
3.8
8.9
14.8
Peak power output at the wheels (kW)
33.7
27.4
84.3
Peak tractive force at the wheels (kN)
2.4
2.3
5.8
18.9%
0.8%
7.5%
Percent idle time (%)
Part two of problem one required us to estimate the average power at the wheels necessary to achieve
an acceleration from 0-to-60mph in 11 seconds, as well as the power required to climb a 3.5% grade, at
60 mph, at the GVWR (2000 kg).
Though we could have adjusted our model to obtain the results, we opted to take a more elegant
approach and modify the given road load equation to output the solution.
, shown below, was the primary modification of the generic road load equation.
𝑃𝑚𝑖𝑛𝑇 = [𝑚𝑔𝐶𝑟𝑟 + 1⁄2 𝜌𝐶𝑑 𝐴𝑓 ∆𝑣 2 + 𝐹𝑖𝑛𝑒𝑟𝑡𝑖𝑎𝑙 ] ∗ ∆𝑣
(Eqn. 2)
In
, PMinT is the average power, m is the vehicle mass (1500), ΔV is the change in velocity (60mph), CdAf is
the product of drag and area (0.75 m2), and Finertial is the inertial force of the vehicle where acceleration
needed to reach 60mph in 11 seconds is 2.43 m/s. The equation was derived from multiplying the road
load equation by the vehicle-velocity to achieve power.
To solve for average power with consideration of a 3.5% grade, we used a modified
, resulting in
𝑃𝑎𝑣𝑔 = [𝑚𝑔𝐶𝑟𝑟 ∗ sin 3.15 + 1⁄2 𝜌𝐶𝑑 𝐴𝑓 𝑣 2 + 𝐹𝑖𝑛𝑒𝑟𝑡𝑖𝑎𝑙 ] ∗ 𝑣
(Eqn. 3)
𝑃𝑎𝑣𝑔 = [𝑚𝑔𝐶𝑟𝑟 ∗ sin 3.15 + 1⁄2 𝜌𝐶𝑑 𝐴𝑓 𝑣 2 + 𝐹𝑖𝑛𝑒𝑟𝑡𝑖𝑎𝑙 ] ∗ 𝑣
(Eqn. 3)
:
In this equation, Pavg is the average power, m is the vehicle mass (2000 kg), g is acceleration due to
gravity (9.81 m/s2), sin(3.15) factors in the grade, ρ is the density of air (1.2 kg/m3), v is velocity (60mph),
Crr is the coefficient of rolling resistance (0.009), and CdAf is the product of drag and area (0.75 m2). In
this case, the inertial acceleration was set to zero, as the vehicle moves at a constant speed up the given
8
grade. The derivation of this equation also included multiplying the original road load equation by the
velocity. Table 3 below summarizes the outputs of this modified equation.
Table 3: Average Power Results
Metric
Result
Average power required to meet
minimum acceleration time (kW)
323.8
Average power required to climb 3.5%
grade at 60 mph at GVWR (kW)
30.8
2 Conventional Vehicle Performance and Fuel Consumption
This modeling exercise investigates three different conventional powertrains in an effort to explore
vehicle performance and energy consumption as a function of engine size and various fuels. The first
model makes uses of a 4-cyclinder engine fueled by E10 ethanol fuel. The second model is a scaled
down version of the first and is used to explore energy consumption in a vehicle that has a slower 0-to60mph acceleration time. The final model explores performance and energy consumption of a B20
biodiesel engine.
2.1 E10 Vehicle Plant Model Description
The following architecture is of a conventional, midsize, 2-wheel-drive vehicle with an automatic 6speed transmission. Using Autonomie, a standard 1.7L, E10 engine is adjusted to reach a maximum
power output of 100kW at 6000 revolutions per minute, 160 Nm of torque, and 35% engine efficiency.
In addition, a 12-gallon tank gives the vehicle an average range of 400 mi. The torque curve of the
engine along with the engine efficiency plots is located in Appendix B. The engine and transmissions
models used are provided in Appendix B as well. The following Table 4 is an overview of the vehicle
model architecture:
Table 4: E10 Vehicle Model Architecture
Test mass, kg
1500
Top speed, kph (mph)
124
Acceleration 0–60 mph, s
10.7
Highway gradeability at 60
mph at test mass, %
7.72
Powertrain configuration
Conventional Midsize
Auto 2wd
Powertrain sizing:
Engine peak power, kW
Transmission, gearing
1.8L 4-Cylinder
100.5
6-speed
1st: 3.4
2nd: 2.6
3rd: 1.9
4th: 1.2
9
5th: 1
6 : 0.87
th
Assumptions: The torque and engine speed curves are defined by a best-approximation of what a
nominal torque and engine speed curve are with reference to torque and engine speed curves provided
in Autonomie. In practice, our peak power point occurs near the maximum torque and engine speed
feasible for our engine.
Limitations: Because engine performance curves have been redefined, vehicle performance has been
altered and peak power has been greatly limited. Specifically, larger torque values for the vehicle model
reside in the high-end rpm values, whereas low-end rpm values have smaller torque values than usual.
This modification was made to have a definitive peak power point at the given torque and engine speed
parameters, 160Nm and 6000 rpm.
Sensitivities: Vehicle performance is exceptionally sensitive to the changing of torque and engine speed
curves. Furthermore, the adjustment of maximum power output has affected vehicle performance,
hindering 0-to-60mph time and top speed. Finally, gear ratios in the final drive and gear box have been
carefully set to achieve a 0-to-60 mph acceleration time less than 11.0 seconds.
The following diagram, Error! Reference source not found., depicts the general model used:
Figure 2: Problem 2, Vehicle Power Flow Diagram
2.2 Drive Cycle Results with E10 Ethanol Fuel
The following chart, Table 5, contains data collected from the powertrain model after completing the
UDDS, HwFET, Combined and US06 drive cycles. These drive cycles are used to test our vehicle against
different driving conditions where the model is run at a certain speed to assess energy flow and
consumption.
Table 5: E10 Ethanol Fuel Drive Cycle Results
Test Mass (kg): 1,500
Engine Size (kW): 100
Unit
UDDS
HwFET
Combined
Net tractive energy at
the wheels
Wh/km
28.5
66.2
45.5
US06
92.1
10
Fuel energy
Battery energy
GHG WTW
Range
Calculated Energy
Balance Results
Wh/km
666.6
523.0
602.1
806.6
DC Wh/km
.136
.075
.108
.111
CO2 g/mi
204.1
160.1
184.3
247.0
km
612
780
677
505
Wh/km
35.1
58.2
49.5
108.9
The above table shows standard results with mechanical energy consumption being the greatest in the
US06 drive cycle. This trend occurs often due to increased stop-and-go starts in the US06 drive cycle,
which force engines to operate with the least amount of efficiency as rotational inertia of the crank
shaft is reduced whenever the vehicle is stopped and idling.
Using the component efficiencies, we can determine whether or not the energy provided at the wheels
of our model is the maximum achievable amount with respect to the initial energy provided to the
system, the fuel. By starting at the energy losses in the engine, we are able to see the overall efficiency
of the vehicle as energy transfers from one component to the next. The following equation is used to
calculate the energy balance of the system where Energyout is measured in Wh/km.
𝐸𝑛𝑒𝑟𝑔𝑦𝑜𝑢𝑡 = 𝐹𝑢𝑒𝑙𝐸𝑛𝑒𝑟𝑔𝑦 × 𝐸𝑓𝑓𝑒𝑛𝑔𝑖𝑛𝑒 × 𝐸𝑓𝑓𝑚𝑒𝑐ℎ𝑎𝑛𝑖𝑐𝑎𝑙 𝑎𝑐𝑐𝑒𝑠𝑠𝑜𝑟𝑦 × 𝐸𝑓𝑓𝑐𝑙𝑢𝑡𝑐ℎ × 𝐸𝑓𝑓𝑔𝑒𝑎𝑟𝑏𝑜𝑥
× 𝐸𝑓𝑓𝑓𝑖𝑛𝑎𝑙 𝑑𝑟𝑖𝑣𝑒 × 𝐸𝑓𝑓𝑔𝑒𝑛𝑒𝑟𝑎𝑡𝑜𝑟 × 𝐸𝑓𝑓𝑡𝑜𝑟𝑞𝑢𝑒 𝑐𝑜𝑢𝑝𝑙𝑖𝑛𝑔 − 𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑎𝑙 𝐴𝑐𝑐𝑒𝑠𝑠𝑜𝑟𝑦𝐸𝑛𝑒𝑟𝑔𝑦
In all, the calculated Energyout should represent the net tractive energy at the wheels of the vehicle
assuming 100 percent efficiency in energy transfer beyond the already specified inefficiencies of each
vehicle component. On average the difference in the energy-balance results and the net tractive energy
at the wheels is 9.45 Wh/km.
2.3 Fuel Consumption Targets Compare-and-Contrast
In all four drive cycles, the conventional vehicle modeled was not able to meet the design targets for
energy consumption and wheel-to-well greenhouse gas emissions. This effect could be the result of
adjusting engine parameters coincided with effects due to increasing the coefficient of drag. (Fix this
sentence or remove it.)
However, in comparison to fuel energy consumption rates from similar vehicles in the 2012 EPA Test Car
List, energy consumption and wheel-to-well greenhouse gas emissions of the E10, 100-kW model are
relatively low. We specifically contrasted our model with a 2012 BMW Mini Cooper that has
comparable weight, engine power output, transmission and final drive system. During the HwFET test,
the Mini achieved a fuel consumption rate of 42 miles per gallon. Similarly, our vehicle model achieved
a fuel consumption of 40.77 miles per gallon on the same drive cycle. Furthermore, the Mini outputted
210 g/mi of CO2 from wheel-to-well, where our vehicle model outputted 160 g/mi of CO2.
To further explore the effects on fuel consumption (what effects?, do you mean “To explore the model
sensitivity to variation in engine characteristics,” ?), a parametric study is used to determine a
relationship between 0-to-60mph times and fuel consumption. In the following plot, 6 different engines
varying only in power output are tested in their ability to propel a 1500 kg chassis from 0-to-60 mph.
Using the same 6-speed gear box as seen in the E10 model, acceleration times are displayed on the yaxis. The varying engines are then run the UDDS and HWFET drive cycles in order to determine the
11
combined fuel consumption (55% UDDS, 45% HWFET), which is found on the x-axis. Finally, the engine
power output is found above each data point.
Acceleration Vs. Fuel Consumption for Various Engine
Powers
80kW
0-to-60 mph Acceleration Time (sec)
13.5
12.5
11.5
100kW
10.5
120kW
9.5
140kW
8.5
160kW
7.5
525
545
565
585
605
625
645
665
685
Fuel Consumption for Combined Drive Cycle (kW/km)
As power output increases for each engine so does fuel consumption. 0-to-60mph acceleration times
also increase as power increases. (Really? As you increase the power of the engine the car has lower
acceleration?) Both trends seem reasonable for more powerful engines have more lower-end torque to
accelerate the vehicle yet consume more fuel in the process to achieve a greater power output.
However, to reduce fuel consumption in both models, methods such as eliminating fuel intake into the
engine during idle and/or deceleration can be employed. To determine fuel consumption at idle, a
steady state drive cycle is used to idle the E10, 100-kW engine for 1150 seconds. With a maximum fuel
consumption of 0.176 kg over the 1150 second drive cycle, an average of 9.18 grams of fuel per minute
is burnt at idle, which could be saved using “engine idle stop.” Furthermore, by referencing the driver
speed demand signal in comparison to the fuel consumption rate for the same vehicle model an average
of 0.0001 kg of fuel per second is spent when decelerating. As a result, about 6 grams of fuel per minute
of vehicle deceleration could be conserved using “decel fuel cutoff.”
2.4 Limited-Power Model to Meet Minimum Acceleration
After decreasing the maximum power output of the E10 engine to 87,000W, our vehicle was able to
meet the maximum 0-to-60 acceleration time of 11 seconds as per the vehicle design targets. The fuel
tank size was kept the same. Showing the limited-power model results, Table 6 includes dive cycle
result values for this limited-power engine.
Table 6: Minimum Acceleration Model Results
Test Mass (kg): 1,500
Engine Size (kW): 100
Unit
UDDS
HwFET
Combined
Net tractive energy
Wh/km
45.5
106
72.7
US06
159
12
Fuel energy
Battery energy (12V)
Wh/km
659
518
595.55
804
DC Wh/km
.105
1.98
1.47
.982
CO2 g/mi
201.7
158.7
182.3
246.2
Km
619
787
685
507
GHG WTW
Range
2.5 Drive Cycle Result with B20 Biodiesel Fuel
In this stage, the E10 Ethanol fueled engine was replaced with a B20 biodiesel engine in order to study
energy consumption differences between the three vehicle models. By changing fuel density, heating
and carbon values, we were able to set our fuel characteristics within the model similar to those of a
B20 biodiesel engine. This powertrain utilizes a 10 gallon tank in order to give the vehicle a long range.
Furthermore, torque curves were given higher output values in the lower rpm region in order to match
torque characteristics of a standard diesel engine. However, size, engine speed, and max power output
are not altered. The results of this drive cycle are shown in Table 7 and Table 8. Error! Reference
source not found. also details fuel consumption of the B20 model.
Table 7: Template for Reporting Results and Powertrain Sizing
Test mass, kg
1500
Top speed, kph (mph)
86
Acceleration 0–60 mph, s
10.7
Highway gradeability at 60 mph at test mass, %
>5.5
Powertrain configuration
Conventional Midsize Auto 2wd
Powertrain sizing:
1.7L 6-Cylinder
Engine peak power, kW
85
6-speed
1st: 3.4
2nd: 2.6
3rd: 1.9
4th: 1.2
5th: 1
th
6 : 0.87
Transmission, gearing
Table 8: B20 Biodiesel Drive Cycle Energy Consumption Results
Test Mass (kg): Engine
Size (kW):
Unit
UDDS
HwFET
Combined
US06
Net tract. energy
Wh/km
28.1
66.0
45.15
99.4
Fuel energy
Wh/km
649.2
615.2
633.9
906.4
DC Wh/km
.14
.075
.110
.087
g CO2 eq/km
184.3
174.6.3
179.9
257.8
Km
585
617
599
419
Battery energy (12V)
GHG WTW
Range
13
In comparison to the E10 100-kw and the E10 87-kW vehicle models, the B20 diesel emits less
greenhouse gasses from wheel-to-well. In addition, less fuel energy is consumed by the B20 than the
other two vehicle models. As a result, the B20 power plant is the most fuel efficient and
environmentally friendly system according to the results required in this study.
3 Battery Electric Vehicle Performance and Energy Consumption
This is a model of a Battery powered Electric midsize fixed gear 2WD vehicle. The BEV was designed
based on the road load equation given in the previous problem statement in Autonomie. A BEV
powertrain has three main components Motor, Transmission (Torque coupling and Final drive) and
Energy Storage (Battery pack). The limiting factor of a BEV is its range. More range requires a larger
battery pack, which is generally heavy and requires a lot of cargo space. For this reason, while designing
the BEV emphasis was placed on the quest to find the optimal battery pack size to meet the range
requirements.
3.1 Modeling an Electric Powertrain
1) Motor
The two most common choices of motors for a BEV is an Induction Motor or a Permanent
Magnet (PM) DC Brushless Motor. In a DC brushless motor achieving stability over the entire
torque-speed range and over temperature is easier compare to an induction motor. This means
added development costs, but likely little or no recurring costs. Also a power factor of unity can
be archived whereas an induction motor can achieve only 85%. The peak power output of the
motor was chosen to be 120 kW as it has sufficient power to meet the acceleration and top
speed required and not use too much power form the battery pack. The minimum voltage of
operation was 240V. A dc brushless motor has a power density of 2.4 kW/kg based on this the
motor mass was calculated to be 50 kg. Similarly the motor controller has a power density of
0.1325 kW/kg, thus the calculated motor controller mass is 15.9.The controller regulates current
flow to the motor based on the driver’s input on the throttle.
2) Transmission
The transmission consists of the Torque coupling (Gear Box) and the final drive (Differential).
The gear box chosen was a standard fixed gear with a ratio of 1.6 and the final drive was 3.63.
The masses of the torque coupling and the final drive train were 10 kg and 20 kg respectively.
There masses were determined based on the masses of transmission systems with the same
configuration available in the market today.
3) Battery pack
The battery pack has always been the bottleneck (critical component, or most significant
component?) of BEV design. In order to select the optimal battery energy capacities a
parametric study was made on different Battery sizes.
A basic module was made by connecting 105 lithium ion cells in series. Each cell has a mass of
0.5kg and nominal voltage of 3.3 V and 19 Ah capacity each were connected in series to make a
module, which has a potential difference of 346.5 V at its terminals. This module has a battery
energy capacity of 6.58 kWh. A parametric study was conducted by calculating the range of the
vehicle in the combined test without passengers (55% UDDS 45%HWFET) and incrementally
adding an extra module. The graph in (xx) shows the results of the parametric study. From the
14
study we can conclude that a battery pack of 5 modules in parallel is required to meet the target
range with four passengers in the vehicle.
450
400
Required
range
350
Range (km)
300
Calculated
range
250
200
150
100
50
0
0
1
2
3
4
5
Number of modules in parallel
Figure
Thus a battery pack made by connecting five of the constructed modules in parallel based on the
results of the parametric study. The capacity of the battery pack chosen was 32.9 kWh
The mass of the battery pack calculated below (see
Battery mass = (mass of each cell) x (no. of cells in series) x (no.of
modules in parallel) x (packaging factor)
(Eqn. 4)
Battery mass = (mass of each cell) x (no. of cells in series) x (no.of
modules in parallel) x (packaging factor)
=0.5 x (105) x (5) x (1.25) = 328.125 kg.
(Eqn. 4)
):
Table
Mass of Vehicle
Component
Energy Storage (Battery)
Electric Motor
Mass (kg)
328.125
50.00
15
Motor Controller
15.9
Electric Powertrain Gearbox
10.00
Final Drive
20.00
Glider Vehicle Mass
1500
Total Mass of the BEV
1924
3.2 Model Assumptions, Limitations, and Sensitivities:
Assumptions:






The mass of the glider vehicle is 1500 kg without the electrical powertrain.
All the components chosen in the powertrain are compatible with each other.
The rolling friction in the road load equation is constant.
The vehicle is large enough to hold the battery pack and have space for 4 passengers.
AC to DC conversion in the charging module is 90% efficient.
The energy losses due to cable resistance and heat is considered trivial
Limitations:

The performance numbers are all simulated in Autonomie. The actual BEV, due to the many
performance factors beyond the scope of this problem, may not give the same results as the
ones simulated.
Sensitivities:


The size of the battery pack was carefully chosen. Increasing the size may increase range but
will adversely affect top speed, acceleration, and cargo space.
The assumption that the rolling resistance is always constant, despite vehicle’s velocity. If
the rolling resistance is a function of the vehicle’s acceleration, the performance will
decrease significantly.
3.3 Energy Flow
The energy flow in the BEV is represented pictorially in
Figure 3. Energy storage (Battery Pack) is charged from the wall through an AC charger that has 90%
efficiency. The energy storage powers the motor through the motor controller that regulate the
voltage to the motor depending on the drivers tactile input on the throttle. The motor is meshed
with a fixed gear Torque coupling which is drives the wheel through the final drive (Differential).
Figure 3: BEV System Components
16
3.4 Performance Tests
The BEV was subjected to basic test cycles such as the acceleration, top speed, consumption, and
gradeability tests (see Table 9). Summing the mass of the powertrain components with the glider
vehicle’s mass has resulted in a total mass of approximately 1924 kg (table xx). The vehicle manage to
achieve a top speed of 137 kph and a 0-60mph acceleration time of 10.5s
Table 9: Reporting Results and Powertrain Sizing
Test mass, kg
1924
Top speed, kph (mph)
137 (86)
Acceleration 0–60 mph, s
10.5
Highway gradeability at 60 mph at test mass, %
15.5
Powertrain configuration
BEV Fixed Gear
Powertrain sizing:
Motor peak power, kW
Transmission, gearing
Battery energy capacity, kWh
Battery mass, kg
120
1.6 (Fixed)
32.9
328.125
17
The BEV was subjected to the three certification test cycles: UDDS, HwFET and US06. The energy
consumption results are shown below (see Table 10).
Table 10: Drive Cycle Energy Consumption Results
Test Mass (kg): 1924
Engine Size (kW): 120
Cycle Distance
Unit
UDDS
HwFET
Combined
US06
km
11.97
16.49
14.01
12.87
Wh/km
27.38
64.67
44.16
96.23
Battery energy
DC Wh/km
97
99
98
167
AC grid energy
AC Wh/km
105
108
106
181
g CO2 eq/km
66.3
67.6
66.9
107.9
Regenerative. braking energy
recovered at the Battery
Wh
661.93
214.75
752.34
779.02
Distance travelled with
regenerated energy during
cycle
km
6.82
2.16
7.67
4.66
Total Range (Calculated)
km
397
343.7
367.2
250.74
Total distance travelled by
regenerative power
(Calculated)
km
226.19
45.02
201.03
90.78
Net tractive energy
(Power at wheels)
GHG WTW
A combined study was made on the BEV with 2 extra passengers. The weight of each passenger was
assumed to be 80 kg, the following results recorded.
Test Mass: 2084
Engine Size:120
Top speed, kph (mph)
Acceleration 0–60 mph, s
Range, km
137 (86)
11.3
353
It was observed that even though the range requirement was met the BEV failed to meet the
acceleration requirement. Also the BEV clearly exceeding the gross vehicle weight rating (GVWR) which
is 2000kg. The battery pack was too large and heavy that it was affecting the performance of the vehicle.
3.5 Energy balance:
Energy out of the system is calculated as below (equation)
18
𝐸𝑛𝑒𝑟𝑔𝑦𝑜𝑢𝑡 = 𝐵𝑎𝑡𝑡𝑒𝑟𝑦𝐸𝑛𝑒𝑟𝑔𝑦 × 𝐸𝑓𝑓𝑚𝑜𝑡𝑜𝑟 × 𝐸𝑓𝑓𝑔𝑒𝑎𝑟𝑏𝑜𝑥 × 𝐸𝑓𝑓𝑓𝑖𝑛𝑎𝑙 𝑑𝑟𝑖𝑣𝑒 × 𝐸𝑓𝑓𝑡𝑜𝑟𝑞𝑢𝑒 𝑐𝑜𝑢𝑝𝑙𝑖𝑛𝑔
− 𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑎𝑙 𝐴𝑐𝑐𝑒𝑠𝑠𝑜𝑟𝑦𝐸𝑛𝑒𝑟𝑔𝑦
Table yy. Component efficiencies in different cycles
Component
Effiency(%)
AC Charger
92 %
Energy Storage
98.8 %
Motor
85.24 %
Torque Coupling
92.6 %
Final Drive
95.8 %
Wheel
65.5 %
* 8% of the battery energy is consumed
by the electronic accessories of the
BEV.
These efficiencies give a satisfactory validation of the designed model.
4 Series Hybrid Electric Vehicle Performance and Energy Consumption
This model is of a midsize 2-wheel-drive Hybrid Electric Vehicle (HEV) with a gasoline-electric series
hybrid powertrain connected to the wheels via a single-speed gearbox. The model was made using
Autonomie. In this case, the electric motor provides power directly to the wheels and acts as the
vehicle’s regenerative brake, while the engine is only used to generate electrical power.
The total vehicle test mass is based upon the test mass for the previous test vehicles combined with the
mass of the electric drivetrain components used. This assumes that the combustion drivetrain used in
this model is of similar mass to those used in the glider vehicle and the conventional vehicle models.
4.1 Engine-Generator System
To emulate an engine-generator system, we are using an engine modeled after a 2004 1.5L Prius engine
and a generator modeled after the UQM Powerphase 75 kW. They are joined in a 1:1 ratio. As per the
problem’s requirements, the engine was scaled up to a maximum power of 100 kW from the stock 57
kW, and the generator was scaled up to 105 kW.
The engine-generator coupled system is added to the battery electric vehicle in order to provide power
input to the high voltage bus, where it can be used by the electric drivetrain or stored in the HV battery.
This has the potential to increase the range of the vehicle, as gasoline has a much higher energy density
than a battery, but can be used with less loss in a generator, rather than in an internal combustion
system.
19
4.2 Battery and general Hybrid Characteristics:
The lithium-ion battery pack will discharge until it is close to 30% charge, and during this discharge
period the engine will be engaged for periods of high power demand such as freeway merging. After
this period, the engine runs whenever the state of charge (SOC) falls below the 30% mark.
For the drive cycles in this problem, the vehicle begins with the battery 70% charged, simulating a
consumer taking the vehicle out from home or a workplace after a previous average drive cycle. For
cycles where the vehicle finishes at a different state of charge, the energy difference in the ESS is used
to correct the results for the discrepancy from the 70% charge level.
The mass of energy storage system is based upon the mass of the Li-ion cells (0.37824kg each) with a
packaging factor of 1.25 to account for additional battery pack components. Via a simple mass
multiplication, this yields a mass of:
(113) × (0.37824 𝑘𝑔) × (1.25) = 53.4264 𝑘𝑔
The mass of electric motor is based upon the measured mass of a 2004 Prius motor scaled linearly with
power to the 100 kW used. The aforementioned component masses are detailed in Table 11.
Table 11: Component Masses
Electric Drivetrain Component Masses
Combustion Component Masses
Component
Mass (kg)
Component
Mass (kg)
Electric Motor
80.26
Combustion Engine (kg)
50.00
Motor Controller
30.10
Generator (kg)
40.00
Electric Powertrain Gearbox
10.00
Engine-Generator Gearbox
10.00
Power Converter
30.00
Fuel + Tank (42.529 gas) (kg)
62.53
Energy Storage
53.43
Total:
203.79
Total:
212.53
4.3 Model Assumptions, Limitations, and Sensitivities:
Assumptions:



The mass of the conventional vehicle without electric drivetrain is 1500kg
The mass of the battery pack components is a function of an individual battery cell’s mass
Rolling resistance is constant with no relation to velocity
Limitations:

The performance numbers are all computer simulated, the actual BEV modeled with the
same design may not give the same results as the ones simulated.
20
Sensitivities:


This model is sensitive to rolling resistance and frontal area, signifying that tire choice and
aerodynamic properties of an actual car based off of this model would need to be carefully
planned
The mass of the model has a large effect on performance and energy usage.
4.4 Modeling a Series Hybrid
Using the conventional vehicle and the masses in Table 11: Component Masses we can find the
estimated mass of the series hybrid vehicle. Using that mass and the powertrain sizing in Table 12
Results and Powertrain Sizing for a Baseline Load-Following Series HEV the rest of the required data can
be found. Acceleration and top speed values were found using Autonomy simulations. The necessary
calculations for highway gradeability are as follows: The necessary calculations for highway gradeability
are as follows: Creating a baseline model of a series hybrid, we arrive with the following data (see Error!
Reference source not found.).
Assuming 80% efficiency of generating, then efficiency of propulsion, we set the maximum power
output of the vehicle times this overall efficiency equal to the energy it must overcome.
𝜂𝑡𝑜𝑡𝑎𝑙 = 𝜂𝑔𝑒𝑛 × 𝜂𝑝𝑟𝑜𝑝 = 0.8 × 0.8 = 0.64
𝐹𝑇𝑟 = 𝑚𝑔 𝐶𝑟𝑟 + ½ 𝜌 𝐶𝑑𝐴𝑓 𝑣 2 + 𝐹𝑖𝑛𝑒𝑟𝑡𝑖𝑎𝑙
𝑃max × 𝜂𝑡𝑜𝑡𝑎𝑙 = (𝐹𝑇𝑟 + 𝑚𝑔%𝑔𝑟𝑎𝑑𝑒 ) × 𝑉
21
m
1
𝑘𝑔
m 2
0.64 × 100 kW = (1704 kg × 9.81 2 × 0.009 + × 1.2 3 × 0.75 m2 × (26.82 2 ) + 0 + 1704 kg
s
2
𝑚
s
m
m
× 9.81 2 × %grade ) × 26.82
s
s
%𝑔𝑟𝑎𝑑𝑒 = 𝟏𝟏. 𝟒𝟒%
We solve for the maximum grade = 11.44%. This is well above the required 3.5%.
Combining all of these results, we get the data in .
Table 12. A representative powertrain configuration is shown in Figure 4.
The specified drive cycles were then run using Autonomy. The results can be found in Table 13: Drive
Cycle Energy Consumption Results.
Table 12 Results and Powertrain Sizing for a Baseline Load-Following Series HEV
Test mass, kg
1703.79
Top speed, kph (mph)
Acceleration 0–60 mph, s
133.5 (83.0)
9.4
Highway gradeability at 60 mph at test mass, %
Powertrain configuration
Series Hybrid
Single-speed HEV
Powertrain sizing:
Engine peak power, kW
100
Generator peak power, kW
105
Motor peak power, kW
100
Transmission, gearing
1.6
Battery energy capacity, kWh
3
Battery peak power, kW
50
Battery mass, kg
53.43
22
Figure 4 : Powertrain configuration and power flow of a series hybrid
Table 13: Drive Cycle Energy Consumption Results
Test mass (kg): 1,650
Units
UDDS
HwFET
Combined
Motor Size (kW): 50
US06
Net tractive energy
Wh/km
44.16
104.21
71.18
155.04
Fuel energy
Wh/km
284.58
320.23
300.67
445.01
Battery energy
DC Wh/km
.19
.09
.15
.04
AC grid energy
AC Wh/km
0
0
0
0
GHG WTW
g CO2 eq/km
123.1
138.4
130.0
192.2
Range
km
1268
1128
1201
812
Gas Equivalent Fuel
Economy
Mi/gal
52.82
46.99
50.02
33.83
23
4.5 Thermostatic-on/off and Load-Following Engine-Control Strategies
To comparing these two hybrid control strategies, we must look into the behavior of the engine in each
case. In addition, it is important to understand the characteristics of the combustion engine. The above
model characteristics were tested using the UDDS drive cycle to gather the data for this section.
With the load following strategy, the battery runs under a narrower charge range, therefore the engine
can only run slightly more than it would under the same circumstances for a conventional vehicle. This
reduces noise, vibration, and harshness (NVH) and allows the vehicle to drive with the engine off at
times, but does not allow the engine to run at maximum efficiency.
Conversely, the thermostatic strategy runs the engine in bursts, running only at the point of peak
efficiency and regaining as much state of charge as possible. This can be seen in both the fuel rate and
power output graphs (see Figure 7 and Figure 8). The benefits are evident when we look at the engine
efficiency graph (see Figure 5). All of the low efficiency points correspond to periods where the loadfollowing strategy requests minimal power from the engine (reflected in Figure 7). The thermostatic
control strategy totally eliminates this type of situation, and therefore increases the average efficiency
of the engine. By maximizing efficiency, the controller also allows the battery to charge more rapidly
(see Figure 10), thus decreasing the total time that the engine must be run.
The overall benefits of thermostatic engine control can be seen most clearly when comparing the fuel
used over the UDDS drive cycle (see Figure 9).
Figure 5: Engine Efficiency
Figure 6: Engine Power Loss
24
Figure 7: Engine Power Output
Figure 8: Instantaneous Fuel Usage
Figure 9: Cumulative Fuel Usage
25
Figure 10: Energy Storage State of Charge
4.6 Energy balance within the vehicle
The energy balance from fuel to wheels may be broadly described by the equation below. (see Error!
Reference source not found.).
𝐸𝑛𝑒𝑟𝑔𝑦𝑜𝑢𝑡 = 𝐹𝑢𝑒𝑙𝐸𝑛𝑒𝑟𝑔𝑦 × 𝐸𝑓𝑓𝑒𝑛𝑔𝑖𝑛𝑒 × 𝐸𝑓𝑓𝑚𝑒𝑐ℎ𝑎𝑛𝑖𝑐𝑎𝑙 𝑎𝑐𝑐𝑒𝑠𝑠𝑜𝑟𝑦 × 𝐸𝑓𝑓𝑔𝑒𝑛𝑒𝑟𝑎𝑡𝑜𝑟 × 𝐸𝑓𝑓𝐸𝑆𝑆 × 𝐸𝑓𝑓𝑚𝑜𝑡𝑜𝑟
× 𝐸𝑓𝑓𝑔𝑒𝑎𝑟𝑏𝑜𝑥 × 𝐸𝑓𝑓𝑡𝑜𝑟𝑞𝑢𝑒 𝑐𝑜𝑢𝑝𝑙𝑖𝑛𝑔 − 𝐸𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑎𝑙 𝑎𝑐𝑐𝑒𝑠𝑠𝑜𝑟𝑦 − 𝐸𝑏𝑟𝑎𝑘𝑒𝑠
Overall, the above energy out differs from the net tractive energy over the UDDS drive cycle by 2.14
Wh/km, or 4.86%, which is within realistic bounds. It is assumed that the model efficiencies are roughly
the same during each drive cycle.
. . . (see Error! Reference source not found.).
4.7 Meeting Design Targets
This new model was arrived at by decreasing the size of both the combustion engine and the generator
and reducing fuel capacity.
The generator output required for maintaining a 3.5% grade at 60 mph indefinitely is as follows:
Prequired ∗ ηtotal
𝑃𝑟𝑒𝑞𝑢𝑖𝑟𝑒𝑑
m
1
𝑘𝑔
m 2
= (1704 kg × 9.81 2 × 0.009 + × 1.2 3 × 0.75 m2 × (26.82 2 ) + 0 + 1704 kg
s
2
𝑚
s
m
m
× 9.81 2 × 3.5%) × 26.82
s
s
=47.8 kW
This means that as long as the generator is outputting 47 kW of power, the vehicle can travel up this
grade using entirely fuel energy. This prevents the battery SOC from dropping too low and prevents the
engine from running constantly when the vehicle is moving uphill.
26
Reducing the engine and generator sizes to 50 kW and 56 kW respectively will reduce the component
masses from 200kg and 40 kg to 100 kg and 30 kg. This is still greater than the minimum required to
drive at a 3.5% grade for 20 minutes at 60. Furthurmore, it is large enough to fully power the electric
motor without modifying the 50 kW ESS.
Because the baseline model’s range was so high, the fuel tank can be reduced from 15 gallons to 6
gallons and still comfortably meet the range requirement, even after decreasing the gear ratio for better
acceleration. This reduces fuel mass from 42.5 kg to 17 kg. We will assume that the fuel tank will only
reduce in size from 20 kg to 12 kg due to additional components such as the filter and pump.
This reduces the overall vehicle mass from 1703.79 kg to 1568.27 kg (see Table 14, Table 15).
Table 14: Series Hybrid Model Specifications to meet Design Targets
Parameter
Modeled
Test mass, kg
1568.27
Top speed, kph (mph)
142 (88.2)
Acceleration 0–60 mph, s
8.9
Highway gradeability at 60 mph at test mass, %
12.5%
Transmission, gearing
1.5
Battery energy capacity, kWh
3
Battery peak power, kW
50
Battery mass, kg
53.43
Fuel Capacity, gal
6
Table 15: Tuned Series Hybrid Model vs. Design Targets
Parameter
Modeled
Target
Combined Energy consumption (Wh/km)
Combined GHG emissions, g CO2 eq/km (/mi)
Range, km (mi)
Top speed, kph (mph)
Acceleration 0–60 mph, s
Highway gradeability at 60 mph at test mass, %
350.2
<370
112.8 ()
<120 (200)
553 ()
>320 (200)
142 (88.2)
>135 (85)
8.9
<11
12.5%
>3.5
4.8 Trade-offs between battery sizing, mass, and losses versus engine generator losses
At this battery size (3kWh), the electric energy density is 0.06 kWh per kg, while that of conventional
gasoline (in a 15 gallon tank) is 8.12 kWh per kg.
On the other hand, it is more efficient to use the electric energy within the car. For instance, the engine
used in this model is 35.74% efficient, while the motor is 81.82% efficient. This means that the process
of generating that electric power and converting it from AC to DC for storage only needs to be 43.68% to
match the combustion process. This is because the only significant losses within the electric powertrain
27
are heat output from the energy storage, motor, and charger, as well as the friction internal to the
motor.
The additional mass required by a hybrid electric vehicle powertrain over a conventional combustion
vehicle due to the battery pack is worthwhile because of the ability to regenerate energy while braking
the vehicle and then use that energy for propulsion. This effectively increases the amount of battery
energy per mass, since the same mass can, over a vehicle drive cycle, hold more energy than its actual
capacity.
5 Innovative Technologies to Reduce Fuel Consumption
Reducing fuel consumption is significantly easier to accomplish if the cost of the resulting vehicle is not a
concern. For example, powertrain electrification and vehicle structure light-weighting using advanced
materials are two expensive technologies that improve fuel economy. However, it is also important to
consider the potential efficiency impacts of applying other more cost-effective innovative technologies
to a conventional vehicle. It may be possible to create a conventional ICE vehicle that approaches the
vehicle design targets by combining these innovative technologies before attempting more expensive
alterations. There is a large body of research that investigates the potential impacts of innovative
technologies. The following section summarizes a small selection of these technologies along with their
potential effects on both hybrid and conventional vehicles.
5.1 Potential Technologies
From a short literature search, the following innovative technologies have the potential to reduce the
fuel consumption of an automobile without incurring substantial cost increases.
Engine start/stop to cut out idle time
Engines in newer conventional vehicles are becoming more reliable and less polluting to start/stop, so in
theory a conventional vehicle could utilize start/stop control logic at stop signs to avoid engine idle time
(ANL dyno study of three vehicle platforms w/ start stop, will add citation)
Diesel or biodiesel as primary fuel source
Diesel engines are inherently more energy efficient than gasoline engines, and diesel fuel is more energy
dense by volume than gasoline.
Deceleration Fuel Cutoff
During vehicle coasting (throttle = 0%) or during braking, the fuel supply to the engine can be cut off
completely. The engine is kept at speed by maintaining the mechanical linkage to the road, so as soon
as the vehicle requests throttle again, fuel supply returns and the engine can run normally (ANL study
citation to be added).
From “Multiple Auto Technologies Study”
From a reference peak of 38% for direct-injected SI engine efficiency, peak efficiency increases to 41%
by 2045. The 41% efficiency level will likely also require advances in in-cylinder monitoring and control,
possibly with camless valve actuation, to allow some use of more efficient thermodynamic cycles (e.g.,
HCCI operation) other than the conventional Otto cycle (Multiple Auto Tech citation to be added).
28
Implementing Driver Feedback to Influence Driving Habits
One of the largest losses in a conventional vehicle is when vehicle deceleration is provided by hydraulic
braking. If a driver can be influenced or changed to avoid using hydraulic brakes either partially or
entirely, the fuel economy of a given drive should increase. One way to influence a driver is to give
realtime feedback during driving that suggests how to change their driving style to reduce fuel
consumption (Comprehending consumption… Citation to be added). Another method is to allow for an
automated self-driving car to take control of the vehicle. This allows for a high level of optimization
when compared to a human driver, because an automated driver would have access to specific
efficiency maps at all times.
5.2 Explored Innovative Technology – Automated Driving
Minimal added mass – an array of sensors
Minimal aero change – a final version of the sensory arrays won’t be obtrusive
Cost may be detrimental, especially during development, but the required sensors can be cheap and the
end cost will decrease as implementation increases on production vehicles.
Once the vehicle is able to control its own drive cycle, it can plan a point-to-point route in the most
efficient way possible, with the intent of eliminating the hydraulic brakes entirely.
Study Parameters
Feed Forward Coast-down Driver Justification and Limitations
Results
Table 16: Coast Down Automated Driver vs. Normal Driver UDDS Results
Parameter
Normal
Coast Down
29
Driver
Driver
Energy consumption (Wh/km)
632
574
Percent Time Trace Missed by 2mph
26.6
0
7
7.43
Distance Traveled (miles)
Limitations of Results
6 Proposed Powertrain Design to Meet EcoCAR3 Design Targets
To gain a better understanding of current hybrid electric vehicle technologies, the team decided to
explore multiple fuel options and control schemes across the three architectures explored. Due to its
high well to wheel emissions, standard E10 gasoline was ruled out as a fuel choice for these vehicles.
The remaining fuel options, B20 and E85, were both explored under different architectures. In addition,
to these different fuels, different control schemes were explored as well. Both a Plug-In and a standard
hybrid electric vehicle were explored in similar configurations to better understand the efficiency
changes in different components of the vehicle. The three resulting vehicles are detailed below.
1.) Parallel Through The Road Plug-In Hybrid Electric Vehicle with a Belted Alternator Starter
running on E85 gasoline
2.) Series Plug-In Hybrid Electric Vehicle running on B20 Diesel
3.) Parallel Through The Road Regular Hybrid Electric Vehicle with a Belted Alternator Starter
running on E85 gasoline
6.1 PHEV Parallel Through the Road with BAS – E85 (Megan)
This architecture has an E85 internal combustion engine (ICE) in the front of the vehicle. Attached to
the ICE is a Belted Alternator Starter (BAS). The BAS provides low end torque to start the vehicles
propulsion. This adjusts the torque curve of the ICE upwards and improves its efficiency without
requiring a large amount of electrical energy as a trade-off. In addition to the front powertrain, this
vehicle also has an electric powertrain over the rear axle. The electric powertrain is powered by a large
battery pack that can be plugged in and charged. When enough charge is available, the vehicle operates
in a charge depleting range using mainly the electric motor for propulsion. When the pack is near the
lower state of charge limit, the vehicle runs in charge sustaining mode switching between the engine
and electric motor. The battery pack also powers the BAS, which in certain configurations can also serve
as a generator to recharge the pack whenever it isn’t providing additional low end torque to the front
axle. A diagram of this architecture can be seen below in Figure 11.
30
Figure 11: PTTR PHEV with BAS Powertrain Diagram
The powertrain components for this specific vehicle were chosen to balance improved efficiency and
increased cost. The overall goal was to slightly exceed the competition design targets. Components
were sized to produce acceleration, range, fuel efficiency, and top speed that consumers would
appreciate in a production vehicle. Table 17 outlines the powertrain sizing and general vehicle
characteristics.
Table 17: Powertrain Sizing
Test mass, kg
Top speed, kph (mph)
Acceleration 0–60 mph, s
1703.79
133.5 (83.0)
9.8
Highway gradeability at 60 mph at test mass, %
Powertrain configuration
PTTR PHEV
w/ BAS
Powertrain sizing:
Engine peak power, kW
100
Generator peak power, kW
105
Motor peak power, kW
100
Transmission, gearing
1.6
Battery energy capacity, kWh
Battery peak power, kW
Battery mass, kg
3
50
53.43
31
Using these specifications, the vehicle was run to determine performance and energy consumption for
combined city and highway driving cycles. A GREET Model spreadsheet was used to calculate WTW
emissions. To display the effectiveness of the chosen powertrain specifications, they are compared to
the EcoCAR3 design targets in Table 18 below.
Performance/Utility
Category
Table 18: EC3 Modeling Targets
Vehicle Modeling Design Targets
Energy consumption
(unadjusted energy use on
combined Federal Test
Procedure [FTP] city and
highway cycles)
Better than 370 Wh/km (600 Wh/mi)
combined city and highway (55%/45%,
respectively)
GHG emissions (WTW
combined city and highway
cycles)
Less than 120 g of carbon dioxide
equivalent (CO2 eq)/km (200 g CO2
eq/mi)
Interior size/number of
passengers
Minimum of four passengers
Luggage capacity
More than 230 L (8 ft3)
Range
Greater than 320 km (200 mi)
combined city and highway
Top speed
Greater than 135 kph (85 mph)
Acceleration time of 0 to 97
km per hour or kph (0 to 60
mi per hour or mph)
Less than 11 seconds
Highway gradeability (at
gross vehicle weight rating
[GVWR])
Greater than 3.5% grade at a constant
97 kph (60 mph) for 20 minutes
Proposed Architecture
Comparison
Table 19: Drive Cycle Energy Consumption Results
Test Mass (kg): 1,500
Engine Size (kW): 100
Unit
UDDS
HwFET
Combined
US06
Net tractive energy
Wh/km
31.211
73.60
54.67
109.544
Fuel energy
Wh/km
-------
---------
--------------
------
Battery energy
DC Wh/km
89.85
104.46
98.31
168.83
AC grid energy
AC Wh/km
99.83
116.066
109.23
187.58
g CO2 eq/km
-------
--------
-------------
-------
Wh
561.88
191.7
752.34
627
GHG WTW
Regen. braking energy
32
recovered at the Battery
Range
km
11.9
16.50
28.34
12.8
Trade-Offs
Discuss trade-offs for component sizing, mass, performance, energy consumption, and losses.
Cost Increases
Consider incremental cost increase resulting from implementing advanced tech at the consumer level.
Innovative Design Features
6.2 Series PHEV 2-Speed w/ 200 kW motor and B20 Diesel (Brian)
The intention of this vehicle architecture is to create a fast yet fuel-efficient plug-in hybrid electric with a
relatively large charge depleting range. In order to achieve a greater top speed, a 2-speed gear box is
used in conjunction with a 200 kW traction motor. Furthermore, a 1.7 L, B20 biodiesel engine capable of
outputting 96 kW of power is used to generate power for the electric powertrain.
Trade-Offs
With the use of a series architecture, an additional motor/generator is used to power-couple the B20
diesel engine and the electric powertrain. As a result, vehicle mass is increased, where a parallel
through-the-road vehicle architecture does not have such a weighting problem as power-coupling is
done through the road. Another issue associated with the use of a series architecture pertains to the
energy losses seen in the inverters and motors used to transfer energy from the engine to the wheels.
Because there is an additional generator/motor in the power-couple between the engine and the
electric powertrain, inefficiencies pertaining to motor and inverter losses are doubled. Furthermore, the
vehicle uses a B20 biodiesel engine to reduce greenhouse gas emissions in the fuel-refinement process.
However, B20 is not the most environmentally-friendly fuel available in comparison to E85. Finally, in
order to increase charge depleting range, a larger battery pack is used, which also increases vehicle
mass.
Innovative Design Features
In order to account for the added weight from the additional motor/generator and larger battery pack,
lightweighting the vehicle is of the upmost importance. The goal is to keep the car below 1950 kg.
Technologies such as, lighter composite materials, lighter component mounting brackets, smaller, high
ampacity high-voltage conductors, and passively cooled battery packs can be used to shed unnecessary
poundage. Furthermore, using a B20 biodiesel engine is a more fuel efficient way of generating energy
for the electric powertrain. In addition, a urea injector is used to even further reduce wheel-to-well
greenhouse gas emissions by absorbing larger amounts of diesel particulates.
Cost Increases
Consider incremental cost increase resulting from implementing advanced tech at the consumer level.
33
The price of a 200 kW motor with a matching inverter is roughly X dollars from Remy Motors and
Rhinehart Motion Systems respectively. In comparison to the price of our current EcoCAR2 inverter and
motor from the same manufacturers, there would be an increase of Y dollars with the use of a more
powerful electric drivetrain modeled in this series architecture. In addition, the cost of a 24kWh battery
pack from A123 is around A dollars. In comparison to the price of the 19.5 kWh batteries pack from
A123 for EcoCAR2, there would be an increase of B dollars for the larger battery pack also seen in this
series architecture. Furthermore, the cost of technologies to lightweight the vehicle is a concern as well.
Composites such as carbon fiber can cost upwards of C dollars/gram. In addition, high-ampacity, lowgage conductors, high voltage, conductors cost more than standard conductors at D dollar per foot.
Powertrain Configuration
Table 20 summarizes the vehicle architecture used in this series PHEV 2-speed model.
Table 20: Results and Powertrain Sizing for a Baseline Load-Following Series HEV
Test mass, kg
1925
Top speed, kph (mph)
133.5 (83.0)
Acceleration 0–60 mph, s
9.4
Highway gradeability at 60 mph at test mass, %
>5.0
Powertrain configuration
Series Hybrid 2-speed PHEV
Powertrain sizing:
1.7 L, 4-cylinder, B20 Diesel w/
200kW 2-Speed Traction Motor
Engine peak power, kW
65
Generator peak power, kW
65
Motor peak power, kW
200
2-Speed gearing
1st: 2.0
2nd: 1.25
Battery energy capacity, kWh
24
Battery peak power, kW
200
Battery mass, kg
53.43
DIAGRAM OF POWERTRAIN
In Table 21, the vehicle technical specifications are compared against the design targets from the
request for proposal.
Table 21: EC3 Modeling Targets
Performance/Utility Category
Vehicle Modeling Design Targets*
Proposed Architecture
34
Comparison
Energy consumption (unadjusted
energy use on combined Federal
Test Procedure [FTP] city and
highway cycles)
Better than 370 Wh/km (600 Wh/mi)
combined city and highway
(55%/45%, respectively)
GHG emissions (WTW combined
city and highway cycles)
Less than 120 g of carbon dioxide
equivalent (CO2 eq)/km (200 g CO2
eq/mi)
Interior size/number of passengers Minimum of four passengers
Luggage capacity
More than 230 L (8 ft3)
Range
Greater than 320 km (200 mi)
combined city and highway
Top speed
Greater than 135 kph (85 mph)
Acceleration time of 0 to 97 km per Less than 11 seconds
hour or kph (0 to 60 mi per hour or
mph)
Highway gradeability (at gross
vehicle weight rating [GVWR])
Greater than 3.5% grade at a constant
97 kph (60 mph) for 20 minutes
Table 22 summarizes the critical results outputted from our vehicle model during the, UDDS, HwFET,
Combined, and US06 drive cycles.
Table 22: Drive Cycle Energy Consumption Results
Test Mass (kg): 1,500
Engine Size (kW): 100
Unit
UDDS
HwFET
Combined
US06
Net tractive energy
Wh/km
25.3
59.6
54.67
88.8
Fuel energy
Wh/km
-------
---------
--------------
------
Battery energy
DC Wh/km
89.85
104.46
98.31
168.83
AC grid energy
AC Wh/km
140
116.066
109.23
187.58
g CO2 eq/km
106.2
--------
-------------
-------
km
745
16.50
28.34
12.8
GHG WTW
Range
6.3 PTTR HEV + BASS (Megan and Brian)
Trade-Offs
Discuss trade-offs for component sizing, mass, performance, energy consumption, and losses.
Cost Increases
Consider incremental cost increase resulting from implementing advanced tech at the consumer level.
Innovative Design Features
Powertrain Configuration
Table 23: Results and Powertrain Sizing for a Baseline Load-Following Series HEV
Test mass, kg
1850
35
Top speed, kph (mph)
133.5 (83.0)
Acceleration 0–60 mph, s
9.4
Highway gradeability at 60 mph at test mass, %
Powertrain configuration
Series
Hybrid
Singlespeed HEV
Powertrain sizing:
Engine peak power, kW
100
Generator peak power, kW
105
Motor peak power, kW
100
Transmission, gearing
1.6
Battery energy capacity, kWh
3
Battery peak power, kW
50
Battery mass, kg
53.43
DIAGRAM OF POWERTRAIN
Table 24: EC3 Modeling Targets
Performance/Utility Category
Energy consumption (unadjusted energy use on
combined Federal Test Procedure [FTP] city and
highway cycles)
Vehicle Modeling Design Targets*
Better than 370 Wh/km (600 Wh/mi) combined
city and highway (55%/45%, respectively)
GHG emissions (WTW combined city and highway Less than 120 g of carbon dioxide equivalent (CO2
cycles)
eq)/km (200 g CO2 eq/mi)
Interior size/number of passengers
Minimum of four passengers
Luggage capacity
More than 230 L (8 ft3)
Range
Greater than 320 km (200 mi) combined city and
highway
Top speed
Greater than 135 kph (85 mph)
Acceleration time of 0 to 97 km per hour or kph
(0 to 60 mi per hour or mph)
Less than 11 seconds
Highway gradeability (at gross vehicle weight
rating [GVWR])
Greater than 3.5% grade at a constant
97 kph (60 mph) for 20 minutes
Table 25: Drive Cycle Energy Consumption Results
Test Mass (kg): 1,500
Engine Size (kW): 100
Unit
UDDS
HwFET
Combined
Proposed Architecture
Comparison
269 (wh/km)
70.2 (g/km)
n/a
n/a
704 (km)
130 (mph)
7 (seconds)
20.73%
US06
36
Net tractive energy
Wh/km
109.841
97.43
104.3
147.6
Fuel energy
Wh/km
291
242
269
533
DC Wh/km
0
0
0
0
g CO2 eq/km
75.8
63.3
70.2
138.9
km
652
781
704
356
Battery energy
GHG WTW
Range
6.4 Proposed Vehicle Architecture
Discuss how the design meets each requirement. Specify fuels and energy carriers, and why.
Define all components.
Specify charge-sustaining fuel consumption and grid energy use.
Justification
Propose a preferred powertrain design with justification.
Consider: size/mass and packaging, consumer appeal, cost and availability.
Energy and Emissions
Discuss how energy management/control strategy was modeled.
Exhaust emissions need not be modeled, but mention any likely effects of the design
Exhaust emissions need not be modeled, but mention any likely effects of the design.
7 Summary and Conclusions
Overview of model and results. Are these useful for powertrain design within model limitations? What
was learned? Summarize energy source and powertrain designs, Tradeoffs with respect to cost and
innovation.
Why does this report demonstrate your merit for EC3?
37
Appendix A: Problem 1: Simulink Model
Figure 12: Problem 1 Vehicle Model; Simulink UPDATE!!!
38
Appendix B
Figure 13: Torque, efficiency, and peak power point
Figure 14: Engine for Model 2 (Autonomie)
39
Appendix C: WTW Energy Consumption Calculator
40