A Study in Reducing the Cost of Vertical Flight with Electric Propulsion

Abstract

Electric propulsion introduces potential to substantially alter the design of vertical lift vehicles for reduced cost.
A substantive operating cost improvement is hypothesized as a result of decreases in the cost of energy for flight propulsion, reduced maintenance hours, and reduced unique part count for electric vertical takeoff and landing (VTOL) aircraft (Figure 1). Energy sourced from the electrical grid costs as little as 30% of equivalent energy delivered from aviation fuel [1]. Considering that fuel typically comprises 20% of rotorcraft operating costs, this 3x reduction in energy costs offers the potential for a direct 6% reduction in operating costs for energy alone using pure battery electric configurations [2].
Maintenance and labor hours account for another 20-30% of today’s rotorcraft operating cost [3, 4]. Electric propulsion simplifies power transmission relative to mechanical drive trains and encourages transition from traditional rotor systems with collective and cyclic controls to multi-rotor systems with differential thrust control via variable speed rotors. Maintenance cost reductions are hypothesized to follow in parallel with simplification of the rotor system mechanical components; however, this hypothesis must be examined from the standpoint of reliability of components of the distributed electric propulsion (DEP) system and the appropriate aggregation to system-level reliability.

Full text

Presented at the AHS International 73rd Annual Forum &
Technology Display, Fort Worth, Texas, USA, May 9–11, 2017.
Copyright 2017 by AHS International, Inc. All rights reserved.
A Study in Reducing the Cost of Vertical Flight with Electric Propulsion
Michael J. Duffy
Aerodynamics Engineer
The Boeing Company
Ithaca, NY, U.S.
Roger Lacy
Phantom Works
The Boeing Company
Ridley Park, PA, U.S.
Sean Wakayama
Associate Technical Fellow
The Boeing Company
Huntington Beach, CA, U.S.
Matt Stauffer
Phantom Works
The Boeing Company
Ridley Park, PA, U.S.
Ryan Hupp
Configuration Engineer
The Boeing Company
Hazelwood, MO, U.S.
INTRODUCTION
Electric propulsion introduces potential to substantially alter the design of vertical lift vehicles for reduced cost.
A substantive operating cost improvement is hypothesized as a result of decreases in the cost of energy for
flight propulsion, reduced maintenance hours, and reduced unique part count for electric vertical takeoff and landing
(VTOL) aircraft (Figure 1). Energy sourced from the electrical grid costs as little as 30% of equivalent energy
delivered from aviation fuel [1]. Considering that fuel typically comprises 20% of rotorcraft operating costs, this 3x
reduction in energy costs offers the potential for a direct 6% reduction in operating costs for energy alone using pure
battery electric configurations [2].
Maintenance and labor hours account for another 20-30% of today’s rotorcraft operating cost [3, 4]. Electric
propulsion simplifies power transmission relative to mechanical drive trains and encourages transition from
traditional rotor systems with collective and cyclic controls to multi-rotor systems with differential thrust control via
variable speed rotors. Maintenance cost reductions are hypothesized to follow in parallel with simplification of the
rotor system mechanical components; however, this hypothesis must be examined from the standpoint of reliability
of components of the distributed electric propulsion (DEP) system and the appropriate aggregation to system-level
reliability.
Figure 1: Operating cost components for current vertical lift aircraft and technologies that enable total
operating cost reduction

A multidisciplinary design analysis & optimization (MDAO) sizing tool was developed to explore many
vertical lift vehicle configurations enabled by electric propulsion. Its modular architecture allows assembling a
variety of power train components in arbitrary configurations. Its use of optimization instead of traditional sizing
iteration creates the flexibility to accommodate different configurations by changing an optimization setup.
Variables controlling rotor speed and power distribution between gas engines and electric motors can be introduced
in optimization to handle new approaches for lift trim or to design the mix of fuel and battery energy.
CONFIGURATION REQUIREMENTS
With the recent release of the Uber Elevate white paper [5] defining clearer requirements to build a business case for
an urban air taxi, some key configuration requirements were adopted for an initial study of the operating cost of such
a vehicle.
o Small footprint for landing <50 ft
o >6 Effectors for hover to accommodate a loss of one motor or propeller
o Predictable and safe transition from hover to cruise
o Control authority at low speed for safe weather operations
o Winged flight for >25 miles range to reduce energy consumption
o Rotors away from passengers
o Reduce the number of unique parts
CONFIGURATION SELECTION
Electric propulsion allows new design freedom for locating lift and thrust around the vehicle with little
constraint from the mechanical complexity of drive systems and shafting (Figure 2). Table 1 shows a list of options
considered for fulfilling key functional lift and control functions, highlighting typical selections for a single main
rotor helicopter configuration compared to the potential design selections for an eVTOL (electric VTOL) aircraft.
Transition from hover to forward flight is a critical design decision, since this drives many of the aircraft major
subsystems. This initial candidate configuration was chosen in order to meet the high level requirements defined in
the Uber paper. In order to provide a predictable transition from hover to cruise, a high wing multi-rotor with
stopped hover rotors and a pusher propeller was chosen for this initial costing exercise. Future studies will include a
detailed sizing and costing effort to determine the best configuration for this mission. The purpose of this study was
to get an initial understanding of the sensitivity of operating cost for various electric components.

Figure 2: Candidate configuration species
Table 1: Configuration options for eVTOL aircraft
SMR Helicopter Sample eVTOL
Lift Type Rotor Propeller Ducted Fan Co-Axi al
Rotor Arrangment Single Tandem Quad Distributed
Low Speed Control Thrust Vector Differential Thrust Defle cted Flow Directed Exhaust
Lift Type Rotor Conventional Wing Canard Tandem Wing
Thrust Type Thrust Vectored Propeller Ducted Fan Compressed Exhaust
High Speed Control Tilt Thrust Differential Thrust Aileron, Rud, Elev. Blown Surface
VTOL Systems in Cruise Slow Stop Fold Stow
Shared Device Rotor Tilt Rotor Tilt Fan Tilt Wing
Cruise Systems in VTOL Slow Stop Fold Stow
<---- Functional Choices ---->
VTOL
Systems
Cruise
Systems
Multi-
Mode
MISSION ASSUMPTIONS FOR ELECTRIC VTOL
The mission assumptions for the vehicle studied in this paper are indicated and compared against mission
assumptions for Uber Elevate [5] in Table 2.

Table 2: Mission assumption comparison
Uber Elevate
Commercial Urban Air Taxi (Vision)
Range
100 mi
100 mi
Cruise Speed
170 mph
150 mph
Payload
4 passengers (800 lb)
4 passengers (800 lb)
Cruise Altitude
1000 ft
1000 ft
Footprint
< 50 ft
< 50 ft
Battery Specific Energy
400 Wh/kg
400 Wh/kg
Reserve Cruise
20 min
20 min
Battery Discharge Reserve Limit
20%
20%
Blade Tip Speed
445 ft/s
<500 ft/s
Take-Off Conditions
Sea Level Standard
Sea Level Standard
The mission profile used to size the vehicle is sketched in Figure 3. Hover is modeled for two minutes at the
beginning and ending of the mission to account for takeoff and landing. Acceleration from hover to climb speed is
modeled to provide checks on transition between rotary wing and fixed wing flight. The propeller becomes active at
the end of the acceleration. Climb, cruise, and descent are flown in fixed wing mode with rotors stopped. Distance
traveled in climb and descent is counted toward the 100 mile range. Deceleration from descent speed to hover is
modeled to provide checks on transition. A 20 minute loiter at 500 ft is modeled to account for reserves.
Figure 3: Mission diagram
VERTICAL LIFT DESIGN TOOL
The Vertical Lift Design MDAO sizing tool provides for modeling and optimizing the design of vertical lift
vehicles. It provides analysis components for rotors, engines, motors, generators, and batteries. Connections between
these power train components can be specified to model arbitrary configurations. A vehicle built from the connected
components can be evaluated at many conditions, and the resulting performance can be summed for mission level
values. These core vehicle and mission analyses are integrated with configuration specific analyses for structures
and aerodynamics. The sizing tool is integrated with a nonlinear optimization package that allows vehicle and
mission parameters to be designed with constraints on condition or mission performance to ensure proper sizing of
the power components. This sizing tool has supported various concept studies looking at feasibility and benefits of
electric propulsion on vertical lift vehicles.
The core analyses for the sizing tool are coded in Java classes, indicated in Figure 4. The vehicle is defined by a
set of classes. The rotor class contains characteristics used to evaluate rotor power and performs the evaluation of
rotor weight. The engine class is used for engines, motors, and generators. Weight is obtained through specific
power, and power consumption for motors and generators is handled with a simple efficiency. Engine fuel flow is

varied with power based on a trend from the NDARC (NASA Design and Analysis of Rotorcraft) referred parameter
turboshaft engine model [6]. The battery class evaluates battery weight via specific energy or specific power. The
aerodynamic component class captures drag and lift characteristics. The connection class is used to describe
connections between rotor, engine, and battery components. The ability to define connections provides flexibility to
model different architectures, including parallel hybrid, series hybrid, single main rotor, and multi rotor
configurations. A class for working with spline data allows data from higher fidelity or computationally costly
sources to be incorporated. Evaluation of rotor induced power involves solution of induced velocity via an iterative
process. To save the computational cost associated with the iteration, rotor induced power is captured with a spline
based on pre-computed tabular data. The vehicle class captures miscellaneous information on the vehicle and sums
up component weights.
Problem Definition
Run
Optimization
Vehicle Definition
Battery
Engine
AeroComponent
Rotor
Vehicle
Connection
SplineManager
Mission
Definition Mission
Segment
Condition
Distribution
Objective
Variable Constraint
Figure 4: Modular analysis toolset for electrified vertical lift design
A set of classes evaluates performance over a mission. The classes are arranged in a nested structure. A mission
can be divided into a number of segments. The segment class models a period of time typically dedicated to a task
such as hover, acceleration, climb, or cruise. Inputs for a segment include parameters such as altitude, speed,
acceleration, climb rate, and duration. A segment also includes inputs to control pitch attitude, rotor thrust, and rotor
speed. A segment sets up evaluation of typically two conditions, one at the start and one at the end of the segment.
The condition class evaluates power and fuel flow for a single condition in a segment. Rotor induced power is
evaluated via momentum theory, using the spline described earlier. Rotor profile power is based on blade element
drag given the rotor speed. The rotor speed needed to get the required thrust is solved during optimization. Lift and
lift dependent drag are calculated from flight condition and aerodynamic component characteristics (including angle
of attack, dynamic pressure, lift curve slope, and span efficiency). Longitudinal and vertical direction forces are
balanced during optimization through variation of pitch attitude and rotor thrust. Starting from the rotors, power is
tracked through the connections, with component efficiencies increasing the required power until it can be converted
into electric power drawn from a battery or fuel supplied to an engine. The condition evaluation results in estimates
for battery power and fuel flow. The segment evaluation integrates these estimates into energy and fuel consumed
over the duration of the segment. The mission evaluation sums over segments to get energy and fuel consumed
during the mission.
An optimization problem is described with classes containing information on the optimization objective, design
variables, and constraints. These classes identify the analysis variables to use in optimization, and they specify
bounds and scales applied to those values in the optimization. The optimization class collects the objects describing
an optimization. More than one optimization may be defined. The run class is used to define the series of
optimizations to run.

The sizing analysis is integrated with a numerical optimization package in Jython. A Java based data
management library handles communication of data between analyses and optimization. The data management
library also provides for loading and storing data in XML (Extensible Markup Language) files. A user typically
works with the XML file to define the vehicle and optimization.
Running under Jython allows additional analysis to be implemented in Jython scripts. Buildups for weight and
aerodynamic characteristics are typically generated in Jython scripts before being loaded in vehicle or aerodynamic
component analysis objects.
A series of optimizations is used to size a vehicle, as indicated in Table 3. This approach makes it easier to
troubleshoot problems when the optimizer sees no path to satisfying violated constraints and declares the solution
infeasible. The large problem to be solved at the end of the series is broken into smaller pieces that group design
variables and constraints to solve a part of the design. When one of these smaller problems is infeasible, it is easier
to determine changes to yield a solution. Grey boxes in the table indicate setups and objectives used for the different
optimizations.
Table 3: Optimization series used for sizing
Optimization
Objective
0
1
2
3
4
5
6
7
8
9
design weight
energy
range
Setup
Variables
Constraints
Mass
design weight
design weight difference
Trim
rotor thrust
vertical acceleration difference
propeller thrust
longitudinal acceleration
difference
pitch attitude
wing lift coefficient
Rotor Speed
rotor speed
rotor thrust match
Propeller
Speed
propeller speed
propeller thrust match
Motor
rotor motor max power
rotor motor power fraction
propeller motor max power
propeller motor power fraction
Range
cruise duration
reserve energy
final energy
Rotor
rotor blade aspect ratio
blade lift coefficient
Propeller
propeller blade aspect ratio
blade lift coefficient
propeller design thrust
thrust fraction
Battery
energy
mission range
battery power fraction
The mass setup matches the design weight used to size the structure to the built up vehicle weight. This match is
often handled via iteration in traditional sizing tools, but is more efficiently solved in optimization with a design
weight variable and a constraint that drives the difference between design and actual weight to zero.
The trim setup determines rotor thrust, propeller thrust, and pitch attitude to balance vertical and longitudinal
forces as measured by differences in actual and target accelerations. The target accelerations are typically zero, but
acceleration segments between hover and forward flight are included to provide checks on the ability to transition
from rotary wing to fixed wing flight. Most conditions are modeled with either the propeller or the rotors stopped.
There is an extra degree of freedom for balancing forces when both the propeller and the rotors are used, which
occurs in a couple conditions used to evaluate transition. The optimization is free to use the extra degree of freedom
to improve the objective, typically by reducing power consumption for those conditions. Wing lift coefficient
constraints are included in the trim setup, but the optimization has no way to satisfy the constraints if they are
violated. Such violations signal the need for a manual intervention such as increasing wing area.

The rotor and propeller speed setups solve for the rotor and propeller speeds required to generate the required
thrust through the flight conditions. The difference between the thrust estimated from blade element theory and the
required thrust is constrained to be zero.
The motor setup sizes the rotor and propeller motors to provide adequate power through the flight conditions.
Actual power in a condition is divided by the motor maximum power to get a power fraction, which is typically
constrained to be less than one. The exception is for hover, where the rotor motors are sized to be at 65% to allow
the vehicle to hover on 6 rotors, accommodating the failure of one motor and powering down of another to balance
moments. The extra motor power is also intended to support differential thrust control.
The range setup allows optimizing the duration of the cruise segment while ensuring there is enough battery
energy for reserves. The reserve energy constraint checks that the battery has at least 20% capacity remaining after
the regular mission. Leaving 20% capacity under ordinary operations is intended to preserve battery life. The final
energy constraint checks that the battery is not drained at the end of reserves.
The rotor setup designs the rotor blade aspect ratio so that blades are at a specified lift coefficient in hover. The
specified lift coefficient leaves margin against stall to handle maneuvers and inoperative motor operations. The
design thrust of the rotors, which is used for weight calculations, is set outside the optimization by dividing the
design weight of the vehicle among the rotors.
The propeller setup designs the propeller blade aspect ratio such that blade lift coefficients remain below
specified limits throughout the conditions where the propeller is used. The setup also matches the propeller design
thrust with the maximum thrust in the mission, which is experienced at the top of the climb segment.
The battery setup allows sizing the battery subject to providing enough energy to meet a mission range
constraint and providing enough power throughout the evaluated conditions.
Different objectives are applied as appropriate in the optimizations used to size the vehicle. A design weight
objective is typically used because it encourages reduced sizing of components. It encourages lowering energy
consumption to reduce the size and weight of the battery. The objective does not matter for some optimizations that
are completely driven by constraints (optimizations 0, 2, and 3). A minimum energy consumption objective is used
for solving the initial trim with the sizing of the vehicle fixed. This objective favors solutions that use less power
when there is a choice between using rotor and propeller thrust. A maximum range objective is used to improve the
performance of the vehicle until it can meet the design range. The objective is switched to minimum design weight
when the addition of the battery setup allows designing for a fixed range.

SIZING RESULTS
An initial sizing was performed on a vehicle for the commercial urban air taxi mission. Characteristics of the
sized vehicle are presented in Table 4. The vehicle concept is illustrated in Figure 5. Plots of altitude, velocity, and
shaft power are shown in Figure 6.
Table 4: Sized vehicle parameters
Overall
Hover
Gross Weight
4631 lb
Number of Rotors
8
Operating Empty Weight
3,831 lb
Rotor Diameter
9.0 ft
Airframe Weight
2479 lb
Disk Area
509 ft2
Useful Load
2,152 lb
Blades per Rotor
2
Payload
800 lb
Rotor Blade Tip Speed
457 ft/s
Useful Load Fraction
0.46
Hover Power Required
541 hp
Battery
Lift Motor Rating
142 hp
Battery Weight
1,352 lb
Hover Disk Loading
9.1 lb/ft2
Start Energy
245 kWh
Hover Power Loading
8.6 lb/hp
Final Energy (no reserve cruise)
52 kWh
Cruise
Battery Specific Energy
400 Wh/kg
Number of Propellers
1
Discharge Rate
3.0C
Propeller Diameter
6.5 ft
Reserve Discharge Limit
20%
Disk Area
33 ft2
Wing
Blades per Rotor
8
Span
36.5 ft
Propeller Blade Tip Speed
486 ft/s
Area
172 ft2
Cruise Power Required
202 hp
Aspect Ratio
7.8
Cruise Wing Loading
26.9 lb/ft2
Airfoil Max Thickness
15%
Cruise Lift to Drag Ratio
11.6
Airfoil Max CL (no High-Lift)
1.4
Min Drag Area
5.3 ft2

Figure 5: Vehicle concept
Figure 6: Mission time history plots
Structural weight is based on composite stiffened skins sized to minimum gauge or bending loads. Factors are
applied for internal structure and non-optimal material. Motor weights are derived from specific power applied to
the power required from vehicle sizing. These weights are intended to cover related motor controllers. Rotor and
propeller weight is based on helicopter rotor sizing trends calibrated to data on fixed pitch propellers used on the
Boeing LIFT! project [7, 8]. These weights are lighter than for rotors with collective and cyclic controls.
Allowances for other systems weights and furnishings are listed under equipment weight.

In order to check the validity of the weight estimates, the weight breakdown for the eVTOL aircraft is compared
to other general aviation aircraft in Figure 7. Both fixed wing and helicopter aircraft are compared since the eVTOL
aircraft has features of both types.
Figure 7: Weight breakdown comparison within vehicle weight class
Since battery specific energy is a critical sizing constraint for all electric aircraft, trends for gross weight with
battery specific energy are shown in Figure 8. The trends were estimated with a simpler analysis than the MDAO
sizing described earlier; however, the results from the simpler analysis compare closely with the results from the
MDAO sizing. Vehicle gross weight increases rapidly as specific energy is reduced from the level assumed for the
vision vehicle. Near term, it may make sense to produce a vehicle with reduced range capability, and incorporate
better battery technology later in production or by retrofit for better performance. In the cost studies to follow,
eVTOL aircraft were sized for a range of 100 miles with both 300 Whr/kg and 400 Whr/kg for comparison.
Figure 8: Vehicle gross weight trends with battery specific energy

REDUCING COMPLEXITY
Modern helicopters with traditional fossil fuel engines have hundreds of unique moving parts (Figure 9). The
large number of parts translates into higher maintenance, qualification, and purchase cost compared to electric
aircraft, which tend to have fewer unique dynamic components. Similar to electric vehicles on the ground, these
cost reductions can translate to a substantial cost reduction during the life of the system. For the cost study to
follow, a comparable helicopter is used to evaluate operating cost between traditional fossil fuel propulsion and
electric propulsion. The Robinson R44 is a single main rotor helicopter with a piston engine. The R44 is optimized
for low operating cost with a simple teetering two bladed rotor connected by a transmission to a two bladed tail
rotor. The R44 has a payload of 748 lbs for 4 passengers, and this aircraft could meet the Uber Elevate range and
payload requirements. Figure 10 compares the eVTOL aircraft to the Robinson R44. The eVTOL was sized for 100
miles range with 800 lb payload, while the R44 has a range capability of 300 miles.
Figure 9: A comparison of rotor hub parts between an eVTOL vehicle and the Robinson R44

Figure 10: Vehicle characteristics for a sample eVTOL aircraft compared to the Robinson R44
OPERATING COST
For this cost study, several aircraft were used to compare to the eVTOL aircraft as shown in Figure 11. The eVTOL
aircraft here was sized to only meet the Uber Elevate mission requirements of 800 lb and 100 miles, with reserve.
The other aircraft used in the operating cost comparison have additional capability in range and payload; however,
the eVTOL is sized to cruise at a 150 mph, which is faster than most traditional helicopter cruise speeds. It should
also be considered that the Robinsin R44 has the closest match in capability to the eVTOL aircraft sized for the Uber
Elevate mission. The other aircraft were added because they have similar payload and mission sets as the Uber
Elevate mission, and could be used to operate an air taxi mission.

Figure 11: Weight comparison of eVTOL aircraft with comparable sized helicopters
Total operating cost is comprised of three major components as indicated in Figure 1: variable cost per
hour, fixed cost per year, and book depreciation per year. Variable cost is further comprised of fuel, maintenance,
and overhaul. For piston engine general aviation aircraft like the R44, fuel cost is driven by the price of AvGas.
AvGas as of January 2017 was around $5.82/gal including all taxes and service charges based on a national average
in the United States [12]. For this study an average AvGas and Jet-A cost was used of $5.50/gal. Electricity cost
varies state by state and by end user (commercial vs residential) [13]. For this study a cost of $0.12/kWh was used.
For helicopter maintenance, the R44 website provides a cost per flight hour break down [9]. For turbine
helicopters, Conklin and de Decker provide an estimate based on the vehicle and mission type [3]. For the eVTOL,
engine maintenance is zero; however, electric motor and power electronics are determined by estimating the cost
and life replacement for these components. The cost of the electric components is based on an Airbus Vahana study
[14], which showed $150/kg for motors. This number was tripled to account for the speed controller and propeller
cost as well, so the final cost for propulsion replacement was $450/kg. For this study the motor life was assumed to
be 6,000 hours of flight based on getting 60% of the life expected for a brushless DC motor [15]. This 60% factor is
applied to account for the harsher environment for aviation aircraft. The speed controller and propeller life was
assumed to follow the same life as the brushless DC motor. Battery life, was assumed to be 7,500 cycles based on
the same cells used in the Tesla battery pack, which are Panasonic NCR18650B [16]. Cost per kWh was based on
doubling the cost of the Tesla battery at the pack level to account for aviation qualification [17]. The current Tesla
battery pack is $260/kWh [17], so a battery replacement cost $520/kWh was applied in this study.
Figure 12 shows eVTOL aircraft evaluated with battery specific energies of 400 Wh/kg and 300 Wh/kg
compared to the Robinson R44 and other turbine helicopters. All operating costs were normalized against the
Robinson R44. Both eVTOL aircraft show a reduction in variable operating cost, mostly from reduction of fuel cost
and elimination of engine overhaul.
Yearly fixed costs include avionics, crew, and insurance. For this study, crew and avionics were assumed
to be constant for all aircraft since the mission would be the same. Only insurance varied, since it is based on
vehicle cost. Figure 13 shows only a slight variation in fixed cost between aircraft.

Book depreciation, compared in Figure 14, is based on a vehicle cost and a 10 year amortization. For this
exercise, it was assumed that these vehicles would be manufactured at a rate of 1,000 per year and would follow the
cost curve defined in the Uber Elevate white paper [5]. This cost curve was scaled based on gross weight, and
estimated cost of initial batteries was added. The Uber Elevate vehicle cost curve assumed a 4,000 lb gross weight
aircraft. The Boeing eVTOL vehicles sized to be over 4,500 lb; therefore, a linear scale factor was applied to the
vehicle cost.
Figure 12: Variable operating cost ($/hr) normalized by the Robinson R44 helicopter

Figure 13: Fixed operating cost ($/year) normalized by the Robinson R44 helicopter

Figure 14: Book depreciation cost (10% price/yr) normalized by the Robinson R44 helicopter
Aircraft utilization is critical to total operating cost per mile. Greater utilization increases the number of
miles over which the aircraft cost is amortized. The Uber Elevate white paper specified 2,080 hours of operation per
year [5]. For an all-electric aircraft, this utilization might translate into 6 hours of operation a day with 10 or more
hours available on the ground for charging, 7 days a week.
Finally, when all operating costs (variable, fixed, and book) are rolled together and divided by the number
of passengers per trip mile, a comparison can be made for total operating cost. In Figure 15, the total operating cost
per passenger seat mile for a high utilization business operation is shown relative to the baseline Robinson R44. It is
shown that compared to the R44, the eVTOL has 20-30% reduced operating cost. Book depreciation between the
eVTOL and R44 is similar due to both having a high yearly manufacturing rate. Compared to turbine helicopters
performing a similar mission, eVTOL aircraft are two to three times cheaper to operate.

Figure 15: Cost per seat mile normalized by the Robinson R44 helicopter
In addition to operating cost, other factors may affect adoption of electric VTOL. Noise is expected to be
reduced because electric motors are quieter than piston or turbine engines, and distributed propulsion provides
options for reducing rotor tip speeds. Safety can be improved by having a propulsion system with enough
redundancy to handle failure of a single component. Environmental considerations should be better as the energy for
operating electric vehicles could come from renewable electric power.
CONCLUSION
This initial evaluation of the cost of an electric VTOL aircraft and its operations supports the feasibility of an
electric air taxi vehicle. Total operating cost is reduced relative to equivalent piston aircraft. Other considerations,
including noise, safety, and the environment may also favor adoption.
Next steps for electric VTOL configuration studies should include broader and higher fidelity evaluation to
better understand feasibility. This work should include better definition of the architecture, weights, and reliability
of electric drive systems. Further exploration, including studying alternative concepts, should be done to identify
better ways to meet the mission.
Author contact: Michael Duffy, Michael.j.duffy3@boeing.com; Sean Wakayama, sean.r.wakayama@boeing.com;
Ryan Hupp, ryan.l.hupp2@boeing.com; Roger Lacy, roger.w.lacy@boeing.com; Matt Stauffer,
Matthew.S.Stauffer@boeing.com
REFERENCES
[1] Moore, Mark D. and Fredericks, Bill, “Misconceptions of Electric Propulsion Aircraft and their Emergent
Aviation Markets,” AIAA SciTech, AIAA Paper 2014-0535, 2014.

[2] National Interagency Fire Center, “Helicopter Services,”
http://www.fs.fed.us/fire/contracting/helicopters_exclu/flt_chrt_awarded_2011-2013.pdf, accessed Aug. 2014.
[3] Conklin and de Decker Aviation Information, “Aircraft Cost Summary,”
https://www.conklindd.com/CDALibrary/ACCostSummary.aspx, accessed Aug. 2014.
[4] Fredericks, William J., Moore, Mark D., Busan, Ronald C., “Benefits of Hybrid-Electric Propulsion to Achieve
4xIncrease in Cruise Efficiency for a VTOL”, AIAA Aviation, 2013
[5] Holden, Jeff and Goel, Nikhil, “Uber Elevate: Fast-Forwarding to a Future of On-Demand Urban Air
Transportation,” Oct. 2016.
[6] Johnson, Wayne, “NDARC NASA Design and Analysis of Rotorcraft, Theory,” Release 1.8, Feb. 2014.
[7] Duffy, Michael J., Samaritano, Tony, “The LIFT! Project – Modular, Electric Vertical Lift System”, American
Helicopter Society Forum 71, 2015
[8] Duffy, Michael J., and Anthony Samaritano. "The LIFT! Project-Modular, Electric Vertical Lift System with
Ground Power Tether." 33rd AIAA Applied Aerodynamics Conference. 2015.
[9] Robinson R44 Cost Data, “R44 Raven II & R44 Clipper II, 2017 Estimated Operating Costs”,
https://robinsonheli.com/wp-content/uploads/2015/06/r44_2_eoc.pdf, accessed March 2017
[10] Robinson R44 User Manual, https://robinsonheli.com/r44-maint-manual/, 2017
[11] Nicolai, Leland M., Carichner, Grant E. Fundamentals of Aircraft and Airship Design. Reston, VA. American
Institute of Aeronautics and Astronautics, 2010. Print.
[12] AvGas Price as of January 2017, Aviation Week,
http://awin.aviationweek.com/ArticlesStory.aspx?keyWord=avgas prices&id=513c2bce-f00e-437e-af9f-
1fd54df22907
[13] Electric Power Monthly, U.S. Energy Information Administration, “Average Price of Electricity to Ultimate
Customers by End-Use Sectors, 2015-2016”
[14] Airbus Vahana, “Vahana Configuratino Trade Study, Part II”, https://vahana.aero/vahana-configuration-trade-
study-part-ii-1edcdac8ad93#.zff7wya9r, accessed March 2017
[15] Minebea website, “Brushless DC Motor Description”, http://www.nmbtc.com/brushless-dc-
motors/engineering/brushless_dc_motors_engineering/, accessed March 2017
[16] Panasonic NCR18650B battery information, “Let’s talk about the Panasonic NCR18650B”,
http://blog.evandmore.com/lets-talk-about-the-panasonic-ncr18650b/, accessed March 2017
[17] Tesla battery pack cost, “Tesla's Battery Pack Costs Are Cheaper Than You Think “,
https://www.fool.com/investing/general/2016/04/26/teslas-battery-pack-costs-are-cheaper-than-you-thi.aspx,
accessed March 2017