Optimal Contro To Save Fuel I Hha09 Rev4

Optimal Control of Heavy-Haul Freight Trains to Save Fuel

Paul K. Houpt, Pierino G. Bonanni, David S. Chan, Ramu S. Chandra, Krishnamoorthy Kalyanam,
Manthram Sivasubramaniam – GE Global Research, Niskayuna NY USA
James D. Brooks, Christopher W. McNally, GE Transportation, Erie PA USA
(correspondence to 1st author at houpt@ge.com)
Summary: Trip Optimizer is a locomotive control system enhancement applicable to diesel-electric hauled freight that
can achieve double-digit fuel savings. Energy savings derive from managing train momentum, with anticipation of its
effects, to reduce the net energy outlay by the train as it completes a trip. GE’s system has two major components: the first is
a planning system that derives an optimal way to drive the train (throttle together with a corresponding speed trajectory
versus distance) subject to speed restrictions along the route and locomotive operating constraints; the second is a dynamic
control system that executes the plan closed-loop, correcting for modeling errors from various sources and assuring proper
train handling consistent with railroad requirements. To compute a plan, information about the track to be traversed (grade
and curvature versus milepost), the power consist makeup (number and type of operational locomotives) and load (tonnage,
train length etc) is required together with updated speed restrictions, work crew locations, and other constraints that may
vary from day-to-day. This paper first gives an overview of the Trip Optimizer system in operation as implemented on GE
Evolution locomotives. Next, key components in the architecture are briefly described, including how the system is operated
with the aid of graphic interfaces. Results of pilot testing of the production system on various revenue service trains on Class
1 railroad’s territories are then summarized to demonstrate actual fuel savings in the 4-13% range while achieving acceptable
train handling.

Index Terms: Fuel optimal train control, freight train automation, energy saving, train cruise-control

1. Background and Design Objectives • Computation of optimal driving profiles
(speed, throttle [notch] as functions of time
AAR 2007 “Railroad Facts” show that fuel burned or distance) to minimize fuel use with no
by North American Class 1 railroads in diesel- impact on schedule
electric freight service exceeded 4.1 Billion • Closed-loop operation to maximize
gallons, accounting for 13% of overall operations consistency in fuel savings and schedule
expense at 2007 fuel prices. While the current objectives and reduce driver workload
hiatus in fuel prices has provided some welcome • Simple setup and operation by crews with
operating cost relief, long-term trends in fuel minimal training
prices are nearly certain to cause the expense and • Flexibility to modify objectives in route to
percentages to increase. To improve efficiency, adapt to changes (route switch, new slow
performance of locomotive components like the orders, alternate arrival time)
diesel prime mover and electrical power • Applicable to all classes of freight service
conversion in the traction system have made an from unit material trains to high HPT
incremental impact over the past 20 years. Even (horsepower per ton) premium services
hybrid technologies to recover braking energy • Fuel benefits obtained from every
have been developed, but this option awaits equipped locomotive, incrementally
improvements in cost and life expectancy of • Use actual locomotive performance
batteries to achieve wide penetration in the characteristics for planning and controls
locomotive fleet. In this context, GE and other
suppliers have set out to develop system level What has emerged is a control system GE calls
control strategies to reduce energy consumption, “Trip Optimizer,” which has progressed from
focusing on how the train can be driven for fuel short breadboard system demonstrations on 15
(and emissions) use reduction while satisfying subdivisions of multiple railroads to commercial
operating constraints of schedule, the rolling stock pilot programs on two Class 1 railroads in daily
and track infrastructure. Among key design revenue service, operating over a wide range of
objectives were: terrains, tonnage and power configurations.
© International Heavy Haul Association
Specialist Technical Session, Shanghai, June 22-25 2009 1

reached. Then to follow the optimal plan, the Trip
2. Trip Optimizer Overview Optimizer control system’s speed regulator can be
engaged. Initiation is via key-presses and master
A simplified block diagram of Trip Optimizer is control handle confirmation by the operator. Once
shown in Figure 1. The crew initiates a trip engaged, the regulator will make closed-loop
request based on train symbol or other code for the corrections to optimal throttle notch plan to follow
train being driven. A GE off-board system using a the speed specified in the trip plan to compensate
satellite link provides track database information for small modeling errors and external
about each trip. The GE off-board is linked to the disturbances such as wind.
railroad either manually or directly and stores all
currently available trips. Once this information is Location navigation as derived from GPS is used
mapped to a road number and received by the lead in conjunction with the plan and speed regulator.
locomotive, a route is generated and passed to the Onboard algorithms use available locomotive
trip planner. speed data to compensate for satellite dropout and
also estimate key train model parameters to
Data Link
New Driver
Di splay validate the trip information received. Severe
departures detected result in automatic
Location, speed Driver
Di splay recalculation of the optimal plan.
GPS Receiver

Sat +
GPS
Antenna
Optimized
Driving Plan
Evolution
Trip optimizer employs an active graphic display
Dispatch Directive
On Board Computer (CMU) Loco Controller
HMI (human machine interface) of terrain and
On-Board Optimal Trip Plan Generation
Track data
Hardware Implementation
situational awareness information to assist the
Loco/train makeup
Speed limits Optimized
Speed & Driver operator with setup, engagement and other
Requested Throttle Plan + Throttle
Trip move Trip
Trip
Planner
Planner
Speed
Speed
Command operational aspects. In-route changes are
Min time -
Timetable
Regulator
Regulator Grade + drag incorporated via track switch prompts that ask the
Loco Data
Updated
Model data
Location &
Location &
Model Estimator
Model Estimator
Locos +
Train
operator about desired track at control points. A
new plan is generated if needed that conforms to
Figure 1 - Trip Optimizer Conceptual Diagram turnout speeds and the new track characteristics.
Because Trip Optimizer does not automatically
The trip planner algorithm utilizes information brake in the speed regulator, the operator is
about the locomotive performance, including notified well in advance of manual braking which
efficiency, train data, such as length, weight, and is flagged in the plan generation process. Trip
road numbers along with trip information such as Optimizer provides the logic to aid the operator’s
origin/destination, slow orders, and preferred transition to braking with warning times of up to
routing. The planner produces an optimal speed two minutes. The system is automatically disabled
trajectory and the corresponding expected notch and an alarm sounded by a supervisory subsystem
levels, expressed a function of distance along the if the operator does not respond. Supervisory
route. Optimization in the plan generation exploits logic is provided to disable the automatic
information about train physics and terrain ahead operation when serious errors occur such as:
to manage momentum in the most fuel-efficient extended loss of GPS; an over-speed is impending
way, subject to time objectives (typically that was not in the plan (due to various errors); the
minimum time) and speed limit constraints. train is off the intended route; prolonged airbrake
Resulting speeds are typically not constant and use; and other detected locomotive failures.
avoid unnecessary braking wherever possible.

After a plan is created, and clearance authorization
obtained, the engineer will depart under manual
control until a critical speed, e.g. 10 mph, is


3. System Components curvature along the track, locomotive tractive
effort, braking characteristics and other factors
Trip optimizer is organized around the major that influence train acceleration such as drag. All
subsystems shown in Figure 1. This section models are validated for consistency with
provides more detail on key Trip Optimizer sub- observed data and are parameterized so that
systems: the Trip Planner, Speed regulator and changes or errors in assumptions from the
Human Machine Interface (HMI). manifest can be detected and corrected.

3.1 Trip Planner Computing the plan is based on solution to a large
optimization problem, set up to achieve desired
The purpose of the planner is to compute a target objectives. Algorithms used for the planner are
driving recipe or “profile” which prescribes how designed to run very fast compared to the time
the train should be driven from a starting location horizon of interest. For example, Figure 2 shows
to a desired end location. The output of the planner the solution obtained from the Trip Optimizer
is a set of speed and notch (throttle/brake) points planner for a 200 mile trip over rolling terrain.
which if followed will achieve desired quantitative This case was for a 4000 ton train operating at a
objectives for the trip, including target arrival time horsepower per ton of approximately 4, typical in
at the destination with minimum fuel use and premium services. Note the large percentage of the
satisfy all equipment and track operating route that is completed without braking, a
constraints. Input data to the planner includes byproduct of the fuel saving objective in the
information on the power consist, the load being optimization. Plans by design calculate where
hauled (weight, train length, number of cars, braking is required and this information is used
weight distribution), the track route starting and within the speed regulator and HMI to alert the
end points, and track description (grade, curvature operator to switch to manual operation with Trip
and standing speed limits as functions of footage Optimizer’s motoring only operation. For future
along the route). Other input data includes generations of the product, the braking calculation
temporary slow orders or other operating will be used to allow automatic operation to be
restrictions relevant to the current run. Trips with retained even through braking events.
multiple stops to do work (e.g. pickups and
setouts) can also be accommodated in a single plan 80
Speed (mph)

60
or can be handled as separate plans running from 40
stop-to-stop. 20
0
0 20 40 60 80 100 120 140 160 180 200
Data for the planner is obtained from both on- 10
Throttle setting

board sources (e.g. track and known locomotive 5
characteristics) and off-board sources via satellite 0
radio links to the customer’s manifest and work -5
orders. Some manual entry updates are also 0 20 40 60 80 100 120 140 160 180 200
1
available to the crew at all times through the HMI.
Grade (%)

Various communication interfaces can be 0
accommodated depending on customer
infrastructure and preferences. -1
0 20 40 60 80 100 120 140 160 180 200
Distance (miles)

Both the planner and the speed regulator, which Figure 2 - 200 Mile Optimized Trip on Rolling Terrain
runs the locomotive to follow the plan, are based
on simplified equations of motion for the train that Solution to this planning problem required
are derived from basic laws of physics and energy approximately:
balances. Models account for effects of grade and • 900 spatial steps


• 1816 decision variables (notch) improvement compared to manual operation (not
• 5440 constraints shown here). The curve represents a “snapshot” of
• 2.25 seconds to converge to required entitlement for this train on this day taking account
tolerance on a typical office computer of all the prevailing train operating conditions and
constraints.
Speed of the planner is vital because planning with
Trip Optimizer is not static. Re-plans can be Trip Optimizer’s planner has enormous flexibility
initiated en-route for numerous reasons, including to achieve complex requirements and operating
addition or removal of temporary slow orders, rules of a railroad customer and/or operator
diversion from a main track route to a secondary preferences permitted by the railroad. A simple
route, stops added to do work, or change in example shows some of the flexibility possible.
planned meets and passes that require a siding Consider the small problem in Figure 4, with
diversion. If a stop is required due to traffic ahead, speed restrictions shown. Figure 5 shows the
and no other changes have occurred, the currently optimal plan solution.
executing plan can be resumed. Otherwise the stop
70
provides an opportunity for the crew, in
60
coordination with dispatch, to update changes in
50
objectives and a new plan is computed
40
295 Start 303.5 304 304.5 305 307.5 310
One of the very useful byproducts of the fast End

planning computation is the ability to generate fuel A B

use / travel time trade-off curves such as Figure 3,
Figure 4 - 15 Mile Simplified Planner Problem
which is calculated for the 200 mile example
above. Results are shown as a function of distance, but
the corresponding time to complete the trip is
1.6
x 10
4
15:25 (minutes: seconds) and a total of 788 lbs of
1.5
fuel are required. Astute operators may argue that
a faster time might be achieved by delaying the
1.4
14% fuel benefit speed reduction (relaxing some constraints, it is
1.3
easy to find a plan that is 20 seconds faster at a
Fuel Consumed (lb)

1.2
cost of some extreme braking that would result in
16 min
1.1
incremental poor train handling). The optimal plan is seen to
1 travel time avoid braking to save energy, but has a sustained
0.9 idle duration between mileposts 300 and 305.
0.8

0.7

0.6
3 3.5 4 4.5 5 5.5 6 6.5 7
Travel Time (hrs)

Figure 3 – Fuel Travel Time Tradeoff
Each point on the curve has a corresponding plan
like Figure 2. While most operators and railroad
management will choose minimum time as the
objective, there is a high sensitivity of fuel use to
travel time. In this example, a 16 minute delay in a
3.5 hr trip yields a 14% incremental fuel saving
from the min-time solution, on top of the


80
Speed (mph)

60 3.2 Speed Regulator Subsystem Functions
40
295 300 305 310 There are three inter-related functionalities used in
10 Trip Optimizer to execute the plan as shown in
Effective Notch

5 Figure 1.
0

-5
295 300 305 310
Speed regulator-manipulates the throttle closed-
1 loop to follow the plan when automatic mode is
engaged. It functions like “cruise-control” on a
Grade (%)

0
highway vehicle, but follows the prescribed
-1 varying speed plan from the optimizer. Errors in
295 300
Distance (mi)
305 310
speed that result from modeling errors for train
track and environment (e.g. wind, manifest errors),
Figure 5 – Optimal Plan with ‘long’ Idle Stretch result in corrections to the optimally planned
notch. This assures schedule compliance that is
Since prolonged idle may result in undesirable
baked into the optimal plan.
slack-action, particularly over some terrains, the
planner optimizer can be constrained to avoid idle
The current implementation of Trip Optimizer
in finding a solution as shown in Figure 6. In this
allows the speed regulator to be active only when
example, adding this constraint requires the plan to
motoring: braking is not applied automatically.
add a small amount of braking to stay below the
Over a typical trip, 50-70% of the trip miles can be
45 mph speed restriction before milepost 305, but
driven automatically in this fashion depending on
the additional fuel cost is only 2 lb (above the 788
the subdivision terrain and train makeup. In
lb) or 0.25%. Adding constraints to achieve
computing the plan, regions where braking will be
desired objectives via the planner can be made
required are identified, and displayed to the driver
active only at specified locations or over the entire
through the HMI. The speed regulator prompts for
route.
and makes a bump-less handoff to the driver
80 where braking is required. When conditions allow
automated operation again, the HMI prompts the
Speed (mph)

60
operator to re-engage automatic operation. While
40 controlling to the planned speed, the system
295 300 305 310
10
accounts for typical operating rules such as
Effective Notch

5
maximum notch/DB levels, power braking
0
restrictions, and maximum “allowed notch above
-5
speed” rules.
295 300 305 310
1
Train Handling—Assurance of acceptable train
Grade (%)

0 handling is critical to any freight train control
system that is expected to operate hands-off.
-1
295 300
Distance (mi)
305 310 Minimum fuel driving strategies turn out to also
promote good train handling. As the example in
Figure 6 – Optimal Plan tuned to Avoid Idle Figure 6 showed, it is possible to create plans that
are likely to have better likelihood of producing
acceptable train handling. A hierarchy of rules
determines how the planned throttle is modified to
achieve acceptable train handling. Rules depend
jointly on what is coming from the planner, the

estimated train state, local track terrain, wrong 20% of the time. Provision has therefore
locomotive health and other data to assure proper been made to provide on-line algorithms that
handling for all terrains and consists. Validation of observe train behavior compared to model
train-handling performance is done through a predictions built on available data. When
combination of off-line simulation in tools like significant errors are detected, estimates of the
TOES and operator reports in field trials and pilots impact on fuel entitlement are used to decide if a
(see below). re-plan should be created on the fly or delayed to a
future stopping point. Decision criteria to replan
A key benefit of closed-loop operation with the are flexible and vary by railroad preferences.
speed regulator is narrowing the distribution of
travel-times and accuracy in following speed 3.3 Human Machine Interface (HMI)
reductions. Figure 7 compares manual against
automatic operation and the distribution of under- Setup & Results Summary--Standard Smart
speeds (negative values) and over-speeds in Display screens on Evolution locomotives are used
transitioning from line speed to various slower to provide a human machine interface to Trip
speeds with the regulator active. The data is Optimizer. Together with associated function keys
compiled from three runs over an entire that are located below the on-board display, the
subdivision on a North American railroad as part HMI provides the means by which the operator
of pilot studies conducted in 2008 all with similar sets-up, initializes, engages and disengages
train makeup and HPT. Similar reductions to automatic operation and shuts down the system.
speed variation have been seen throughout field Figure 8 shows a typical Trip Optimizer setup
testing of Trip Optimizer. screen. Operators can request data to be
downloaded by train symbol or other shorthand
and proceed to make last minute edits to the power
consist, e.g. change locos in consist different from
90% Auto the manifest, flag isolated units, set DB cutout etc.
10% Control Future features are being considered to allow other
editing capability for data supplied in the manifest
Number of reductions

and track data. Setup confirmation and review
screens (not shown) are also provided before a
trip, and summary statistics screens are provided
to the operator at the end of the trip.
Manual
8% 67% Control ER ATC Distance GE
40
90 30 50 0 2010
40 60 80 100 120 20 60
BP Consist Klb Reverser
25% 90 10
200
15
5
70
2:3 0 Cntr
0 80
Rear Flow Main BC Effort Klb Throttle

-5 - 1.5 0 +1.5 +5
88 2 140 72 0.00 0 0 180 Idle

Over-speed (mph) PRK
BRK
PARK
<reserved for aar > SAND HORN BELL ON
BRK ON

Figure 7 – Performance of Speed Regulator vs. Manual Trip Optimizer – Locomotive Setup
Locomotive Position Power Mode New Power Mode
Estimation--Performance benefits from Trip GE 2010 1 Running Running

Optimizer are dependent on knowing the various GE
GE
2005
2015
2
3
Isolated
Running
Isolated
DB Cutout

train and track parameters used in the planner GE 2901 4 DB Cutout

optimizer and speed regulator. Track data-bases Use Arrow Keys To Select Correct Mode For Each Locomotive , L1

are vetted through an off-line process, though Then Press F7 To Continue.
Change
Yes
2525-0

Save Changes Cancel
developing tools to assist in track data-base Page Page
Length
Change Change Previous Page Page End Smart
Down Up Loaded Empty Cars Down Up Throttle

construction was a significant development effort.
Train data extracted from the manifest may be Figure 8 – Sample Trip Optimizer Setup Screen


ER Distance GE
40
90 30 50 0 2010
40 60 80 100 120 20 60

Running Screen--Figure 9 Trip Optimizer BP
90 10
200
15
5
70
Consist Klb

2:3 60 K
Reverser

Fwd
Running Screen shows the running screen for Rear
88
Flow
2
Main
140
BC
0
0

47
80

MPH 30
Effort Klb
0 180
Throttle
N8
Trip Optimizer that appears after departure tests WHEEL
SLIP
PCS
OPEN
BRAKE AUTOSTOP
WARN MM:SS
ALERTER
20
UNIT
ALARM
CS TTP
19
BATT
DEAD
EOT
MOVE

are completed and the proper setup of the train AUTO CONTROL
ACTIVE SAND HORN SAND
BELL HORN PARK
PARK
BELL
BRK ON
BRK ON
AUTO
N4

allow the operator to proceed. To minimize “heads
Speed
60
50 50

25 Cab Signal
down time” for the operator viewing the screen, Terrain UP: Cut Out

only essential data to manage Trip Optimizer is CNW: Cut Out

provided. Situational data of the standard AAR Current MP:
101

101.2
102

Track:
103 104

MAIN1
105
Ind Brk Auto Brk
Lead Cut In
type is provided in the upper 20% of the screen. Arrival In: 01:45 Arrival Time:
Destination : WILLOW SPRINGS
13:45 EDT
L1

In the center is a new rolling strip map with Ready
Air End of Update Confirm Confirm Auto Manual
2550-0

Exit
Brakes Train Track Throttle Auto Control Control

distance traversed established from GPS data, Distance
Start
Distance
Setup
Auto
Start/Stop
Consist
Manager
Trip
Optimizer
Screen
Controls
End
Trip

graph of terrain (grade), train on terrain, civil
speed limit and, in a different color, temporary Figure 9 Trip Optimizer Running Screen
slow orders. Under the rolling map is current MP
location, track being followed and destination for 4 – Pilot and Performance Test Results
this trip. About 6 miles are displayed on this
example, which is railroad configurable. Trip Optimizer has progressed from a prototype
Automatic status is displayed on the box over the system in 2006 that ran four short-term, supervised
rolling display and the current actual notch being pilots on 15 subdivisions to a complete production
generated by the speed regulator in the box to the system now running around the clock in revenue
right. The light area on the terrain to the right of service at two Class 1 railroads without GE
the train is a region where manual (braking) supervision.
operation will be required as inferred from the
optimal plan. A sequence of warnings to the 4.1 Evolution Locomotive Implementation
operator to take over are provided, as the system
reverts to manual. When automatic mode can Trip Optimizer has been implemented as a
again be resumed, appropriate prompts will be production version in the hardware shown in
made to the driver. Figure 1. Standard locomotive displays used in the
When automatic operation is permitted, the SDIS architecture on EVOs are used for the HMI.
operator presses the appropriate key and moves For later application to non-GE power, other
the throttle to Run 8. The speed regulator will then architectures are being considered. Only the lead
pickup like the cruise control on a car and power needs to be equipped to gain all the benefits
modulate the throttle to follow the plan. At any of Trip Optimizer.
time, the operator can disengage automatic
operation by moving throttle out of Run 8 position 4.2 Pilot Test Methodology
or pressing the a key, making disengagement
straightforward and intuitive. Overview--A pilot is a key first step in
understanding the benefit of Trip Optimizer over a
particular subdivision and in preparing the system
to run there. Prior to beginning a pilot, work is
done to prepare track databases, identify the
expected train types and configurations, and
coordinate delivery of trip data with the railroad.
Runs without Trip Optimizer active are made to
collect data used to validate all aspects of the track
database. Train handling analysis is carried out in


off-line simulations for the expected trains to subjective feedback from train crews in the pilot
ensure acceptable operation. This includes indicate Trip Optimizer is performing better than
simulation comparison of train forces with the baseline from a run-in perspective.
manual operations based on historical event
recorder data for similar trains on the same route. 4.3 Current Pilot Status

Each pilot begins with manned runs wherein GE Several long-term pilots are currently underway at
personnel ride with each Trip Optimizer equipped two Class 1 US railroads. Trains are running over
train to ensure operation is as intended and collect six subdivisions containing more than 700 miles of
valuable data for system validation. Supported track with tonnages up to 10,500 tons and varied
runs are used to provide crew training and get distributed power configurations. Trip Optimizer
detailed feedback from each crew covering ease of is running without GE supervision on five of these
use, transition from auto to manual, train handling, six, with the last soon to follow. Over 50,000 trip
screen layouts and information displayed. All miles have been run as of early 2009 with an
feedback is integrated in a database to assess gaps average of 60% of these miles in automatic
and identify enhancements for future product control. It is important to note that on only about
development. 74% of the total miles was automatic available due
to various operational factors, so that on average
Fuel Use Assessment - Actual test runs are crews have been able to keep Trip Optimizer
selected in collaboration with the railroad to cover engaged about 81% of the time where it could be
a tonnage and HPT range that is representative of used. These totals are being added to daily at an
their operations and for which benchmark manual average rate of 240 miles in automatic control, or
operator runs are available. The same 410 trip miles per day.
measurement methodology as the customer is used
to compute fuel expended with and without Trip Fuel Saving results—The common normalization
Optimizer. For all results discussed here, fuel use metric used for fuel expenditure has been in gross
was predicted from records of time at notch and ton-miles/gallon where more is better or its
fuel-flow at notch summed up for all the power in reciprocal where less is better. Results using
the consist on a particular run. Procedures are gallons per gross ton-miles for the most recent
vetted for consistency with railroad practices. pilot runs completed in 2008 and early 2009 are
summarized in Table 1 and Table 2 (actual
Train Handling Assessment-No train force railroads and subdivisions are not identified for
couplers were available for actual in-train force proprietary consideration to the lines at their
measurements in any of our Pilot studies, and request). Trains dispatched in both populations
applying to a large number of trains would be ranged in HPT from just under 1.0 to 4.0. Terrains
logistically and cost prohibitive. Instead we relied ranged from flat to mountainous, so that this
on two methods of validation: (1) post-run sample includes both the middle and extremes of
analysis of event recorder data from Trip the population. Operators were representative of
Optimizer trains with a third party simulation tool the population, both experienced and
(similar to and validated against TOES train inexperienced. Savings of fuel ranged from 4.6 to
simulator developed by the AAR); (2) anecdotal 13% for these pilots; the wide variation in savings
subjective reports from crews and their supervisors reflects the broad differences among territory,
on the frequency and magnitude of run-ins or other train type and railroad operation that were selected
anomalies observed of excessive buff and draft to benchmark Trip Optimizer capability.
forces in operation. Both methods consistently
show, at a minimum, there is no negative impact
on train handling with Trip Optimizer deployment
compared to crews in the baseline. More

there are no unexpected train handling surprises.
Table 1 These simulation results, coupled with extensive
Fuel Savings for Railroad A over 3 Subdivisions crew feedback from post-run interviews and more
Fuel Use Number of Trip Opt Test than two years of field tests of Trip Optimizer,
Railroad A Reduction Runs With Valid give confidence to assert that train handling with
Subdivision From Baseline Comparison Family
Trip Optimizer is equivalent to good manual
Alpha -7.8% 38
operation.
Beta -13.0% 48
Gamma -4.6% 47
Ave / Total -8.6% 133
5 Summary and Conclusions

Table 2 Trip Optimizer has been shown to be a viable on-
Fuel Savings for Railroad B over 4 Subdivisions board control system for GE Evolution series
Fuel Use Number of TO Test locomotives to save fuel. By focusing on a closed-
Railroad B Reduction from Runs With Valid loop approach using GPS and an optimized
Subdivision Baseline Comparison Family driving plan, savings can be obtained with no
CHI -5.9% 26
compromise to operating schedule. Repeatability
NU -8.3% 19
ETA -6.5% 21 in operations reduces operator variability in
LAMBDA -8.2% 21 achieving up to 13% or more fuel saving based on
AVE/Total -7.1% 87 more than 50,000 miles of pilot testing in revenue
service. Pilot test feedback from crews and their
supervisors suggest that it is easy to set up and use
Train Handling Analysis—Using the pilot runs with the provided HMI and graphics display
for guidance, data was grouped for a total of four design. The system requires minimal training to
similar trains ranging in tonnage from about 4800 rapidly adopt in revenue service. Train handling
to 6800 tons with lengths from 6800 to 7400 feet. has been shown, both in detailed simulation
Looking through event recorder records where analysis and field reports of handling anomalies to
Trip Optimizer was in automatic, approximately be equivalent to good manual operation. While the
78 total miles were selected and partitioned into a existing product is a motoring-only design, crews
total of 32 “segments” where the train speeds were found the cues for transition in and out of regions
similar between Trip Optimizer and manual requiring manual braking intuitive and
control. These segments ranged in length from straightforward to use. Moreover, fully automatic
under a mile to more than 10 miles. Segments operation could be sustained in 80% of the route
were picked to span the variation in terrain over distance where braking wasn’t required where
the subdivisions selected. For each of the selected other factors (e.g. traffic) did not impede
segments, a TOES dynamic simulation was operation. Extensive flexibility has been
constructed according to available manifest data, engineered into the product to not only generate
first with the manual field data of notch (and fuel-efficient plans at the start of a journey but to
speed) and then with corresponding trip optimizer flexibly re-plan as objectives and constraints
throttle time history and speed that were recorded. change during the real world execution of a trip.
Resulting buff and draft force extremes were
captured and are analyzed. Over the 32 segments,
the in-train forces were shown to be statistically
the same. Trip Optimizer averages 10 kips higher
in draft and 2 kips higher in buff with the exact
same number of run-in events as manual operation
over the same segments. Analysis using this
methodology continues to build confidence that


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