Optimal Contro To Save Fuel I Hha09 Rev4

  • 1,757 views
Uploaded on

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, …

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.

More in: Business , Technology
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
    Be the first to like this
No Downloads

Views

Total Views
1,757
On Slideshare
0
From Embeds
0
Number of Embeds
1

Actions

Shares
Downloads
61
Comments
0
Likes
0

Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide

Transcript

  • 1. 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
  • 2. 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 © International Heavy Haul Association Specialist Technical Session, Shanghai, June 22-25 2009 2
  • 3. 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 © International Heavy Haul Association Specialist Technical Session, Shanghai, June 22-25 2009 3
  • 4. • 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 © International Heavy Haul Association Specialist Technical Session, Shanghai, June 22-25 2009 4
  • 5. 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 © International Heavy Haul Association Specialist Technical Session, Shanghai, June 22-25 2009 5
  • 6. 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 © International Heavy Haul Association Specialist Technical Session, Shanghai, June 22-25 2009 6
  • 7. 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 © International Heavy Haul Association Specialist Technical Session, Shanghai, June 22-25 2009 7
  • 8. 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 © International Heavy Haul Association Specialist Technical Session, Shanghai, June 22-25 2009 8
  • 9. 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 © International Heavy Haul Association Specialist Technical Session, Shanghai, June 22-25 2009 9