NREL is a national laboratory of the U.S. Department of Energy Office of Energy Efficiency & Renewable Energy Operated by the Alliance for Sustainable Energy, LLC

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.go v/publications.

Contract No. DE

- AC36

- 08GO28308 

# The Development of Vocational Vehicle Drive Cycles and Segmentation

Adam Duran, Caleb Phillips, Eric Wood, Arnaud Konan, Ken Kelly, and Jeff Gonder

Technical Report NREL/TP

- 5400

- 65921 February 2016NREL is a national laboratory of the U.S. Department of Energy Office of Energy Efficiency & Renewable Energy Operated by the Alliance for Sustainable Energy, LLC

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE

- AC36

- 08GO28308 National Renewable Energy Laboratory 15013 Denver West Parkway Golden, CO 80401
303

- 275

- 3000 • www.nrel.gov 

# The Development of Vocational

Vehicle Drive Cycles and Segmentation

Adam Duran, Caleb Phillips, Eric Wood, Arnaud Konan, Ken Kelly, and Jeff Gonder

Prepared under Task No. WFGY.1000

Technical Report NREL/TP

- 5400

- 65921 February 2016

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. 

### NOTICE

This report was prepared as an account of work sponsored by an agency of the United States government. Neither the United States government nor any agency thereof, nor any of their employees, makes any wa rranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States government or any agenc y thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States government or any agency thereof.

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www. nrel.gov/publications

. Available electronically at SciT ech Connect http:/www.osti.gov/scitech

Available for a processing fee to U.S. Department of Energy and its contractors, in paper, from:

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Cover Photos by Dennis Schroeder: (left to right) NREL 26173, NREL 18302, NREL 19758

, NREL 29642, NREL 19795.

NREL prints on paper that contains recycled content

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# Notice

This DRAFT report is being provided to the USEPA for the purposes of obtaining public feedback on the work performed to date under an interagency agreement to develop medium and heavy

- duty vehicle drive cycles and dri ve cycle segmentation. This DRAFT report contains substantial information about the analysis methodologies, data, and results to date that are suitable for comments and suggestion. A final technical report will be published after comments have been recei ved and necessary edits have been made.

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# List of Acronyms

AS Aerodynamic Speed CA Characteristic Acceleration CARB California Air Resources Board CH 4 

### methane CO 2 

### carbon dioxide DOE U.S. Department of Energy DRIVE Drive Cycle Rapid Investigation, Visualization, and Evaluation tool EISA Energy Independence and Security Act EPA U.S. Environmental Protection Agency ETC European Test Cycle, also known as the FIGE Cycle Ft/s feet per second Ft/s 2 

### feet per second squared GHG greenhouse gas GPS global positioning system HD heavy

- duty HHDDT Heavy Heavy

- Duty Diesel Truck Hz hertz kg kilogram MD medium

- duty MOVES Mo tor Vehicle Emission Simulator mph miles per hour N 2 

### O nitrous oxide NHTSA National Highway Traffic Safety Ad ministration NREL National Renewable Energy Laboratory PCA Principal Components Analysis RMS root mean squared
rpm revolutions per minute WHTC World Harmoniz ed Transient Cycle WHVC International World Harmonized Vehicle Cycle

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# Table of Contents

List of Acronyms

................................ ................................ ................................ ................................ ......... v List of Figures

................................ ................................ ................................ ................................ ........... vii List of Tables

................................ ................................ ................................ ................................ .............. ix Executive Summary

................................ ................................ ................................ ................................ .... x 1 Introduction and Background

................................ ................................ ................................ ............. 1 1.1 Supporting EPA GHG Phase 2 Regulation Development

................................ ............................. 1 1.2 NREL’s Fleet DNA Database

................................ ................................ ................................ ....... 1 1.3 NREL’s DRIVE Tool

................................ ................................ ................................ .................... 2 1.4 Segmenting the U.S. Vocational Vehicle Population and Developing Representative Drive Cycles and Weights

................................ ................................ ................................ ....................... 3 2 Cluster Analysis

................................ ................................ ................................ ................................ .... 4 2.1 Principal Components Analysis

................................ ................................ ................................ .... 5 2.2 Drive Cycle Clustering

................................ ................................ ................................ .................. 8 2.3 Data Resampling

................................ ................................ ................................ ......................... 12 2.4 Vehicle Clustering

................................ ................................ ................................ ....................... 17 3 Predictive Modeling

................................ ................................ ................................ ............................ 21 3.1 Logistic Regression Model

................................ ................................ ................................ .......... 24 3.1.1 Full Model

................................ ................................ ................................ ...................... 24 3.1.2 Univariate Diesel Model

................................ ................................ ................................ 26 3.2 Validation and Error Process

................................ ................................ ................................ ....... 26 4 Development of Representative Transient Drive Cycle Component

................................ ............. 29 4.1 Generation of Representative Low Speed Transient Component Using DRIVE TM

.................... 29 4.2 Generation of Representative High Speed Transient Component Using DRIVE TM

................... 31 4.3 Comparison of DRIVE Generated Transient Cycles to Existing Standard Cycles

..................... 32 4.3.1 Standard Test Cycles Selected for Comparison

................................ ............................. 33 4.3.2 Drive Cycle Characterization and Comparison

................................ .............................. 36 4.3.3 Analysis of Mileage Accumulation by Veh icle Speed

................................ ................... 37 4.3.4 Comparison of Acceleration/Deceleration Behavior

................................ ..................... 40 5 Development of Representative Drive Cycle Weights

................................ ................................ .... 45 5.1 Comparative Drive Cycle Characterization

................................ ................................ ................ 45 5.2 Analysis of Mileage Accumulation

................................ ................................ ............................. 48 5.3 Weighting of Clusters by Mileage Accumulated in Broad Speed Categories

............................. 50 5.4 Characterization of Acceleration/Deceleration Behavior

................................ ............................ 51 6 Known Limitations and Potential for Future Work

................................ ................................ .......... 55 References

................................ ................................ ................................ ................................ ................. 56 Appendix A: Standard Fleet DNA Data Channels

................................ ................................ .................. 60 Appendix B: Determining Engine to Vehicle Speed Ratios via Kernel Density Estimation

.............. 62 Appendix C: Com parison of Fleet DNA and MOVES Resampled Distributions by Cluster

.............. 66

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# List of Figures

Figure 1: Sample composition

................................ ................................ ..... Error! Bookmark not defined. Figure 2: A correlogram for the eight metrics gives a visual indication of the degree of linear correlation

(dependence) between each combination of variables.

................................ ............................ 5 Figure 3: All drive cycles for all vehicles visualized in the space defined by the first two principal

components.

................................ ................................ ................................ ............................. 7 Figure 4: Scatter plot of all vocational drive cycles.

................................ ................................ ..................... 8 Figure 5: Two clustering using k

- medoids algorithm with cluster centers marked in black. Only

vocational vehicles are drawn in this plot.

................................ ................................ ............... 9 Figure 6: Three clustering using k

- medoids algorithm with cluster centers marked in black. Only

vocational vehicles are shown in this plot.

................................ ................................ ............. 10 Figure 7: Resampling flow for vocational vehicles

................................ ..... Error! Bookmark not defined. Figure 8: Difference between resampled cluster center statistics and original Fleet DNA (entire

population) statistics for two clusters..

................................ ................................ ................... 14 Figure 9: Difference between resampl ed cluster center statistics and original Fleet DNA (entire

population) statistics for two clusters.

................................ ................................ .................... 15 Figure 10: Convergence on final cluster center metrics as a function of successive resamplings

.............. 16 Figure 11: Convergence on final cluster center metrics as a function of successive MOVES subcategory

resamplings

................................ ................................ ................................ ............................ 17 Figure 12: Trace

- mode clustering of Fleet DNA Vocational vehicles using two

- cluster solution..

........... 18 Figure 13: Trace

- mode clustering of Fleet DNA Vocational vehicles using three

- cluster solution..

......... 19 Figure 14: Trace

- mode clustering of Fleet DNA Vocational vehicles using two

- cluster solution. This plot

provides a simplified (condensed) version of the entire drive cycle data.

............................. 20 Figure 15: Scatter plot showing correlation between RPM at 55 mph and RPM at 65 mph for different

vehicle fuel types.

................................ ................................ ................................ .................. 21 Figure 16: Engine RPM data available for different vocational categories..

................................ .............. 22 Figure 17: Engine RPM has a clear bimodality which corresponds with two

- cluster assignments.

........... 23 Figure 18: Distribution of Engine RPM characteristics for different types of vehicles.

............................. 23 Figure 19: Logistic histogram showing the relationship between RPM at 65 mph and the probability of

being in cluster 2 (Fast).

................................ ................................ ................................ ......... 25 Figure 20: Locations (red points) of 40 most often misclassified vehicles (in 100

- fold cross validations)

using the full logistic regression model.

................................ ................................ ................ 27 Figure 21: Screen capture of DRIVE GUI processing data

................................ ................................ ........ 29 Figure 22: Speed

- time trace for representative low speed EPA transient cycle

................................ ......... 30 Figure 23: Speed

- t ime trace for representative high speed EPA transient cycle

................................ ........ 32 Figure 24 : Speed

- time trace for CARB HHDDT transient cycle

................................ ............................... 33 Figure 25 : Speed

- time trace for WHVC

................................ ................................ ................................ ..... 34 Figure 26 : Speed

- time trace for ETC FIGE Cycle

................................ ................................ ...................... 35 Figure 27: Speed

- time trace for JP JE05 cycle

................................ ................................ ........................... 36 Figure 28: Comparative distribution of mileage accumulation binned by vehicle speed for low speed

transient

................................ ................................ ................................ ................................ .. 39 Figure 29: Comparative mileage accumulation for standard cycles of interest

................................ .......... 40 Figure 30: Comparison of standard cycle acceleration frequency

................................ .............................. 41 Figure 31: Comparison of standard cycle deceleration frequency

................................ .............................. 42 Figure 32: Comparison of standard cycle peak acceleration rates

................................ .............................. 42 Figure 33: Comparison of sta ndard cycle peak deceleration rates

................................ .............................. 43 Figure 34: Comparison of standard cycle average acceleration rates

................................ ......................... 43 Figure 35: Comparison of standard cycle average deceleration rates

................................ ......................... 44 Figure 36: Comparison of cluster and representative cycle zero speed time vs. mileage accumulated below

55 mph

................................ ................................ ................................ ................................ ... 46

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. Figure 37: Comparison of cluster and representative cycle average driving speed and kinetic intensity

... 47 Figure 38: Comparison of cluster and representative cycle standard deviation of speed and stops per

mile

................................ ................................ ................................ ................................ ........ 48 Figure 39: Comparison of cluster and representative cycle mileage accumulation

................................ .... 50 Figure 40: Compa rison of cluster and representative cycle acceleration frequency

................................ ... 52 Figure 41: Comparison of cluster and representative cycle dece leration frequency

................................ ... 52 Figure 42: Comparison of cluster and representative cycle peak acceleration rates

................................ ... 53 Figure 43: Comparison of cluster and representative cycle peak deceleration rates

................................ ... 53 Figure 44: Comparison of cluster and representative cycle average acceleration rates

.............................. 54 Figure 45: Comparison of cluster and representative cycle average deceleration rates

.............................. 54 Figure 46: Sample potential dual mode test cycle

................................ ................................ ...................... 55 Figure 47: Sample heat plot of vehicle speed vs. engine operatin g speed

................................ .................. 62 Figure 48: Single vehicle sample distribution of vehicle speed to engine speed ratio data

........................ 63 Figure 49: Single vehicle sample distribution of vehicle speed to engine speed ratio data with peaks

identified

................................ ................................ ................................ ................................ 64 Figure 50: Identified gear ratios plotted atop sample vehicle speed vs. engine operating speed data

........ 65 Figure 51: Comparison of multidimensional RMS Distance distributions for full fleet DNA Dataset and

MOVES resampled population for Cluster 1

................................ ................................ ......... 66 Figure 52: Comparison of multidimensional RMS distance distributions for full Fleet DNA dataset and

MOVES resampled population for Cluster 2

................................ ................................ ......... 67 Figure 53: Comparison of multidimensional RMS distance distributions for full Fleet DNA dataset and

MOVES resampled population for Cluster 3

................................ ................................ ......... 68

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. 

# List of Tables

Table 1. Principal Component Loadings (All Vehicles)

................................ ................................ ............... 6 Table 2. Principal Component Loadings (Vocational Vehicles)

................................ ................................ .. 6 Table 3. Vocational Drive Cycles

– 2

- Clustering Centers

................................ ................................ .......... 11 Table 4. Vocational Drive Cycles

– 3

- Clustering Centers

................................ ................................ .......... 11 Table 5. Vocational Resampling by MOVES Ca tegories

................................ ................................ ........... 12 Table 6. Vocational Resampling by MOVES Sub

- categories

................................ ................................ .... 13 Table 7. Fitted Coefficients for Full Logistic Regression (Fleet DNA Vocational Vehicles Only)

........... 25 Table 8. Fitted Coefficients for Univariate Diesel

- only Logistic Regression

................................ ............. 26 Table 9. Comparison of Standard Cycle Drive Cycle Metrics

................................ ................................ .... 36 Table 10. Sub 45 mph Mileage Accumulation Comparison for Standard Cycles

................................ ...... 38 Table 11. 45+ mph Mileage Accumulation Comparison for Standard Cycles

................................ ........... 38 Table 12. Comparison of Cycle Acceleration Statistics

................................ ................................ .............. 40 Table 13. Comparison of Representative Cycle and Cluster Drive Cycle Metrics

................................ ..... 45 Table 14. Sub 45 mph Mileage Accumulation Comparison for Cluster and Representative Cycles

.......... 49 Table 15. 45+ mph Mileage Accumulation Comparison for Clusters and Representative Cycles

............. 49 Table 16. Final Weights for Identified Clusters

................................ ................................ .......................... 50 Table 17: Comparison of Acceleration Behavior for Clusters and Representative Cycle

.......................... 51 Table 18. Example GPS File

................................ ................................ ................................ ....................... 60 Table 19. List of Additional Logged Controller Area Network Channels

................................ .................. 61

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# Executive Summary

In collaboration with the U.S. Environmental Protection Agency along with the U.S Department of Energy and a collection of valued industry stakeholders, the National Renewable Energy Laboratory (NREL) has performed a series of in

- depth analyses to characterize the on

- road driving behavior including distributio ns of vehicle speed, idle time, accelerations and decelerations, and other driving metrics of medium and heavy

- duty vocational vehicles operating within the United States. As part of this effort, NREL researchers segmented U.S. medium

- and heavy

- duty vocat ional vehicle driving characteristics into three distinct operating groups or clusters using real world drive cycle data collected at 1

- Hz and stored in NREL’s Fleet DNA database. Leveraging coupled vehicle chassis specification information also stored in Fleet DNA, researchers were then able to develop a logistic model capable of predicting individual vehicle cluster membership. In addition to developing the predictive model, the results of the vocational segmentation analyses were also used by researchers to develop a series of drive cycle weights along with a testable transient drive cycle representative of real world operating behavior of commercial U.S. vehicles.

This report includes an introduction to the Fleet DNA database and the data contained withi n, a presentation of the results of the statistical analysis performed by NREL, review of the logistic model developed to predict cluster membership, and a discussion and detailed summary of the development of the vocational drive cycle weights and represe ntative transient drive cycles for testing and simulation.

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# 1 Introduction and Background

1.1 Supporting EPA GHG Phase 2 Regulation Development

In August of 2011, the U.S. Environmental Protection Agency (EPA) and the National Highway Traffic Safety Administration (NHTSA) adopted a national set of sta ndards to reduce greenhouse gas (GHG) emissions and improve fuel efficiency of medium

- duty (MD) and heavy

- duty (HD) trucks. The jointly developed under the Energy Independence and Security Act (EISA), and the Clean Air Act, the GHG standards included the e stablishment on limits for CO 2 

### , N 2 

### O, and CH 4 

### emissions. These limits would be enforced on model year 2014

– 2018 vehicles, and have come to be known as Phase 1 of the national GHG regulatory program. The development of the Phase 1 standards provided EPA, NHT SA, and the state of California with a set of fully aligned regulations, allowing manufactures to build a single fleet of vehicles and engines for the U.S. market.

Soon after the implementation of the Phase 1 regulations, in response to the President’s Cli mate Action Plan in February of 2014 President Obama announced efforts to update existing MD/HD vehicle regulations in Phase 2 of EPA’s national GHG program. This new update would cover model year vehicles from 2021 to 2027, and when fully implemented coul d provide up to a 16% reduction in CO 2 

### emissions for vocational vehicles compared to Phase 1.

As part of the EPA’s proposed Phase 2 rulemaking, the U.S. Department of Energy (DOE) and EPA are partnering to support a targeted project to refine and evaluate appropriate duty cycles for tractor

- trailers and vocational vehicles to be used as part of MD/HD vehicle certification procedures for GHG emission standards. The National Renewable Energy Laboratory (NREL) will provide technical leadership utilizing DOE V ehicle Technologies Office

- supported data, tools, and expertise to support these efforts.

NREL’s experience with large transportation database projects, including Fleet DNA and the Transportation Secure Data Center, provide the prerequisite capabilities f or tackling data

- intense problems. Additionally, NREL’s data analysis tools including the Drive Cycle Rapid Investigation, Visualization, and Evaluation tool (DRIVE) are used to distill large volumes of

on

- road vehicle data into statistically representativ e subsets suitable for testing and evaluation purposes. These skillsets combined with NREL’s long

- standing efforts in evaluating the on

- road performance of conventional and advanced technology MD/HD vehicles for large commercial fleets are valuable tools t o provide enhanced information for the EPA Phase 2 GHG rulemaking.



## 1.2 NREL’s Fleet DNA Database The Fleet DNA clearinghouse of commercial fleet transportation data helps vehicle manufacturers and developers optimize vehicle designs while assisting fleet manag ers with the selection of advanced technologies for their fleets, all with the goal of reducing petroleum consumption and improving energy efficiency. This online database and tool

— available at http://www.nrel.g ov/fleetdna

— provides both static and interactive data summaries and visualizations representative of the real

- world “genetics” for medium

- and heavy

- duty commercial fleet vehicles.

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### The data for each visualization/report have been drawn from multiple fleet s operating in a variety of vocations for each vehicle type, across multiple locations within the United States. This extensive breadth of data is necessary to capture the wide range of vehicle operation resulting from different geographies and fleet applications demonstrated by medium

- and heavy

- duty vehicles. In addition to providing public access to commercial fleet data summaries and visualizations, Fleet DNA serves as a portal for users to explore rel ated DOE programs and projects such as N REL ’s f leet testing activities and reports, the DOE Office of Energy Efficiency and Renewable Energy’s Alternative Fuels Data Center, and the Clean Cities ’ National Clean Fleet Partnership program.

Designed by NREL in partnership with Oak Ridge National Laborato ry, this online database and the accompanying reports help vehicle manufacturers and fleets understand the broad operational range of many of today’s commercial vehicle vocations . See Appendix A for examples of the type of 1

- Hz data stored in the database.



## 1.3 NREL’s DRIVE Tool

The DRIVE analysis tool produces representative drive cycles from large amounts of vehicle data gathered via onboard logging devices. Developed by NREL, DRIVE uses global positioning system (GPS) and controller area network data to chara cterize vehicle operation and produce custom vehicle drive cycles based on real

- world activity, analyzing thousands of hours of data in a matter of minutes.

DRIVE quickly filters large sets of raw data, removing erroneous data points and repairing missing data sections before performing analyses covering 168 unique vehicle drive cycle metrics. Upon filtration and analysis, the tool is used to generate custom representative drive cycles from "ideal" sections of filtered data using specialized statistical clu stering methods. The duration of these representative drive cycles, which aim to match the fuel economy and performance characteristics of the source data, can be specified via user

- defined time limits, allowing for a wider range of testing and modeling ap plications.

In addition to DRIVE's ability to generate custom

- duration representative drive cycles from raw data, the tool is capable of comparing vehicle operation to industry standard test cycles and can even select a representative standard cycle from its built

- in library. Comparing the filtered data across the calculated drive cycle metrics allows the tool to select the standard cycle with the best fit.

Algorithms mimic the logic and expertise of a human engineer, cutting testing and analysis time by d ays or weeks while establishing a repeatable process and making information accessible through a simple interface. Additional output results range from simple tabulated summary statistics to Google Earth route maps, providing information that has enough de pth for scientific applications but is accessible to users without technical backgrounds.

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## 1.4 Segmenting the U.S. Vocational Vehicle Population and Developing Representative Drive Cycles and Weights

In support of ongoing refinement of EPA’s proposed GHG Phase 2 standards, NREL researchers were tasked with leveraging the ir unique collection of data, tools, and analysis expertise to successfully characterize and segment the U.S. Vocational Vehicle Population. Multivariate statistical analysis approaches would be applied to the collection of drive cycle and chassis data stored in the Fleet DNA database to produce a robust set of statistically representative vehicle segments. Examples of some of the key metrics that were examined include:

Drive cycle metrics:

 Avera ge driving speed

 Maximum driving speed

 Standard deviation of speed

 Kinetic intensity and its derivative components aerodynamic speed and characteristic acceleration

 Ratios of zero speed, low speed (0+

- 45 mph), and high speed (55+ mph) driving times and di stances

 Stops per mile

 Percentage of operating time spent at zero speed/idle, including time spent in and out of gear during said events

Chassis/Engine Parameters:

 Final gear ratio

 Gross vehicle weight rating

 Vehicle weight class

 Number of axles

 Engine pow er rating

 Engine displacement

 Engine rpm in top gear at 65 mph.

Having successfully identified a set of distinct vocational vehicle segments, NREL researchers were then tasked with developing a series of drive cycle weights which could be used in conjuncti on with existing proposed Phase 2 chassis dynamometer test cycles to describe each segment. In addition to developing drive cycle weights, NREL researchers also explored the development of both low and high speed representative transient test cycles using NREL’s DRIVE tool. The results of the drive cycle development and weights determination were then compared to existing standard chassis test cycles. This report documents the results of the work completed to date.

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# 2 Cluster Analysis

In this section we utili ze a set of eight metrics describing each drive cycle to attempt to define representative segments through cluster analysis. The metrics used in this analysis are:

 Aerodynamic Speed (ft/s)

 Characteristic Acceleration (ft/s2)

 Percent (%) of total cycle distance accumulated below 55 mph

 Percent (%) of total cycle duration accumulated at 0 mph

 Number of stops per mile

 Mean (nonzero) driving speed (mph)

 Maximum driving speed (mph)

 Standard deviation of (nonzero) driving speed (mph)

For this analysis, we u tilize a collection 16,250 daily drive cycles from 913 vehicles. Among those vehicles, 108 (5,071 cycles) are long haul trucks with sleeper cabs and 754 (10,765) are vocational vehicles. In this work we will present results for the entire sample population

, as well as detailed results for the vocational segment. Additionally, for the vocational segment, we utilize resampling based on true populations of vehicles in the U.S. to ensure our sample population has a composition that is a representative distribut ion.

Figure 1 : Sample composition

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. Figure 2 : A c orrelogram for the eight metrics gives a visual indication of the degree of linear

correlation (dependence) between each combination of variables

.



## 2.1 Principal Components Analysis

In order to explore the variability in the eight

- metric dataset, we perform a pairwise correlation (Figure 2) and a Principal Components Analysis (PCA). The correlogram shows the degree of linear dependence between each pair o f variables. For instance, we can see from this plot that mean speed and percentage below 55 mph are strongly inversely correlated while the mean speed and the AS standard are highly correlated. The PCA is a dimensionality reduction process that allows us to describe a higher dimensional dataset with a smaller number of dimensions that can be easily visualized. In PCA, each observation can be described by a weighted sum of orthogonal loadings (Eigen vectors; principle components). As its focus is dimensiona lity reduction, the orthogonal loadings are always fewer than the number of starting dimensions.

A PCA on the eight metric drive trace dataset suggests that two components (dimensions) are able to describe 75% of the variance in the complete dataset. The f irst four components are able to describe 91% of the variance and 6 components describe 99%. Tables 1 and 2 give the PCA loadings (rotation) for all vehicles and vocational vehicles. The first principal component convolves up

- weightings on driving speed an d standard deviation. The second principal component most heavily weights the percentage of time spent at zero. Figures 3 and 4 show how the population of drive cycles is distributed in a project two

- dimensional PCA space. Some vocational categories are mo re distinct in their characteristics than others

— Transit and Haul vehicles are most similar in their drive cycles, although the variance within those groups is relatively high. In both plots the rotation of the underlying variables is given at the center o f the plot. There are two modalities visible in this dataset which appear to be largely differentiated by

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### maximum speed and variability in speed, with a large cluster of mixed

- mode traces somewhere in between.

Table 1 . Principal Com ponent Loadings (All Vehicles)

Variable PC1 PC2 PC3 PC4 PC5 PC6

AS Standard 0.44

- 0.1 0.13

- 0.09

- 0.02

- 0.26

CA Standard

- 0.34

- 0.12 0.54 0.37 0.54

- 0.4
Percent below 55 mph

- 0.42

- 0.04

- 0.21 0.19 0.07 0.39
Percent at 0 mph

- 0.24

- 0.57

- 0.11

- 0.72 0.29

- 0.04
Stops per mile

- 0.34 0 0.58

- 0.25

- 0.68

- 0.07 Mean driving speed (mph) 0.43 0.13 0.1

- 0.2 0.11

- 0.29 Max driving speed (mph) 0.36

- 0.21 0.53 0 0.19 0.71 Driving speed SD (mph) 0.17

- 0.76

- 0.14 0.45

- 0.34

- 0.13

Table 2

. Principal Component Loadings (Vocational Vehicles)

Variable PC1 PC2 PC3 PC4 PC5 PC6

AS Standard 0.44 0.1 0.1 0.06

- 0.02 0.24

CA Standard

- 0.33 0.13 0.53

- 0.55 0.36 0.41
Percent below 55 mph

- 0.42 0.01

- 0.26

- 0.22

- 0.02

- 0.39
Percent at 0 mph

- 0.2 0.72

- 0.22 0.43 0.44 0.1
Stops per mile

- 0.32 0.1 0.59 0.49

- 0.51 0.04 Mean driving speed (mph) 0.43

- 0.15 0.07 0.19 0.23 0.3 Max driving speed (mph) 0.35 0.23 0.48

- 0.07 0.25

- 0.72 Driving speed SD (mph) 0.27 0.6

- 0.13

- 0.42

- 0.55 0.1

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. Figure 3 : All drive cycles for all vehicles visualized in the space defined by the first two principal components. Long haul vehicles are present in a high speed grouping. Color is given per vocation.

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. Figure 4 : Scatter plot of all vocational drive cycles. Refuse and transit vocations stand out in their usage as compared to the rest of the sample.


## 2.2 Drive Cycle Clustering

In this section we aim to group similar drive cycles and derive representative drive cycle cha racteristics based on the central tendency of each cluster. After some evaluating various clustering methods for this data (K

- means, hierarchical, etc.), we have selected the K

- medoids algorithm as the best candidate. K

- medoids functions well on large data sets by optionally clustering random subsamples. This algorithm works by first randomly selecting a number of cluster centers specified a priori. It then assigns all points to the closest cluster. New cluster centers are chosen and the operation repeats u ntil a convergent set of optimal clusters are found. Unlike K

- means which provides metric averages for each cluster, the K

- medoids algorithm selects a most

- representative data point (Kaufmann 1990). To determine an optimal number of clusters in the data, w e utilize the silhouette method described in ( Rousseeuw 1987) where clustering is performed sequentially starting with a goal of two clusters and iterating with additional clusters. At each iteration, the silhouette analysis describes the ratio between tig htness (within cluster variance) and separation (between cluster variance). Based on this analysis, we

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. 

### select two clusters as the optimal clustering in the metric space. Figure 5 shows the resulting two clustering for vocational vehicles, which finds the u nderlying bimodal structure and splits the space logically. A small section of detached vehicles at the bottom of the left cluster are school buses with low average speed compared to the other vehicles in the cluster. For the sake of clarity, in the follow ing sections we will refer to the left cluster, which contains slower speed cycles with more stops, as the ‘Slow’ cluster and the right cluster, which contains higher speed cycles with fewer stops, as the ‘Fast’ cluster. In this plot, as in the prior plots , each point represents one drive cycle in the entire data set. Points are colored according to their optimized cluster placement. A single vehicle may have any number of drive cycles, which may over or under

- represent individual vehicles in these plots.

Figure 5 : Two clustering using k

- medoids algorithm with cluster centers marked in black. Only vocational vehicles are drawn in this plot.

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. Figure 6 : Three clustering using k

- medoids algorithm with cluster centers marked in black. Only vocational vehicles are shown in this plot. 

### For regulatory purposes where it may be useful to consider three classes of vehicles, we also calculate an optimal solution with three clusters for the vocati onal vehicle traces. Figure 6 shows this data. The first two clusters are joined in this plot by a middle cluster that contains those traces that do not clearly fall into either the left (slower) or right (faster) cluster. Tables 2 and 3 give the statistic s for the center point of each cluster along with the vocation of the most representative vehicle.

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Table 3 . Vocational Drive Cycles

– 2

- Clustering Centers

Medoid Cycle Vocation AS

Standard CA

Standard Percent

Below 55 mph Percent

at 0 mph Mean

stops/mile Mean

driving speed Max

driving speed Driving

speed Std Dev.

Left (Slow) Cluster Refuse

Pickup 54.59 0.48 91.08 50.62 2.68 21.26 59.22 16.28
Right (Fast) Cluster Drayage 81.21 0.28 26.62 22.11 0.24 43.76 65.01 21.06

Table 4 . Vocational Drive Cycles

– 3

- Clustering Centers

Medoid Cycle Vocation AS

Standard CA

Standard Percent

Below 55 mph Percent

at 0 mph Mean

stops/mile Mean

driving speed Max

driving speed Driving

speed Std Dev.

Left (Slow) Cluster School

Bus 48.88 0.50 97.85 47.87 1.22 24.12 62.61 13.03

Middle Cluster Towing 69.27 0.68 64.46 52.68 0.98 34.50 67.86 18.75
Right (Fast) Cluster Freight 84.85 0.20 25.38 25.43 0.26 47.70 70.79 20.48



### In order to utilize cluster centers to define a representative drive cycle for testing purposes, we combine the drive cycle characteristics from the top 50 points closest to these centers as ranked by multivariate least squares distance from the centroid to determine the cluster averages for each metric

.

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## 2.3 Data Resampling

Although the Fleet DNA database provides detailed data for a great number of vehicles, in order to draw broad conclusions about how vehicles behave we must a ddress sources of potential bias in this dataset. This bias may arise simply because those fleets most willing to contribute data to Fleet DNA may not be perfectly representative of the entire population of vehicles in the wild. In this section we perform resampling based on EPA’s MOtor Vehicle Emission Simulator (MOVES) study categorization, compute new clusters based on the new vehicle population, and then measure the difference in cluster center. Using this analysis we can determine the degree to which t he entire Fleet DNA sample population differs from the population of vehicles proportioned according to true population statistics.

Tables 5 gives the MOVES

- equivalent vehicle counts and proportions for resampling as compared to the entire Fleet DNA data. The Fleet DNA data appears to have a smaller fraction of Short Haul vocational vehicles while having relatively more school buses, refuse trucks, and transit buses. The resulting resampled population would contain 108 fewer vehicles if resampled according to these proportions. A MOVES

- equivalent sub

- category resampling, which includes vehicle weight class, is given in Table 6. In order to match proportions of each category within weight classes, the total population of vehicles must be decreased by 306 (nea rly half of all vocational vehicles).

Table 5 . Vocational Resampling by MOVES Categories

Vocation Entire Sample Population MOVES Resampled Population

Short Haul 631 (58.9%) 631 (84.1%)

School Bus 254 (23.6%) 82 (10.9%)

Refuse 57 (5.3%) 29 (3.9%)

Transit Bus 23 (2.1%) 9 (1.1 %)

N/A 108 (10.1%) Figure 7 : Resampling flow for vocational vehicles

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Table 6 . Vocational Resampling by MOVES Sub

- categories

Vocation Entire Sample Population MOVES Resampled Population

Short Ha ul

– Class 4/5 68 (6.3%) 53 (34.6%)

Short Haul

– Class 6/7 155 (14.4%) 29 (19.0%)

Short Haul

– Class 8 224 (20.9%) 21 (12.4%)

Refuse

– Class 6/7 2 (0.2%) 0 (0%)

Refuse

– Class 8 55 (5.1%) 4 (2.6%)

School Bus

– Class 6/7 212 (19.8%) 12 (7.8%)

School Bus

– Class 8 27 (25.2%) 1 (0.7%)

Transit Bus

– Class 6/7 3 (0.3%) 0 (0%)

Transit Bus

– Class 8 20 (1.9%) 1 (0.6%)

N/A 306 (28.5%)



### In order to evaluate how resampling the underlying data may change the extant drive cycle clusters, we perform a 10

- fold evaluation, where 10 random subsamples that are consistent with the MOVES proportions are selected and used for cluster analysis. Figur es 7 and 8 show the center value of each of the 8 metrics both with and without sampling for MOVES categories and subcategories. Figures 9 and 10 show how quickly the values stabilize when averaging results from iterative resamplings for both MOVES categor ies and subcategories. These plots show that after just 5 resamplings there is not a meaningful degree of additional variability.

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. Figure 8 : Difference between resampled cluster center statistics and original Fleet DNA (entire population) statistics for two clusters. The left pane shows the median value for the slow (cluster 1) population for each metric as a red dot. The right pane shows the same for the fast (cluster 2) population. In each pane, the boxplots give the distribution of median values with random MOVES

- based resamplings.

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. Figure 9 : Difference between resampled cluster center statistics and original Fleet DNA (entire population) statistics for two clusters. This plot uses MOVES subcategory resampling. The left pane shows the median value for the slow (cluster 1) population for each metric as a red dot. The right pane shows the same for the fast (cluster 2) population. In each pane, the boxplots give the distribution of median values with random MOVES

- based resamplings. The subcategory resampling has smaller variance in the metrics because of a smaller sample size.

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. Figure 10 : Conve rgence on final cluster center metrics as a function of successive resamplings

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. Figure 11 : Convergence on final cluster center metrics as a function of successive MOVES subcategory resamplings



## 2.4 Vehicle Clustering

The prior sections have focused on drive cycle clustering. In this section we generalize the approach to the level of vehicles. Rather than segmenting vehicles based on a notion of their average use, which may hide multiple modalities or be skewed by outlier drive c ycles, we instead assign each vehicle to the cluster that the majority of its drive cycles belong. This assignment can be computed trivially using the statistical mode function on each vehicle’s drive cycle cluster assignments. More complex definitions of cluster mode can also take into account the multi

- modalities by, e.g., assigning vehicles with a mixture of modalities to a third (center) cluster.

Figures 12 through 14 show the result of this approach to per

- vehicle classification. Each point in the scat terplot is placed according to the vehicle’s average statistical behavior. The cluster

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### assignment for each vehicle is the most common cluster among that vehicle’s drive cycles. In Figure 14, the relationship between cluster membership, operational characte ristics, and vocation is shown. Transit vehicles (buses) are entirely contained in the left (Slow) cluster, while delivery, special purpose, and utility vehicles are present in both clusters. In the next section we will explore possibilities for predicting vehicle cluster membership (and hence, drive characteristics) using information available at the time of regulatory testing.

Figure 12 : Trace

- mode clustering of Fleet DNA Vocational vehicles using two

- cluster solution. This plot provides a simplified (condensed) version of the entire drive cycle data. Each vehicle’s location in the scatterplot is given by average statistics. Cluster assignment is by the mode of trace cluster membership (most common cluster).

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. Figure 13 : Trace

- mode clustering of Fleet DNA Vocational vehicles using three

- cluster solution. This plot provides a simplified (condensed) version of the entire drive cycle data. Each vehicle’s location in the scatterplot is given by average statist ics. Cluster assignment is by the mode of trace cluster membership (most common cluster).

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. Figure 14 : Trace

- mode clustering of Fleet DNA Vocational vehicles using two

- cluster solution. This plot provides a simplified (condensed) version of the entire drive cycle data. Each vehicle’s location in the scatterplot is given by average statistics. Cluster assignment is by the mode of trace cluster membership (most common cluster). Vocations are provided to show vocational segmentation i n the PCA space.

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# 3 Predictive Modeling

In this section we seek to understand whether it is possible to predict drive cycle characteristics using limited information available about vehicles at the time of regulatory testing. In addition, we aim to quantify the efficacy and general purpose accuracy of this method. For a given vehicle, the information that a predictive model could use is limited to those variables that are in both the Fleet DNA database, and are present at the time of test:

 Transmission Type

 Fuel Type

 Drivetrain Type

 Engine RPM at 55 mph

 Engine RPM at 65 mph For the vast majority of vehicles, RPM at 65 and 55 mph are trivially correlated, as can be seen in Figure 15. Based on this, we use RPM at 65 mph, which most likely corresponds to the fin al gear for analysis. Figure 16 shows the data coverage for RPM data in the Fleet DNA data according to vocational category. All vocations are well represented aside from the Transit category (city buses and school buses) which appear under

- represented in terms of availability of engine RPM data. This plot also makes clear that there is not an obvious or simple relationship between gearing and vocational category.

Figure 15 : Scatter plot showing correlation between RPM at 55 mph and RPM at 65 mph for different vehicle fuel types. 

### Figures 17 and 18 show the relationship between engine RPM and vehicle cluster categorization. Our approach to modeling in this section stems from the observation that gearing (i.e., as indicated by RPM a t high speeds) is indicative of typical driving characteristics and hence cluster

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### membership. Mean RPM for vehicles with the majority of their operation in cluster 1 (slower cluster) is 1975, while it is 1563 for cluster 2 (faster cluster), inter

- quartile range for cluster 1 (slower) is 1704 to 2268, while it is 1354 to 1710 for cluster 2 (faster). Figure 18 draws out additional relationships between typical gearing and fuel and transmission type. We can see that CNG, Gasoline, and Hybrid vehicles are exclu sively in cluster 1 in the Fleet DNA data. Vehicles with manual transmissions are exclusively in cluster 2, while vehicles with automatic transmissions are present in both clusters.

The remainder of this section explores using these features as parameters in a logistic regression to predict whether a vehicle will likely belong to cluster 1 (slower cluster) or cluster 2 (faster cluster). In this analysis, we do not consider the three category case, as vehicles whose operation lies in between these two cluste rs are not readily differentiable from RPM data alone.

Figure 16 : Engine RPM data available for different vocational categories. Transit vehicles are under

- represented in the Engine RPM dataset. Grey points are vehicles that do not have RPM data available in the FleetDNA database. The remainder of vehices are colored according to their RPM at 65 mph.

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. Figure 17 : Engine RPM has a clear bimodality which corresponds with two

- cluster assignments. In this boxplot, the median is given as a horizontal line and interquartile range is described by the height of the boxes. From this plot, it is clear that the distribution (both central tendency and

spread) in RPM between clusters 1 and 2 is different.

Figure 18 : Distribution of Engine RPM characteristics for different types of vehicles. Vehicles with manual transmissions are exclusively in cluster 2 (Fast), with lower average gearing. Gasoline, CNG, and Hybrid vehicles are excl usively in cluster 1 (Slow) with similar central tendency to

(mean/median) Diesel vehicles in the same cluster.

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## 3.1 Logistic Regression Model

Multiple logistic regression seeks to classify input data into two (binomial) outcomes by using a weighted combinatio n of fitted logistic curves. Each logistic curve describes the relationship between a single attribute and the probability of being in a particular category. In our application, we will use information about a vehicle’s transmission, fuel type, and RPM at 65 mph to classify vehicles as being in one of two characteristic clusters.

Model evaluation is performed using standard k

- fold cross

- validation where 25% of data are withheld while 75% of data are used for fitting. The fitted model is used to predict the 25% of data that are withheld. This process is repeated 100 times and average model error rates are analyzed. Fitted parameters are obtained using a generalized linear least squares optimization using a binomial model.

3.1.1 Full Model

Using all available featur es, we are able to achieve 89% accuracy (N = 240) at predicting whether a vehicle is likely to be placed in cluster 1 or cluster 2. The accuracy per cluster is not quite symmetric since our training data is skewed towards cluster 1. Accuracy at correctly

a ssigning vehicles to cluster 1 is 94%, while the accuracy at placing vehicles correctly in cluster 2 is 75%.

This model shows the strongest relationship between RPM at 65 mph and cluster mode, which has a statistically significant relationship to the outco me variable (p

- value << 0.05, Wald chi

squared test (Diggle 1994)). Figure 19 shows the relationship between RPM at 65 and cluster assignment. Transmission type (i.e., Automatic vs. Manual) also appears to have a statistically significant relationship. The other variables do not show as strong a relationship with the cluster outcomes (i.e., a 95% CI on their effect crosses zero), but still improve overall model accuracy, so we keep them here because they may be applicable to an application to data with a di fferent composition (the Vocational Fleet DNA database has very few Gasoline and Hybrid vehicles compared to Conventional Diesel vehicles). Table 7 gives fitted coefficients for the full model:

Pr ( cluster 2 | rpm 65 & trauto & dtconv & f uelgas ) = σ ( t ) = 1
1 + e − t



### t = 5

. 295

− 0

. 003415 ∗ rpm 65

− 1

. 231 ∗ trauto

− 10

. 623 ∗ fuelgas + 1

. 196 ∗ dtconv

Where :

t is the logistic exponent

σ(t) is the probability of being in cluster 2 (faster cluster) given the evidence

rpm65 is the RPM of the vehicle at 65 mph

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### trauto is True if the vehicle has an automatic transmission and is False otherwise

dtconv is True if the vehicle has a convential drivetrain and is False otherwise

fuelgas is True if the vehicle has a Gasoline engine an d is False otherwuise

Figure 19 : Logistic histogram showing the relationship between RPM at 65 mph and the probability of being in cluster 2 (Fast)

. The blue bars show the actual distribution of RPM measurements in each cluster. The red curve shows the shape of the logistic function. This

analysis is only for two clusters, hence there is no middle cluster.

Table 7 . Fitted Coefficients for Full Logistic Regression (Fleet DNA Vocational Vehicles Only)

N=240

Estimated Coefficient (X) Coefficient 95% CI Wald Chi

- Square

p - value

(Intercept) 5.295 2.917

- 7.842

RPM at 65 mph (rpm65)

- 0.003 415

- 0.005

- 0.002 < 0.01

Transmission = Automatic (trauto) - 1.231

- 2.069

- 0.396 < 0.01

Fuel = Gasoline (fuelgas)

- 1 0 .623 NA

- 76.432 0.99

Drivetrain = Conventional (dtconv) 1.196

- 0.033

- 2.679 0.08

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### 3.1.2 Univariate Diesel Model

To streamline the process of prediction, we build a simplified logistic regression for Diesel vehicles using only RPM at 65 mph to determine cluster membership. This model achieves 81% accuracy (N = 260). Accuracy is similarly asymmetric depending on cluster: accuracy for cluster 1 is 87% while cluster 2 is 70%. Table 8 provides fitted parameters for this model. The probability of membership in cluster 2 can be calculated as follows:

Pr ( cluster 2 | rpm 65 ) = σ ( t ) = 1
1 + e − t



### t = 5

. 277

− 0

. 00328 ∗ rpm 65

Table 8 . Fitted Coefficients for Univariate Diesel

- only Logistic Regression

(Fleet DNA Vocational Vehicles Only)

N=260

Estimated Coefficient (X) Coefficient 95% CI Wald Chi

- Square

p - value

(Intercept) 5.177 3.733

- 6.731

RPM at 65 mph (rpm65)

- 0.003280

- 0.004

- 0.002 < 0.01



## 3.2 Validation and Error Process

In this section, we seek to understand sources of error in the logistic regression classification. In particular, we want to know whether misclassifications are systematic or random in nature, particularly since systematic errors can be strategically corre cted. Figure 20 shows the location of the 25 vehicles who account for 91% of misclassifications during the 100

- fold cross validation. We can see that these vehicles’ drive characteristics are roughly in the middle.

Cluster 2 (fast) vehicles that are consi stently misclassified into cluster 1 are largely automatic diesel delivery vehicles with conventional drivetrains and high RPM at 65mph (mean RPM 2138). It stands to reason that these vehicles would be misclassified since according to the Fleet DNA data, v ehicles with a high mean RPM at 65mph are generally used for slow applications (cluster 1). Similarly, cluster 2 vehicles more often have manual transmissions than this group of vehicles.

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. Figure 20 : Locations (red points) of 40 most often misclassified vehicles (in 100

- fold cross

validations) using the full logistic regression model.



### Cluster 1 (slow) vehicles that are consistently misclassified into cluster 2 are largely conventional diesel vehicles with automatic transmissions used for special purposes (e.g., tanker, concrete, drayage, dump truck, etc.). These vehicles have a gearing that is consistent with faster moving (cluster 2) vehicles (mean RPM at 65mph = 1354).

Based on this analysis, we observe that misclassification oc curs in two scenarios:

1. Vehicles designed for one type of application (e.g., local deliveries) are used consistently outside of their intended use (e.g., highway transit). These vehicles are generally delivery vehicles (Beverage delivery, Food delivery, Con crete, or Linen delivery). Since these vehicles, whose design is inconsistent with their use, is a small fraction of overall vehicles, and the errors cannot be addressed systematically without additional knowledge (i.e., vocational categorization).

2. Vehicle s have multi

- modal usage, with a fraction of time spent in applications consistent with cluster 1 and a fraction of time spent in applications consistent with cluster 2. These vehicles are generally special purpose vehicles (e.g., drayage, utility, warehou se delivery) as well as parcel delivery vehicles. Misclassified drayage trucks, for instance, spend

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### approximately 63% of their drive cycles operating in cluster 1 (presumably at port) and 37% of their drive cycles operating in cluster 2 (presumably doing h ighway transfers). Since these vehicles are geared for slower use, the model misclassifies them into cluster 1 when they may actually spend a larger fraction of days operating in a way consistent with vehicles in cluster 2.

Both misclassifications could be addressed using additional information about the vehicle use at the time of testing (e.g., vocational information), since the limited available information (gearing and chassis) is insufficient to identify these special cases in practice. Nevertheless, they are a small percentage of the overall vehicles (approximately 10%).

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# 4 Development of Representative Transient Drive Cycle Component

Having segmented the U.S. medium

- and heavy

- duty commercial vehicle data into a collection of three distinct clusters b ased on a multivariate drive cycle clustering analysis while also developing a logistic model to predict cluster participation, the results of the clustering analysis were then used as a basis for the development of both a representative low and high speed transient drive cycle. Deploying NREL’s DRIVE Tool as shown in Figure 21 within its High Performance Computing environment, researchers condensed thousands of hours of drive cycle data down into representative drive cycles that are approximately 12 and 20 minutes in duration.

Figure 21 : Screen c apture of DRIVE GUI processing data



## 4.1 Generation of Representative Low Speed Transient Component Using DRIVE TM


### To develop the representative low speed transient cycle, the top 50 drive cycl es from each of the 3 clusters described in section 3 were ranked by root mean squared (RMS) distance from medoid and fed into DRIVE as source data. Using the top 150 total drive cycles most representative of their respective clusters, a variety of input p arameters such as desired and minimum cycle durations were adjusted to generate over 100 unique cycles. These cycles were then compared using a non

- weighted least squares approach using the eight drive cycle metrics from the

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### clustering analysis described in section 3. Each of the drive cycles were then compared to the average values for the top 50 cycles in the low speed cluster.

Several additional constraints were applied to the DRIVE tool including:

 Targeted cycle duration of 668 seconds to match the dur ation of the California Air Resources Board (CARB) Heavy Heavy

- Duty Diesel Truck (HHDDT) Transient Cycle.

 A minimum allowable cycle duration of 300 seconds was established to ensure sufficient test duration.

 Exclusion of any drive cycles with a maximum spe ed in excess of 55 mph to ensure generated drive cycle is representative of low speed transient operation only

To examine the effects of population makeup on the resultant transient cycle, the drive cycle generation process was repeated a total of 3 times: one for each of the unique populations explored in the cluster analysis (Fleet DNA, MOVES resampled data, and MOVES subcategory resampled data

– see section 3).

The resulting transient cycle shown in Figure 22 was developed using 150 drive cycles drawn fr om the MOVES resampled Fleet DNA database. These source drive cycles were then processed using DRIVE with targets for the representative drive cycle to match the overall drive cycle characteristics of the full Fleet DNA population instead of the MOVES sub sampled population. The unique combination of source data and drive cycle targets produced the highest ranked representative low speed transient cycle shown in Figure 22.

Figure 22 : Speed

- time trace for representative low spee d EPA transient cycle 

### 0 10 20 30 40 50 60

1 28 55 82 109 136 163 190 217 244 271 298 325 352 379 406 433 460 487 514 541 568 595 622 649 676 703 Speed (mph)



### Time (s) 

# NREL

- EPA Low Speed Transient

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### Key statistics for the final low speed transient cycle include:

 724 seconds in duration

 Total of 10 microtrips

 Maximum speed of ~52 mph

 Average driving speed of ~21 mph

 ~22% of total cycle duration is at zero speed

o The target zero speed duration for the representative transient cycle was less than 24.5%. This value was chosen as it represents the the percentage of zero speed time observed from the high speed cluster identified in Section 3 (cluster 3

- 3). The high speed cluster pos sesses the lowest amount of zero speed time of all of the clusters, thus it was used as an upper bound when developing the low speed transient cycle.



## 4.2 Generation of Representative High Speed Transient Component Using DRIVE TM



### An approach similar to the one employed for the low speed transient cycle was also used to develop a high speed transient cycle. The same top 50 drive cycles from the three clusters identified in Section 3 were fed into DRIVE and used as source data for the generation of the high speed transient cycle. Inputs such as the desired and minimum cycle duration and the set of drive cycle metrics to match were then swept to generate hundreds of unique drive cycles. These cycles were then compared using a non

- weighted least squares approach. I n the case of the high speed transient however, modifications were made to the DRIVE constraints to ensure the generation of a high speed transient cycle.

The key DRIVE constraints for the high speed transient cycle included:

 Targeted 2,050

- second duratio n to match the CARB HHDDT Cruise Cycle duration.

 A minimum allowable cycle duration of 900 seconds was established to ensure sufficient test duration.

 Exclusion of any drive cycles with a maximum speed below 55 mph to ensure generated drive cycle is repres entative of high speed transient operation only

Shown in Figure 23 is the resulting representative high speed transient cycle developed from the Fleet DNA data. As one can see, the resulting transient is significantly shorter than the CARB HHDDT transient, possessing approximately half the duration.

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. Figure 23 : Speed

- time trace for representative high speed EPA transient cycle 

### Key statistics for the final high speed transient cycle include:

 1,208 seconds in duration

 Total of 1 mic rotrip

 Maximum speed of ~70 mph

 Average driving speed of ~54 mph

 ~19% of total cycle duration is at zero speed

o Similar to the low speed transient, a target of 24.5% zero speed time as an upper bound was selected for the generation of the high speed transie nt. Since this cycle consists of a single high speed transient microtrip and was generated using real world data, the options for zero speed time collection are at either the beginning or end of the cycle. Due to the definition of microtrip (driving to n ext driving event including any zero speed time) zero speed time for this cycle is accumulated at the end of the cycle rather than the beginning.



## 4.3 Comparison of DRIVE Generated Transient Cycles to Existing Standard Cycles

Having developed a set of representative transient test cycles, the next step in the analysis was to compare the drive cycle characteristics of the generated drive cycles to established standard test cycles from around the world. A series of four standard test cycles were chosen for comparison, the U.S. CARB Heavy Heavy

- Duty Diesel Truck HHDDT Transient, the International World Harmonized Vehicle Cycle (WHVC), the European Test Cycle (ETC) also known as the FIGE Cycle, and the Japanese JE05 cycle. In sections 4.3.1 we provide illustrations and brief 0 10 20 30 

### 40 50 60 70 80

1 46 91 136 181 226 271 316 361 406 451 496 541 586 631 676 721 766 811 856 901 946 991 1036 1081 1126 1171 Speed (mph)



### Time (s) 

# NREL

- EPA High Speed Transient Component

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### descriptions of these standard cycles, followed by more in

- depth comparisons of the standard cycles to the representative cycles generated by DRIVE.

4.3.1 Standard Test Cycles Selected for Comparison
4.3.1.1 Californi

a Air Resources Board Heavy Heavy

- Duty Diesel Truck Transient Cycle

The CARB HHDDT Transient Cycle is a chassis dynamometer test cycle developed by CARB in collaboration with West Virginia University using real world data captured from trucks operating in the state of California. It is the current transient component defined in EPA’s proposed GHG Phase 2 regulations.

Figure 24: Speed

- time trace for CARB HHDDT transient cycle 

### Some key drive cycle characteristics of the CARB HHDDT include:

 668 seconds in du ration

 Contains five microtrips

 Maximum driving speed of 47.5 mph

 2.85 miles in length.

4.3.1.2 World Harmonized Vehicle Cycle (WHVC)

The WHVC cycle is a chassis dynamometer test cycle developed based on the same set of data used for the development of the World Harmonized Transient Cycle (WHTC). The first 900 seconds of the cycle are representative of urban driving, while the following 481

- and 419

- 0 10 20 30 40 50

1 26 51 76 101 126 151 176 201 226 251 276 301 326 351 376 401 426 451 476 501 526 551 576 601 626 651 Speed (mph)



### Time (s) 

# CARB HHDDT Transient Cycle

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### second sections represent rural and highway driving, respectively. Shown in Figure 25 is the speed

- time trace for t he WHVC cycle.

Figure 25 : Speed

- time trace for WHVC 

### Some key drive cycle characteristics of the WHVC include:

 1,800 seconds in duration

 Contains 11 microtrips

 Maximum driving speed of 54.56 mph

 11.22 miles in length.

4.3.1.3 European Transient Cycle (ETC)/FIGE Cycle

The ETC cycle was developed by the former FIGE Institute in Aachen, Germany based on real world drive cycle data captured from in

- use heavy duty vehicles. The ETC cycle consists of three segments, representative of urban, rural, and highway driving. With a total duration of 1

, 800 seconds, each segment represents 1/3 of the total duration of the overall cycle. The first segment represents urban driving conditions, the second segment represents rural driving, and the final segment represents highway dr iving. 0 10 20 30 40 50 60

1 71 141 211 281 351 421 491 561 631 701 771 841 911 981 1051 1121 1191 1261 1331 1401 1471 1541 1611 1681 1751 Speed (mph)



### Time (s) 

# World Harmonized Vehicle Cycle (WHVC)

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. Figure 26 : Speed

- time trace for ETC FIGE Cycle 

### Some key drive cycle characteristics of the ETC/FIGE include:

 1,800 seconds in duration

 Contains six microtrips

 Maximum driving speed of 56.61 mph

 18.33 miles in length.

4.3.1.4 JE05 Cycle

In 2005, new Japa nese emissions standards resulted in the development and introduction of the JE05 emission test cycle. The JE05 was developed using data captured from both diesel and gasoline commercial vehicles operating in Tokyo. The test cycle was developed for use by heavy vehicles with gross vehicle weights (GVW) above 3,500 kg. 0 10 

### 20 30 40 50 60

1 68 135 202 269 336 403 470 537 604 671 738 805 872 939 1006 1073 1140 1207 1274 1341 1408 1475 1542 1609 1676 1743 Speed (mph)



### Time (s) 

# ETC/FIGE Cycle

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. Figure 24 : Speed

- time trace for JP JE05 cycle 

### Some key drive cycle characteristics of the JE05 include:

 1,829 seconds in duration

 Contains 14 microtrips

 Maximum dri ving speed of 54.43 mph

 8.63 miles in length.

4.3.2 Drive Cycle Characterization and Comparison

To benchmark the representative transient cycles developed from the Fleet DNA data, a detailed drive cycle characterization was performed using DRIVE. The eight metr ics used in the drive cycle segmentation and clustering analysis were used to compare each of the different drive cycles. Shown in Table 9 are the results of the drive cycle characterization.

Table 9 . Comparison of Standard Cycle Dr ive Cycle Metrics

Cluster Aerodynamic

Speed (ft/s) Characteristic

Acceleration (ft/s 2 ) Percent of

mileage accumulated below 55 mph Percentage

of Cycle Time at Zero Speed Number

of Stops Per Mile Average Driving Speed (mph) Maximum

Driving Speed (mph) Standard

Deviation of Driving Speed (mph)

CARB HHDDT Transient 43.36 0.49 100.00 15.57 1.40 18.20 47.50 13.38

WHVC 61.00 0.28 100.00 13.61 1.07 25.98 54.56 19.03 

### 0 10 20 30 40 

### 50 60

1 69 137 205 273 341 409 477 545 613 681 749 817 885 953 1021 1089 1157 1225 1293 1361 1429 1497 1565 1633 1701 1769 Speed (mph)



### Time (s) 

# JE05 Cycle

### 37

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. Cluster Aerodynamic

Speed (ft/s) Characteristic

Acceleration (ft/s 2 ) Percent of

mileage accumulated below 55 mph Percentage

of Cycle Time at Zero Speed Number

of Stops Per Mile Average Driving Speed (mph) Maximum

Driving Speed (mph) Standard

Deviation of Driving Speed (mph)

JE05 51.31 0.40 100.00 25.19 1.62 22.70 54.43 15.94

ETC FIGE 68.71 0.23 94.46 0.00 0.00 36.65 56.61 17.83

NREL

- EPA Transient High 85.01 0.25 25.13 19.09 0.07 54.28 69.92 24.13

NREL

- EPA Transient Low 50.99 0.59 100.00 21.69 2.98 21.31 51.67 16.16

FDNA 1

- Average 50.59 0.55 96.45 47.30 1.71 22.62 62.39 13.74



### From Table 9, one can see that the NREL

- EPA low speed transient closely matches the statistics of slow speed urban cluster number 1 (FDNA 1

– Average) and the CARB HHDDT Transient. The cycles are in very close agreement in both Aerodynamic Speed and Charac teristic acceleration, with a noteable difference in the percentage of total cycle time spent at zero speed, and the number of stops per mile. The differences in zero speed time as a percent of overall cycle time can be adjusted with post processing of tes t data, or the inclusion of additional idle time allowing the transient to be adjusted to match the idle characteristics of any of the clusters identified during the segmentation analysis.

Comparing the high speed transient and low speed transient to the c omplete WHVC, JE05, and ETC FIGE cycles, we can see that the maximum speed achieved by the representative cycles closely match those of the standard test cycles, with the EPA high speed transient possessing a higher maximum driving speed than any of the st andard drive cycles.

4.3.3 Analysis of Mileage Accumulation by Vehicle Speed

As part of the drive cycle analysis, a detailed characterization of mileage accumulation by vehicle speed bin was performed to further aid in understand the driving behavior represented by each of the cycles. The mileage accumulation for each of the drive cycles examined can be found in Table 10 and Table 11, where mileage is accumulated in 5 mph speed bin increments as a percentage of total miles traveled over the cycle. By splitting the data into 5 mph speed bins, researchers were able to examine transien t speed mileage accumulation (<45 mph), rural (45

- 55 mph), and highway type (55 mph) driving behavior at a finer level of detail than could be described by any single high level drive cycle metric.

### 38

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. Table 10 . Sub 45 mph Mileage Accu mulation Comparison for Standard Cycles

Cycle 0+

- 5

mph distance (%) 5+

- 10

mph distance (%) 10+

- 15

mph distance (%) 15+

- 20

mph distance (%) 20+

- 25

mph distance (%) 25+

- 30

mph distance (%) 30+

- 35

mph distance (%) 35+

- 40

mph distance (%) 40+

- 45

mph distance (%)

CARB HHDDT Transient 1.25 12.72 3.97 9.02 18.20 10.20 15.72 11.96 8.29

WHVC 1.07 3.87 6.11 7.47 4.34 6.34 9.30 7.42 8.09

JE05 0.97 4.68 8.98 9.53 9.32 13.53 12.14 12.10 2.06

ETC FIGE 0.19 0.60 2.40 4.24 3.81 2.36 1.31 3.50 17.49

NREL

- EPA Transient High 0.02 0.08 0.16 0.29 0.42 0.84 1.46 6.19 2.87

NREL

- EPA Transient Low 2.66 4.40 5.42 7.08 8.37 8.30 15.15 18.69 12.55

FDNA 1

- Average 1.39 4.35 7.11 8.90 11.05 12.95 15.15 14.06 10.69



### Looking at the data in Table 10, a few trends become apparent. First is that the ETC FIGE cycle does not accumulate much mileage below 40 mph, while other cycles like the CARB HHDDT Transient, the JE05, and the NREL

– EPA Transient accumulate a more equally distributed set of mileage at speeds below 45 mph. These findings are in agreement with the averages calculated for the slow speed cluster 1 identified in the transient analysis.

Table 11 . 45 + mph Mileage Accumulation Comparison for Standard Cycles

Cycle 45+

- 50 mph

distance (%) 50+

- 55 mph

distance (%) 55+

- 60 mph

distance (%) 60+

- 65 mph

distance (%) 65+

- 70 mph

distance (%) 70+

- mph

distance (%)

CARB HHDDT Transient 8.66 0.00 0.00 0.00 0.00 0.00

WHVC 7.28 38.71 0.00 0.00 0.00 0.00

JE05 9.46 17.23 0.00 0.00 0.00 0.00

ETC FIGE 15.41 43.14 5.54 0.00 0.00 0.00

NREL

- EPA Transient High 4.77 8.03 23.23 38.74 12.89 0.00

NREL

- EPA Transient Low 12.75 4.64 0.00 0.00 0.00 0.00

### 39

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. Cycle 45+

- 50 mph

distance (%) 50+

- 55 mph

distance (%) 55+

- 60 mph

distance (%) 60+

- 65 mph

distance (%) 65+

- 70 mph

distance (%) 70+

- mph

distance (%)

FDNA 1

- Average 6.57 4.22 2.52 1.00 0.04 0.00



### Examining Table 11 in greater detail, we can see that the high speed EPA transient cycle is the only cycle analyzed with more than 5% of its mileage accumulated at speeds in excess of 60 mph. The bulk of the ETC (43.14%) and WHVC (38.71%) cycle mileage is accumulated at speeds between 50 and 55 mph. Looking at this table further, we can see that the CARB HHDDT Transient does not accumulate any mileage at speeds in excess of 50 mph. Compare this to the roughly 8% of total mileage accumulated at speeds in exc ess of 50 mph for slow speed cluster 1, and it can be seen that there is a need for more high speed (45

- 65 mph) transient speeds in the final low speed transient test cycle.

Visualizing the mileage accumulation for slow speed cluster 1, NREL

- EPA low speed transient, and CARB HHDDT Transient distributions shown in Table 10 and Table 11, one can see that the cluster mileage accumulated is normally distributed with either a slight skew towards low speed (CARB HHDDT Transient) or high speed (EPA Transient Low) for the test cycles. These results can be seen in Figure 28.

Figure 25 : Comparative distribution of mileage accumulation binned by vehicle speed for low speed transient 

### Figure 28 illustrates one of the limitations of the CARB HHD DT Transient which is no mileage accumulation at speeds in excess of 50 mph.

0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 

### 16.00 18.00 

### 20.00 Percentage of Total Mileage Accumulated 

# Comparative Mileage Accumulation Distributions

FDNA 1 EPA Transient Low CARB HHDDT Transient

### 40

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. 

### Figure 29 shows a similar distribution of mileage accumulation for the standard chassis test cycles compared to the combined NREL

- EPA low speed and high speed transient cycles. In this case, the combined NREL

- EPA transient cycle consists of the low speed transient directly followed by the high speed transient to form a bimodal test cycle.

Figure 26 : Comparative mileage accumulation for standard cycles o f interest 

### Looking at the unweighted combined drive cycle mileage accumulation results, the differences in high speed mileage accumulation between the cycles becomes apparent. None of the standard cycles examined accumulate mileage at speeds in excess of 6 0 mph, while the ETC FIGE is the only standard chassis test cycle which accumulates mileage at speeds in excess of 55 mph.

4.3.4 Comparison of Acceleration/Deceleration Behavior

A detailed analysis of the acceleration and deceleration behavior of both the repres entative transient cycles and the standard cycles was performed to benchmark both the NREL

- EPA transient cycles and the CARB HHDDT as it relates to slow speed urban cluster 1 identified in the segmentation analysis. Metrics describing the frequency (per mi le), and aggressiveness (average and max rates) were calculated and collected in Table 12.

Table 12 . Comparison of Cycle Acceleration Statistics

Cluster acceleration

events (1/mile) deceleration

events (1/mile) max_accel

(ft/s 2 ) max _decel

(ft/s 2 ) avg

accel (ft/s 2 ) avg

decel (ft/s 2 )

CARB HHDDT 10.52 10.17 4.25

- 4.06 0.94

- 1.24



## 0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.00 45.00 50.00 Percentage of Total Miles Accumulated 

# Comparative Mileage Accumulation Distributions

WHVC JPJE05 ETC FIGE EPA Combined Cycle

### 41

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. Cluster acceleration

events (1/mile) deceleration

events (1/mile) max_accel

(ft/s 2 ) max _decel

(ft/s 2 ) avg

accel (ft/s 2 ) avg

decel (ft/s 2 )

Transient

WHVC 13.28 13.10 3.84

- 4.99 0.71

- 0.81

JPJE05 9.50 9.38 5.22

- 6.01 1.03

- 1.08

ETC FIGE 11.95 11.73 12.58

- 13.21 0.63

- 0.72

EPA

- Transient High 2.64 2.64 2.51

- 4.57 0.52

- 0.60

NREL

- EPA Transient Low 6.26 6.26 5.34

- 5.66 1.41

- 1.34

FDNA 1

- Average 7.18 7.19 9.90

- 9.98 1.25

- 1.43



### Figures 30

- 35 illustrate the results stored in Table 12. Looking at the results, one can see that both the Fleet DNA low speed cluster data and NREL

- EPA representative cycles displayed fewer acceleration/deceleration events per mile. The low

- speed transient cluster data (FNDA 1) showed higher maximum accelerations than all cycles except for the ETC FIGE. The NREL

EP A representative cycle and the low speed cluster data show somewhat higher average acceleration and deceleration rates than all other cycles. Figures 30

- 35 also illustrate the close agreement over a wide range of metrics between the representative transien t cycles and Fleet DNA dataset.

Figure 27 : Comparison of standard cycle acceleration frequency 

### 0.00 

### 2.00 4.00 6.00 8.00 10.00 12.00 14.00

HHDDT Transient Segment JPJE05 ETC FIGE WHVC EPA Transient High EPA Transient
Low FDNA 1 - Medoid FDNA 1-

Average 

# Acceleration Events (1/mile)

### 42

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.

Figure 28 : Comparison of standard cycle deceleration frequency

Figure 29 : Comparison of standard cycle peak acceleration rates 

### 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00

HHDDT Transient Segment JPJE05 ETC FIGE WHVC EPA Transient High EPA Transient
Low FDNA 1 - Medoid FDNA 1-

Average 

# Deceleration Events (1/mile)

0.00 2.00 4.00 6.00 8.00 10.00 

### 12.00 14.00

HHDDT Transient Segment JPJE05 ETC FIGE WHVC EPA Transient High EPA Transient
Low FDNA 1 - Medoid FDNA 1-

Average 

# Maximum Acceleration (ft/s^2)

### 43

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.

Figure 30 : Comparison of standard cycle peak deceleration rates

Figure 31 : Comparison of standard cycle average acceleration rates 

### -14.00 -12.00 -10.00 -8.00 -6.00 -4.00 -2.00 0.00 HHDDT Transient Segment JPJE05 ETC FIGE WHVC EPA Transient High EPA Transient
Low FDNA 1 - Medoid FDNA 1-

Average 

# Maximum Deceleration (ft/2^s)

0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40 1.60

HHDDT Transient Segment JPJE05 ETC FIGE WHVC EPA Transient High EPA Transient
Low FDNA 1 - Medoid FDNA 1-

Average 

# Average Acceleration (ft/s^2)

### 44

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.

Figure 32 : Comparison of standard cycle average deceleration rates 

### -1.60 -1.40 -1.20 -1.00 

### -0.80 -0.60 -0.40 -0.20 0.00 HHDDT Transient Segment JPJE05 ETC FIGE WHVC EPA Transient High EPA Transient
Low FDNA 1 - Medoid FDNA 1-

Average 

# Average Deceleration (ft/s^2)

### 45

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. 

# 5 Development of Representative Drive Cycle Weights

Following a similar approach as was used to develop the representative transient cycles, the top 50 drive cycles from eac h of the 3 clusters from the cluster analysis as ranked by RMS distance from medoid were used to compute cluster averages for each of the drive cycle metrics examined in Section 4. These averages represented the typical drive cycle behavior captured by the cluster. Using these calculated average values, drive cycle weights for zero speed time (% of total cycle duration), percentage of total mileage accumulated below 55 mph, percentage of total mileage accumulated between 55 and 65 mph, and percentage of tot al mileage accumulated above 65 mph could be determined for each cluster in alignment with the existing proposed phase 2 GHG rule framework.



## 5.1 Comparative Drive Cycle Characterization

To explore the differences in vehicle behavior captured by each of the veh icle segments identified by the cluster analysis, a detailed drive cycle characterization was performed using DRIVE. The same metrics as used in the drive cycle cluster analysis were used to compare the different clusters and representative transients. Tab le 13 shows the results of the drive cycle characterization.

Table 13 . Comparison of Representative Cycle and Cluster Drive Cycle Metrics

Cluster Aerodynamic

Speed (ft/s) Characteristic

Acceleration (ft/s 2 ) Percent of

mileage accumulated below 55 mph Percentage

of Cycle Time at Zero Speed Number

of Stops Per Mile Average Driving Speed (mph) Maximum

Driving Speed (mph) Standard

Deviation of Driving Speed (mph)

FDNA 1

- Average 50.59 0.55 96.45 47.30 1.71 22.62 62.39 13.74

FDNA 2

- Average 69.14 0.46 64.64 52.40 0.76 33.64 66.85 19.25

FDNA 3

- Average 83.40 0.29 26.47 24.50 0.21 48.57 70.81 19.17

NREL

- EPA Transient Low 50.99 0.59 100.00 21.69 2.98 21.31 51.67 16.16

CARB HHDDT Transient 43.36 0.49 100.00 15.57 1.40 18.20 47.50 13.38

FDNA 1

- Medoid 48.88 1.22 97.85 47.87 1.22 24.12 62.61 13.03

### 46

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. 

### To further illustrate the differences in vehicle operating behavior and the three distinct operating modes defined by the cluster analysis (Section 3), data from Table 13 were graphed in Figures 36

– 38.

Figure 33 : Comparison of cluster and representative cycle zero speed time vs. mileage accumulated below 55 mph 

### Looking closer at Figure 36, one can see that there is a significant difference between the three clusters in mileage accumulation. We can see that cluster 3

- 1 accumulates nearly all of its mileage below 55 mph, while the cluster 3

- 2 accumulates roughly half of its mileage at 55 mph or below, and the cluster 3

- 3 accumulates approximately 30% of its mileage at speeds below 55 mph. It can also be seen that there is a significant difference in zero speed time between the representative transient test cycles and the low speed urban cluster (3

- 1). This difference in zero speed time can be adjusted thr ough the addition of extra zero time in a supplemental idle test, allowing the transient cycles to match the final zero speed time percentages of any of the three clusters. 0 

### 20 40 60 80 100 120

0 10 20 30 40 50 60 70 % of Distance Accumulated Below 55 mph



### % of Time Accumulated at Zero Speed 

# Zero Speed Time (%) vs Distance Accumulated

Below 55 mph

Cluster 3-1 Cluster 3-2 Cluster 3-3 EPA Transient Low HHDDT Transient

### 47

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. Figure 34 : Comparison of cluster and representative cycle average driving speed and kinetic intensity 

### Similarly, illustrated in Figure 37 is the distinct separation of the drive cycles when looking at Kinetic Intensity and average driving speed. We can see that cluster 3

- 1 is centered around 20 mph average driving speed, 3

- 2 around 35 mph, and cluster 3

- 3 at 50 mph. These represent urban driving, mixed driving, and highway driving cases. The HHDDT cycle is similar to the NREL

EPA transient low speed cycle with a slightly higher Kinetic Intensity (1.4) and sl ightly lower average driving speed (18 mph). 

### 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 

### 2

0 10 20 30 40 50 60 Kinetic Intensity (1/mile)



### Average Driving Speed (mph) 

# Average Driving Speed vs. Kinetic Intensity

Cluster 3-1 Cluster 3-2 Cluster 3-3 EPA Transient Low HHDDT Transient

### 48

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. Figure 35 : Comparison of cluster and representative cycle standard deviation of speed and stops per mile 

### Shown in Figure 38 are a comparison of stops per mile and standard deviation of driving speed. These two metrics combine to describe how much transient driving behavior a given drive cycle possesses. Higher values for stops per mile are indicative of congested urban driving with frequent stops in traffic and at stop lights/signs. Low er values for stops per mile are indicative of constant speed cruising drive cycles reflective of highway or rural driving behavior. Standard deviation of speed when coupled with stops per mile helps describe the relative variation within a daily drive cyc le. Cycles possessing high standard deviation of speed show high levels of variation, or significant transient behavior. Lower standard deviations of speed reflect more constant driving behavior as would be displayed on highway or rural drive cycles. Compa ring the two clusters with a low number of stops per mile (clusters 3

- 2 and 3

- 3), we can see that they display similar standard deviations of speed. This means that the two clusters possess similar driving behavior in terms of transient operation, with the primary difference between the clusters in this case being driving speed and zero speed time.



## 5.2 Analysis of Mileage Accumulation

As part of the drive cycle analysis, a detailed characterization of mileage accumulation was performed to further aid in underst and the driving behavior represented by each of the representative cycles and identified clusters. The mileage accumulation for each of the drive cycles e xamined can be found in Table 14 and Table 15 , where mileage is accumulated in 5 mph speed bin increme nts as a percentage of total miles traveled over the cycle

. By splitting the data into 5 mph speed bins, researchers were able to examine transient speed mileage accumulation 

### 0 0.5 1 1.5 2 2.5 3 3.5 4 

### 4.5

10 12 14 16 18 20 22 24 Stops Per Mile



### Standard Deviation of Speed (mph) 

# Average Driving Speed vs. Kinetic Intensity

Cluster 3-1 Cluster 3-2 Cluster 3-3 EPA Transient Low HHDDT Transient

### 49

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. 

### (<45 mph), rural (45

- 55 mph), and highway type (55 mph) driving behavior at a fin er level of detail than could be described by any single high level drive cycle metric.

Table 14 . Sub 45 mph Mileage Accumulation Comparison for Cluster and Representative Cycles

Cluster 0+

- 5

mph distance (%) 5+

- 10

mph distance (%) 10+

- 15

mph distance (%) 15+

- 20

mph distance (%) 20+

- 25

mph distance (%) 25+

- 30

mph distance (%) 30+

- 35

mph distance (%) 35+

- 40

mph distance (%) 40+

- 45

mph distance (%)

FDNA 1

- Average 1.39 4.35 7.11 8.90 11.05 12.95 15.15 14.06 10.69

FDNA 2

- Average 0.68 1.98 2.74 3.36 4.46 6.06 7.54 8.75 9.05

FDNA 3

– Average 0.23 0.57 0.81 1.03 1.39 2.02 2.74 3.10 3.82

NREL

- EPA Transient Low 2.66 4.40 5.42 7.08 8.37 8.30 15.15 18.69 12.55

CARB HHDDT Transient 1.25 12.72 3.97 9.02 18.20 10.20 15.72 11.96 8.29

FDNA 1

– Medoid 0.87 3.60 5.94 8.37 10.96 14.02 17.95 15.61 11.69

Table 15 . 45+ mph Mileage Accumulation Comparison for Clusters and Representative Cycles

Cluster 45+

- 50 mph

distance (%) 50+

- 55 mph

distance (%) 55+

- 60 mph

distance (%) 60+

- 65 mph

distance (%) 65+

- 70 mph

distance (%) 70+

- 75 mph

distance (%)

FDNA 1

- Average 6.57 4.22 2.52 1.00 0.04 0.00

FDNA 2

- Average 9.00 11.00 17.95 14.28 3.09 0.04

FDNA 3

- Average 4.27 6.49 17.57 44.90 10.88 0.16

NREL

- EPA Transient Low 12.75 4.64 0.00 0.00 0.00 0.00

CARB HHDDT Transient 8.66 0.00 0.00 0.00 0.00 0.00

FDNA 1

– Medoid 5.41 3.44 1.70 0.45 0 .00 0 .00

### 50

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. Figure 36 : Comparison of cluster and representative cycle mileage accumulation



## 5.3 Weighting of Clusters by Mileage Accumulated in Broad Speed Categories

Table 16 . Final Weights for Identified Clusters

Cluster Percent of mileage

accumulated below 55 mph Percentage of mileage accumulated between 55 and 65 mph Percentage of mileage accumulated above 65 mph Percentage of Cycle Time at Zero Speed

FDNA 1

- Average 96.45 3.51 0.04 47.30

FDNA 2

- Average 64.64 32.23 3.13 52.40

FDNA 3

- Average 26.47 62.47 11.06 24.50

FDNA 1

- Med o i d 97.85 2.15 0.00 47.87



### 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00 

### 20.00 Percentage of Total Mileage Accumulated 

# Comparative Mileage Accumulation Distributions

FDNA 1 - Average EPA Transient Low FDNA 1 - Medoid

### 51

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. 

## 5.4 Characterization of Acceleration/Deceleration Behavior

A detailed analysis of the acceleration and deceleration behavior of both the representative low speed transient cycle and t he CARB HHDDT was performed to compare the behavior of the two transi ent cycles to the clusters identified in the segmentation analysis. Metrics describing the frequency (per mile), and aggressiveness (average and max imum rates) were calculated and displayed in Table 1 7

.

Table 17 : Comparison of Acceleration Behavior for Cl usters and Representative Cycle

Cluster Acceleration

events (1/mile) Deceleration

events (1/mile) Max_accel

(ft/s 2 ) Max _decel

(ft/ s 2 ) Avg

accel (ft/s 2 ) Avg

decel (ft/s 2 )

FDNA 1

– Average 7.18 7.19 9.90

- 9.98 1.25

- 1.43

FDNA 2

– Average 5.09 5.09 8.97

- 10.28 1.01

- 1.13

FDNA 3

- Average 4.93 4.93 10.74

- 8.88 0.58

- 0.62

NREL

- EPA Transient Low 6.26 6.26 5.34

- 5.66 1.41

- 1.34

CARB HHDDT Transient 10.52 10.17 4.25

- 4.06 0.94

- 1.24

FDNA 1

- Medoid 6.37 6.37 9.01

- 8.80 1.18

- 1.30



### Table 17 results have been visualized in Figures 40 through 45.

### 52

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. Figure 37 : Comparison of cluster and representative cycle acceleration frequency

Figure 38 : Comparison of cluster and representative cycle deceleration frequency 

### 0.00 2.00 4.00 6.00 8.00 10.00 12.00

FDNA 1 FDNA 2 FDNA 3 EPA Transient
Low HHDDT

Transient Segment FDNA 1 -

Medoid 

# Acceleration Events (1/mile)

0.00 2.00 4.00 6.00 8.00 10.00 

### 12.00

FDNA 1 FDNA 2 FDNA 3 EPA Transient
Low HHDDT

Transient Segment FDNA 1 -

Medoid 

# Deceleration Events (1/mile)

### 53

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. Figure 39 : Comparison of cluster and representative cycle peak acceleration rates

Figure 40 : Comparison of cluster and representative cycle peak deceleration rates 

### 0.00 2.00 4.00 6.00 8.00 10.00 12.00

FDNA 1 FDNA 2 FDNA 3 EPA Transient
Low HHDDT

Transient Segment FDNA 1 -

Medoid 

# Maximum Acceleration (ft/s^2)

-12.00 -10.00 -8.00 -6.00 -4.00 -2.00 0.00 FDNA 1 FDNA 2 FDNA 3 EPA Transient
Low HHDDT

Transient Segment FDNA 1 -

Medoid 

# Maximum Deceleration (ft/2^s)

### 54

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. Figu re 41 : Comparison of cluster and representative cycle average acceleration rates

Figure 42 : Comparison of cluster and representative cycle average deceleration rates 

### 0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40 1.60

FDNA 1 FDNA 2 FDNA 3 EPA Transient
Low HHDDT

Transient Segment FDNA 1 -

Medoid 

# Average Acceleration (ft/s^2)

-1.60 -1.40 -1.20 -1.00 -0.80 -0.60 -0.40 -0.20 0.00 FDNA 1 FDNA 2 FDNA 3 EPA Transient
Low HHDDT

Transient Segment FDNA 1 -

Medoid 

# Average Deceleration (ft/s^2)

### 55

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. 

# 6 Known Limitations and Potential for Future Work

As mentioned in previous reports, the need for coupled road grade and drive cycle information is an area of ongoing interest that provides the opportunity for future development and refinement of both the high speed transient and constant high speed cruise drive cycle components. In addition, exploring the opportunity for simplified testing through the development of a dual mode test cycle similar to the one shown in Figure 46 to replace the existing 3 component test procedure is a topic of futu re interest which could result is faster less costly certification.

Figure 43 : Sample potential dual mode test cycle 

### 0 10 20 30 40 50 60 70 80

1 73 145 217 289 361 433 505 577 649 721 793 865 937 1009 1081 1153 1225 1297 1369 1441 1513 1585 1657 1729 1801 1873 Speed (mph)



### Time (s) 

# NREL

- EPA Combined Drive Dual Mode Test Cycle

### 56

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. 

# References

[1] U.S. Environmental Protection Agency. (2011). “EPA and NHTSA Adopt First

- Ever Program to Reduce Greenhouse Gas Emissions and Improve Fuel Efficiency of Medium

and Heavy

- Duty Vehicles.” EPA

- 420

- F

- 11

- 031. Office of Transportation and Air Quality. Accessed F ebruary 6, 2016. http://www.epa.gov/otaq/climate/documents/420f11031.pdf

[2] U.S. Environmental Protection Agency. (2011). “Final Rulemaking to Establish Greenhouse Gas Emission Standards and Fuel Efficiency Standards to Medium

- and Heavy

- Duty Engines and Vehicles: Regulatory Impact Analysis.” EPA

- 420

- R

- 11

- 901. Office of Transportation and Air Quality and National Highway Traffic Safety Administration. Accessed January 13, 2016. http://www.epa.gov/otaq/climate/documents/420r11901.pdf

[3] Federal Register. (2011). “Greenhouse Gas Emissions Standards and Fuel Efficiency Standards for Medium

- and Heavy

- Duty Engines and Vehicle s: Final Rule.” Environmental Protection Agency and Department of Transportation. (76:179); Accessed February 16, 2016. http://www.gpo.gov/fdsys/pkg/FR

- 2011

- 09

- 15/pdf/2011

- 20740. pdf

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# Appendix A: Standard Fleet DNA Data Channels

When performing data collection through either manual vehicle instrumentation or automated data transfer via installed onboard v ehicle telemetric devices, NREL researchers collect a standard baseline of vehicle operating data for analysis and storage in the Fleet DNA database. The specific channels chosen for data collection have been developed with a focus on characterization of v ehicle drive cycle and engine/drivetrain operation. Shown in Table 18 is an example of the standard Global Positioning Satellite (GPS) channels collected and stored in Fleet DNA.

Table 18

. Example GPS File



### Of particular interest are the latitude, longitude, elevation, speed, and time channels. Latitude and Longitude information can be coupled with elevation data and additional datasets stored in Fleet DNA such as the Tom Tom National Road Network database to provide Geospatial con text for route analysis. GPS and time channels provide the requisite source data necessary for drive cycle characterization and vehicle idle time analyses.

To enable deeper analysis of the influence of drive cycles on vehicle fuel consumption, performance, and emissions, a number of additional data channels publically product over the vehicle Controller Area Network (CAN) bus according to SAE J1939 standards have been identified and are collected at 1Hz in conjunction to standard GPS telematics data. Table 19 details the list of basic vehicle engine, powertrain, and emissions channels collected and stored in the Fleet DNA database.

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Table 19

. List of Additional Logged Controller Area Network Channels

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# Appendix B: Determining Engin e to Vehicle Speed Ratios via Kernel Density Estimation

Given the presence of limited vehicle chassis specification information stored in the Fleet DNA database, NREL researchers developed a novel kernel density estimator method to infer vehicle gear ratios using existing captured 1

- Hz Fleet DNA data such as in stantaneous vehicle and engine speed. Starting with filtered raw vehicle speed and engine data as shown in Figure 47, the first step in the process of identifying individual operating gears is to combine the two individual metrics into a single variable by dividing vehicle speed in mph by engine speed in rotations per minute to compute a ratio of vehicle speed to engine speed.

Figure 44 : Sample heat plot of vehicle speed vs. engine operating speed



### Shown in Figure 48 are the resul ts of this transformation. Visually inspecting the results, it is apparent to see that there are a series of common peaks at which the ratio of vehicle speed to engine speed is most frequently observed in the dataset.

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. Figure 45 : Single vehicle sample distribution of vehi cle speed to engine speed ratio data 

### Higher vehicle speed to engine speed ratios are easier to both visualize and statistically separate, as the noise (time spent operating between gears) to signal (time spent oper ating in a given gear) ratio is much smaller at these high vehicle speeds.

Having calculated vehicle speed to engine speed ratios, a kernel density estimator can be applied to the collection of ratios to convert the histogram show in Figure 48 from a discr ete set of binned data into a continuous function as shown in Figure 49.

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. Figure 46 : Single vehicle sample distribution of vehicle speed to engine speed ratio data with peaks identified



### Once the set of ratios have been converted to a continuous function, application of any number of peak detections algorithms can be performed to produce estimated vehicle speed to engine speed ratios.

Once the gear ratios have been identified, plotting linear fit curves using the identified ratios for slope allows researchers to visually validate that appropriate ratios have been selected. This process is shown in Figure 50.

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. Figure 47 : Identified gear ratios plotted atop sample vehicle speed vs. engine operating speed dat a

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# Appendix C: Comparison of Fleet DNA and MOVES Resampled Distributions by Cluster

Figure 48 : Comparison of multidimensional RMS Distance distributions for full fleet DNA Dataset and MOVES resampled population for Cluster 1 

## 0.00% 20.00% 40.00% 

## 60.00% 80.00% 100.00% 120.00%

0 

## 50 100 150 200 

## 250 300 350 400 450 500

0 6 12 18 24 30 36 42 48 54 60 66 72 78 84 90 Number fo Drive Cycles



## Distance from Mediod 

# Cluster 1 Medoid Distance Distribution

Frequence - FDNA Frequency - MOVES

Cumulative % - FDNA Cumulative % - MOVES

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. Figure 49 : Comparison of multidimensional RMS distance distributions for full Fleet DNA dataset and MOVES resampled population for Cluster 2 

## 0.00% 20.00% 40.00% 60.00% 80.00% 100.00% 120.00%

0 50 100 

## 150 200 250 300 350

0 6 12 18 24 30 36 42 48 54 60 66 72 78 84 90 Number fo Drive Cycles



## Distance from Mediod 

# Cluster 2 Medoid Distance Distribution

Frequence - FDNA Frequency - MOVES

Cumulative % - FDNA Cumulative % - MOVES

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. Figure 50 : Comparison of multidimensional RMS distance distributions for full Fleet DNA dataset and MOVES resampled population for Cluster 3 

## 0.00% 20.00% 40.00% 60.00% 80.00% 100.00% 120.00%



## 0 50 100 150 200 250 300

0 6 12 18 24 30 36 42 48 54 60 66 72 78 84 90 Number fo Drive Cycles



## Distance from Mediod 

# Cluster 3 Medoid Distance Distribution

Frequence - FDNA Frequency - MOVES

Cumulative % - FDNA Cumulative % - MOVES