representative midwestern us cycles: synthesis and

This paper is a part of the hereunder thematic dossierpublished in OGST Journal, Vol. 68, No. 1, pp. 3-178

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3 > Editorial

13 > Analysis and Experimental Implementation of a Heuristic Strategyfor Onboard Energy Management of a Hybrid Solar VehicleAnalyse et expérimentation d’une stratégie heuristique pour la gestiond’énergie à bord d’un véhicule hybride solaireG. Coraggio, C. Pisanti, G. Rizzo and M. Sorrentino

23 > Open Issues in Supervisory Control of Hybrid Electric Vehicles:A Unified Approach Using Optimal Control MethodsQuestions ouvertes sur la supervision énergétique des véhiculeshybrides électriques : une approche unifiée par la théorie de lacommande optimaleL. Serrao, A. Sciarretta, O. Grondin, A. Chasse, Y. Creff, D. Di Domenico,P. Pognant-Gros, C. Querel and L. Thibault

35 > Optimization of Hybrid Power Trains by Mechanistic SystemSimulationsOptimisation de groupes motopropulseurs électriques hybrides parsimulation du système mécaniqueT. Katrašnik and J.C. Wurzenberger

51 > A Phenomenological Heat Transfer Model of SI Engines – Applicationto the Simulation of a Full-Hybrid VehicleUn modèle phénoménologique de transfert thermique au sein demoteurs à allumage commandé – Application à la simulationd’un véhicule full-hybrideR. Dubouil, J.-F. Hetet and A. Maiboom

65 > Battery Electric Vehicle (BEV) or Range Extended Electric Vehicle(REEV)? – Deciding Between Different Alternative Drives Based onMeasured Individual Operational ProfilesVéhicule électrique à batteries (BEV) ou véhicule électrique àprolongateur d’autonomie (REEV) ? – Choisir entre différentsentraînements alternatifs sur la base de profils opérationnelsindividuels mesurésS. Marker, B. Rippel, P. Waldowski, A. Schulz and V. Schindler

79 > Assessment by Simulation of Benefi ts of New HEV PowertrainConfigurationsÉvaluation par simulation des bénéfi ces de nouvelles chaînesde traction hybridesN. Kim and A. Rousseau

95 > Dual Mode Vehicle with In-Wheel Motor: Regenerative BrakingOptimizationVéhicule bi-mode avec moteurs roues : optimisation du freinagerécupératifG. Le Solliec, A. Chasse, J. Van-Frank and D. Walser

109 > Engine Downsizing and Electric Hybridization Under Considerationof Cost and DrivabilityRéduction de taille moteur et hybridation électrique avecconsidérations de coût et de performance de conduiteS. Ebbesen, P. Elbert and L. Guzzella

117 > Representative Midwestern US Cycles: Synthesis and ApplicationsCycles représentatifs du Middle West américain : synthèse etapplicationsT.-K. Lee and Z.S. Filipi

127 > A Review of Approaches for the Design of Li-Ion BMS EstimationFunctionsRevue de différentes approches pour l’estimation de l’état decharge de batteries Li-ionD. Di Domenico, Y. Creff, E. Prada, P. Duchêne, J. Bernard andV. Sauvant-Moynot

137 > Experimental Assessment of Battery Cycle Life Within theSIMSTOCK Research ProgramÉvaluation expérimentale de la durée de vie de la batterie dansle programme de recherche SIMSTOCKP. Gyan, P. Aubret, J. Hafsaoui, F. Sellier, S. Bourlot, S. Zinola and F. Badin

149 > Smart Battery Thermal Management for PHEV EfficiencyUne gestion avancée de la thermique de la batterie basse tensionde traction pour optimiser l’efficacité d’un véhicule hybrideélectrique rechargeableL. Lefebvre

165 > Parameterization and Observability Analysis of Scalable BatteryClusters for Onboard Thermal ManagementParamétrage et analyse d’observabilité de clusters de batteriesde taille variable pour une gestion thermique embarquéeXinfan Lin, Huan Fu, Hector E. Perez, Jason B. Siege, Anna G. Stefanopoulou,Yi Ding and Matthew P. Castanier

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Representative Midwestern US Cycles:Synthesis and Applications

T.-K. Lee1* and Z.S. Filipi2

1 Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109 - USA2 Department of Automotive Engineering, Clemson University, Greenville, SC 29607-5257 - USA

e-mail: [email protected] - [email protected]

* Corresponding author

Résumé — Cycles représentatifs du Middle West américain : synthèse et applications — Cet articlepropose un ensemble de cycles de conduite représentatifs du monde réel dans le Middle West américain,aptes à reproduire la dépendance des modes de conduite à la distance parcourue. Des analyses récentes dela conduite aux Etats-Unis montrent que la plupart des cycles de certification mènent à une sous-estimation de la consommation d’énergie par mile parcouru par rapport aux habitudes de conduite.La conduite dans le monde réel est un mix de conduite locale et de conduite sur autoroutes. De plus, leshabitudes de conduite montrent une forte dépendance à la distance parcourue. Pour couvrir la vastegamme de distances parcourues dans le monde réel, cinq cycles synthétiques ont été générés, allant de4,78 miles à 40,71 miles, conformément à la répartition des distances parcourues dans le monde réel.Chaque cycle individuel est construit par un processus stochastique utilisant les informations de conduiteextraites de données de trajets dans le Middle West américain. Lors de la construction de l’ensemble descycles, les critères statistiques de validation de la représentativité des cycles sont traités afin de pouvoirreproduire la dépendance à la distance et d’éliminer les variations aléatoires. Les cycles synthétisés sontensuite utilisés pour des études de conception et de contrôle de véhicules hybrides électriques ouélectriques rechargeables afin d’évaluer l’impact des véhicules électrifiés sur le réseau.

Abstract — Representative Midwestern US Cycles: Synthesis and Applications — This paper proposeda set of representative real-world driving cycles in Midwestern US, which are capable of capturing thedependence of driving patterns on driving distance. Recent analyses of the real-world driving in USAshow that most of certification cycles lead to underestimation of energy consumption per mile comparedto the naturalistic driving patterns. Real-world driving is a mix of local driving and highway driving.Furthermore, the driving patterns show high dependency on the driving distance. To cover the widerange of real-world driving distances, five synthetic cycles are generated ranging from 4.78 miles to40.71 miles following the real-world driving distance distribution. Each individual cycle is constructedby a stochastic process using the extracted driving information from the naturalistic trip data in theMidwestern US. While constructing the cycle set, the statistical criteria for validating the cyclerepresentativeness are processed to capture the clear distance dependency and remove randomvariations. The synthesized cycles are subsequently used for Plug-in Hybrid Electric Vehicle (PHEVs) orHybrid Electric Vehicle (HEVs) design and control studies for the assessment of the impact of electrifiedvehicles on the grid.

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INTRODUCTION

Strict regulations on fuel economy of vehicles and greenhousegas emission reduction put strong emphasis on the develop-ment of Hybrid Electric Vehicles (HEVs) and Plug-in HybridElectric Vehicles (PHEVs). The regulations mandate the120 g CO2/km in the European Union and the 35.5 mpg(mile par gallon) fleet fuel economy by 2016 in US. Hybridpropulsion system allows exceptional fuel economy improve-ments through flexible use of multiple power sources onboard of a vehicle, engine shut-downs and recuperation ofbraking energy. Hence, the usage of stored electricity forvehicle propulsion in PHEVs presents promising ways forreducing the dependency on petroleum in the transportationsector and for facilitating future growth of renewable energysources on the power grid.

Design optimization and supervisory control strategy arekey elements in developing HEVs and PHEVs to obtain thefull benefit of vehicle electrification. Vehicle design shouldbe determined to satisfy the driving performance underreal-world driving and vehicle control strategy should bedeveloped to maximize the vehicle hardware potential. Sinceoverall vehicle performance specification and hardwaredesign are determined under applied driving cycles, real-world driving patterns must be considered in the very initialdevelopment stage to achieve the better fuel economy andperformance.

Until present, certification driving cycles have beenpredominantly used to assess vehicle performance and fueleconomy (Carlson et al., 2009; Duoba et al. 2009). Thecycles include UDDS (Urban Dynamometer DrivingSchedule) (Kruse and Huls, 1973) and HWFET (HighwayFuel Economy Test) cycles. New European Driving Cycle(NEDC) is typically used by European researchers. However,measured naturalistic driving cycles show a wide spectrumsof driving patterns. Naturalistic cycles tend to be more aggres-sive than certification cycles (Patil et al., 2009). The discrep-ancy between certification cycles and real-world driving cyclestends to become larger with increased trip length. Thus, drivingcycles play a critical role to obtain more realistic and reliablevehicle analysis and optimization results (Fellah et al., 2009;Kwon et al., 2008). In case of PHEVs, driving cycles are evenmore important since electric driving ranges, such as “AllElectric Range (AER)” or “Mostly Electric Range (MER)”,are directly influenced by driving patterns. Thus, capturingfeatures of realistic driving patterns with a set of representa-tive real-world driving cycles is indispensable for in-depthanalysis of vehicle design and control strategy development.

Real-world driving patterns have strong dependency ontrip distance. For instance, vehicles are not normally drivenin low-speed city conditions for 30 or 40 miles and longcommutes typically involve a portion of higher speed free-way driving. Thus, the information about real-world drivingand its integration into vehicle analysis is indispensable for

the large scale life-cycle analysis of energy use in transportationand the impact of the power-generation mix on the green-house gas emission. To represent real-world driving patternsin Europe, ARTEMIS European driving cycles were devel-oped (André, 2004). The ARTEMIS cycles were composedby assembling adequately classified segments out of the data-base collected during actual driving of European cars and bysubsequent representativeness validation process. An alterna-tive approach that utilizes Markov chain and TransitionProbability Matrices (TPMs) augmented by statistical analysisfor validating representativeness was recently proposed byLee and Filipi (2010, 2011a).

This paper proposes a procedure to synthesize a set ofrepresentative real-world driving cycles and its applications.The proposed cycles are capable of capturing the dependencyof driving patterns on driving distance based on the method-ology. The trip distance dependency is captured from theextracted driving pattern information in each divided seg-ment on daily driving distance distribution. To synthesizedriving cycles, Markov chain is used with its capability ofrepresenting naturalistic driving information in a compactform as proposed in Lee and Filipi (2011a). The proposedmethodology has a unique flexibility in constructing arbitrarydistance cycles with desired driving characteristics.Furthermore, the resulting synthetic cycles are general andindependent of vehicle types and vehicle control strategy,since the proposed approach uses only velocity and accelera-tion data, i.e. it does not include vehicle related information orsubjective parameters while synthesizing schedules.

In the present paper, real-world driving data are analysedand driving distance distribution is modeled first. Then,driving cycle synthesis procedure is described. MidwesternUS driving cycles, typical of urban/suburban driving in aMidwestern US region are proposed and analysed. A responsesurface approach is then introduced to assess the impact ofa large fleet PHEV on the grid in the application section.Finally, this paper ends up with conclusions.

1 DRIVING DATA IN MIDWESTERN US

Real-world driving data in Southeast Michigan collected bythe University of Michigan Transportation Research Institute(UMTRI) by Field Operational Test (FOT) (LeBlanc et al.,2006) are used to analyze naturalistic driving patterns in theMidwest US area. Total 830 days 4 409 trips were used forthe analysis of real-world driving patterns. The data includesdriving information sufficient for representing real-worlddriving patterns with respect to trip distance. Daily drivingdistance distribution is shown in Figure 1. Daily drivingdistance is a summation of trip lengths during one day.

Driving distance distribution is regressed to find asmoothed Probability Density Function (pdf) with the purposeof dividing driving data into several segments with the same

ogst120054_TKLee 19/04/13 10:39 Page 118

T.-K. Lee and Z.S. Filipi / Representative Midwestern US Cycles: Synthesis and Applications 119

probability depending on driving distance. Since the distribu-tion is skewed as show in Figure 1, a Chi-square distribution(χ2-distribution) is used for the regression model. TheChi-square distribution is expressed as:

(1)

where Γ(.) is the Gamma function defined as:

Γ(z) = ∫0∞ tz–1e–1dt (2)

xn is the normalized driving distances defined as x/Δd, x is thedeparture time, Δd is the reference discretized step of thedriving distance corresponding to the histogram and v isdetermined to minimize the root mean square (rms) error of theresponse variable. The regressed function shows a smoothedcurve fit to the raw data distribution.The probability distributionsatisfies:

(3)

Figure 2 shows the regressed pdf and the CumulativeDistribution Function (cdf) of one-day driving distances.Driving distance dependent driving patterns are captured fromthe driving cycle data divided into ten segments having thesame probability on the cdf. Representative driving distancein each segment is selected as the mean value of the segmentrange. The selected one-way trip distances range from 4.78 to40.71 miles.

While synthesizing cycles, driving pattern informationis extracted from each segment. Initially, ten independentcycles are constructed. Then, five cycles, marked as solidcircles, are selected to be members of a representative set,

P x dxn n( )∫ = 1

P xx e

vnn

v x

v

n

( )( / )

( )/ /

/=

− −2 2 2

22 2Γ

capable of capturing driving features as a function of triplength. The synthesis procedure is presented in Section 2 andthe resulting cycles are proposed in Section 3.

2 CYCLE SYNTHESIS PROCEDURE

Generalized real-world driving patterns include both local tripsand free-way trips. Driving patterns are different with respectto driving distances. Thus, the driving distance based catego-rization (Lee and Filipi, 2011a) is used to synthesize SoutheastMichigan Urban/Suburban driving cycles in this paper.

The overall procedure is illustrated in Figure 3. A stochasticprocess combined with subsequent assessment procedurecan construct driving cycles with verified representative-ness (Lee and Filipi, 2010). Initially, naturalistic drivingcycles for the extraction of real-world driving informationare selected within each concerning segment. Drivinginformation is extracted in a form of velocity and accelera-tion matrices (see Fig. 4). The matrices relate currentvelocity and acceleration to future information. Everycurrent state is mapped to the states in the next time step(i.e., future time step) one-to-one. Markov chain uses theinformation to synthesize the cycles.

In this paper, a discrete-time Markov chain is used and itis a sequence of random variables X1, X2, X3, etc. with theMarkov property are expressed as:

(4)P X x X x X x X x

P X x

n n n n

n

( , , , )

(

+ +

+

= = = … =

= =

1 1 1 1 2 2

1

nn n nX x+ =1 )

10 20 30 40 50 60 700

Nor

mal

ized

dis

trib

utio

n

0.25

0

0.15

0.10

0.05

0.20

Distance (mile)Δd

Daily driving (one-way trips)distance distribution

P(xn): regressed probabilitydensity function

Nor

mal

ized

dis

trib

utio

n

0.25

0 0

0.05

0.10

0.15

0.20

Cum

ulat

ive

dist

ribut

ion0.8

1.0

0.6

0.4

0.2

Distance (mile)

Daily driving (one-way trips) distance distribution

60 700 5040302010

Selected representativeone-way trip distances

Figure 1

Daily driving distance distribution.

Figure 2

Statistical distribution of daily driving distances: probabilitydensity function, cumulative density function and selectedrepresentative one-way trip distances.

ogst120054_TKLee 19/04/13 10:39 Page 119


The set of possible values that the random variables Xncan take is the state space of the chain. The conditionalprobabilities pij = P(Xn+1 = j⏐Xn = i) are transition probabilities.The probability used in the synthesis procedure is time-independent (or time-homogeneous).The sum of all probabilitiesleaving a state must satisfy:

(5)

To satisfy the Markov property in Equation (4) thatrepresents future states depend only on the present states, anadequate number of states should be chosen. The requiredstates are selected by investigating the simplified vehicledynamics equation. The vehicle dynamics can be expressedby velocity and acceleration and they are chosen as the statesfor the Markov chain. The TPM is then generated in the formof a two dimensional matrix with velocity and acceleration atcurrent time tk. The velocity and acceleration are discretizedwith the number of M and N, respectively. The number ofevents at the next time step tk+1 is counted at each currentvelocity vk and current acceleration ak at the present time steptk, then divided by the total number of event to construct theprobability matrix. Then, the conditional probability isexpressed as:

(6)

where i and p = 1, 2, …, M, and j and q = 1, 2, …, N, and theoverall TPM structure is shown in Figure 4.

P P v v a a v v a ai j k p q k k i k j k p k, , | , , ( , | ,+ + += = = = =1 1 1 qq )

p P X j X iij

j

n n

j

∑ ∑= = = =+( | )1 1

The representativeness of synthesized cycles is verified byinvestigating statistically significant criteria. The statisticalcriteria are determined through generalized linear regressionanalysis in Lee and Filipi (2011a) and briefly described asfollows. Initially, a total number of 27 possible explanatoryvariables are identified and categorized into velocity related,acceleration related, driving-time and distance-related, andevent related variables. Through the assessment of the inter-relationship between two variables, one of them is droppedout. Then, 16 variables remain as initial explanatory variablesfor the regression analysis. Next, generalized linear regres-sion analysis is used to find the least number of significantvariables. The analysis includes three assessment stepsincluding t-test, normal probability plots of the residuals andhistograms of the residuals. The least significant variablesare dropped one by one, as long as the reduced equation canrepresent the response variable with sufficient accuracy.The final regression equations use statistically significantvariables to establish bases for subsequent assessments ofthe representativeness of synthesized driving cycles. Thesignificant explanatory variables are:– standard deviation of velocity (mph);– mean positive acceleration (m/s2);– standard deviation of acceleration (m/s2);– percentage of driving time under positive acceleration (%);– percentage of driving time under negative acceleration (%);– mean positive velocity (mph);– percentage of idle time (%);– number of stops/mile (1/mile).

Extract probability matrices

Resulting synthetic cycle

Synthesize cycles using Markov chainPr (vk+1 = v2, ak+1 = a2 I vk = v1, ak = a1)

Yes

No

Select naturalistic driving schedules(by distance or by patterns)

1) Driving distance2) Statistical criteria

Figure 3

Naturalistic driving cycle synthesis procedure using Markovchain and statistical criteria.

Extract transition probability matrices

Velocity data Acceleration data

Velocity at tk (mph)

Velocity at tk+1

Transfer probability matrix(v = vi and a = aj at t = tk)

Acc

eler

atio

n at

t k (m

/s2 )

Acc

eler

atio

n at

t k+

1

a1

v1 ... ...[ ]

vi –1

Pi, j –2, k +1| i , j –2, k

Pi, j , k +1| i , j , k

...

...Pi, j +2, k +1| i , j +2, k

vi –1 vi +1vi

vi +1 vM–1 vM

[ ][ ]

vi

[ ] [ ] [ ] [ ]

a2

..................

...

...

...

...

...

[ ] [ ][ ] [ ] [ ] [ ] [ ]

aj –1 [ ] [ ][ ] [ ] [ ] [ ] [ ]

aj [ ] [ ] P[ ] [ ] [ ] [ ]

aj +1

aj –2

aj –1

aj

aj +1

aj +2

[ ] [ ][ ] [ ] [ ] [ ] [ ]

...aN –1 [ ] [ ][ ] [ ] [ ] [ ] [ ]

aN [ ] [ ][ ] [ ] [ ] [ ] [ ]

v2

Figure 4

Illustration of the procedure to extract Transition ProbabilityMatrix (TPM) from real-world driving data.

ogst120054_TKLee 19/04/13 10:39 Page 120


3 MIDWESTERN US DRIVING CYCLES

Five cycles are selected to cover the naturalistic driving rangeand to capture most of naturalistic driving patterns with thedriving distance dependency. Figure 5 shows the full set ofUrban/Suburban driving cycles typical for the Midwestern US.Each cycle shows different driving patterns. The short dis-tance cycles show more frequent starts and stops, lowervelocity and higher acceleration. When the driving distancebecomes greater, longer segments with high speed are morefrequent.

3.1 Visual Assessment

Synthesized driving cycles show clear difference with respectto the driving distance. The shortest cycle (driving distanceof 4.87 miles) is the mildest one having the lowest maximumvelocity no higher than 53 mph and the most frequent stops.It does not include freeway driving patterns at all over theentire cycles. The longer driving distance, the higher thevelocity events start to appear. At the 10.6 mile cycle, veloc-ity profiles become moderately high but still below 65 mph.It is well corresponded to the speed limits (40 ~ 55 mph) oflocal road driving in Southeast Michigan area without severetraffic jam.

Freeway driving patterns become prevalent from mediumdistance cycles (from 15.5 miles driving distance). At the25.2 mile cycle, the duration of a continuous freeway drivingevent is up to 500 seconds and the maximum velocity is up to80 mph (see Fig. 5d). The medium distance cycles includeseveral local way driving patterns shown in 4.87 mile cyclewhile showing freeway patterns. In the longest cycle(40.9 miles), the continuous freeway driving event becomeseven longer up to 800 seconds. However, the highest speedis mostly maintained below 80 mph owing to the freewayspeed limitation (70 mph at freeway, Michigan, US). Thecycle includes local way patterns with frequent stops(1 950 ~ 2 500 s in Fig. 5e) and without frequent stops(0 ~ 700 s in Fig. 5e).

3.2 Trends of Statistical Parameters

Statistically significant parameters of the proposed cycleshave clear trends with respect to driving distance. Tables 1 to5 show statistical parameters and their comparisons betweenaveraged real-world data and synthetic cycles. The values ofparameters from synthetic cycles are well matched to thereal-world data. The investigated parameters are:– mean positive velocity (mph);– standard deviation of velocity (mph);– mean positive acceleration (m/s2);– standard deviation of acceleration (m/s2);– number of stops per mile (1/mile).

700 8006005004003002001000

Vel

ocity

90

0

80

70

60

50

40

30

20

10

a) Time (s)

140

(mph) Driving distance: 4.87 miles(km/h)

0

120

100

80

60

40

20

120010008006004002000

Vel

ocity

90

0

80

70

60

50

40

30

20

10

Time (s)

140(mph) Driving distance: 10.6 miles(km/h)

0

120

100

80

60

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20

1400 1600120010008006004002000

Vel

ocity

90

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Time (s)


0

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100

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60

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500 25001000 1500 20000

Vel

ocity

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3500500 1000 1500 2000 2500 30000

Vel

ocity

90

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50

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30

20

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Time (s)


0

120

100

80

60

40

20

b)

c)

d)

e)

Figure 5

Midwestern US Cycles.

ogst120054_TKLee 19/04/13 10:39 Page 121


acceleration decreases along increasing trip distance. Theresults can be explained as follows: acceleration is directlylinked to the velocity change during driving. During shortdistance trips, frequent starts and stops are prevalent whilethe driving speed is low. However, long distance tripsinclude a long duration of freeway driving that show highspeed cruising without frequent starts and stops. This is wellmatched to the trend of the number of stops per mile. Thenumber of stops per mile is decreasing from 0.97 stop/mile atthe 4.87 mile cycle to 0.17 at the 40.9 mile cycle.

3.3 Velocity vs Acceleration Distributions

Driving patterns with respect to driving distance are assessedby investigating two dimensional plots of velocity versusacceleration distributions. Figure 7 shows that driving patternsare significantly different depending on driving distance.More aggressive acceleration patterns are shown at shortdistance cycles (see Fig. 7a). At the 4.87 mile cycle, onedominant peak is shown around 30 mph and 1 m/s2 and itrepresents local driving. In contrast, long distance cycles showdominant operating events at high speed (above 60 mph) withmoderate acceleration (below 0.5 m/s2) and it represent freewaydriving. At the 40.9 mile cycle, the distribution pattern is

The error criteria of the variables directly related to velocityand acceleration are set to tight (± 5%).

Trends of the statistical parameters are shown in Figure 6.All presented parameters have clear and smooth trends withrespect to driving distance and three of them are shown here.We note that mean positive velocity and mean positive accel-eration have an opposite trend. The mean positive velocity ishigher, as trip distance is longer. In contrast, the mean positive

TABLE 1

Comparison of the statistical parameters of the synthetic driving cycleswith 4.87 miles driving distance

Statistical parameters Real cycles* Synthetic cycle

Distance (mile) 4.78 4.87

Mean positive velocity (mph) 27.8 27.6

Standard velocity (mph) 17.3 16.9

Mean positive acceleration (m/s2) 0.99 1.02

Standard acceleration (m/s2) 1.29 1.26

Number of stops per mile (1/mile) 1.00 0.97

* Mean values are presented for real cycles.

TABLE 2








number of stops per mile (1/mile) 0.62 0.56


TABLE 3










TABLE 4

Comparison of the statistical parameters of the synthetic driving cycles

with 25.2 miles driving distance









TABLE 5

Comparison of the statistical parameters of the synthetic driving cycles

with 40.9 miles driving distance









ogst120054_TKLee 19/04/13 10:39 Page 122


4 APPLICATIONS OF REPRESENTATIVE DRIVINGCYCLES

Accurate prediction of PHEV electric load on the grid isimportant to assess the impact of PHEV penetration on theelectric grid and its environmental influence. The electricload prediction requires a large number of driving data andthe data could be up to several hundred thousand trips. Whendetailed vehicle simulations are executed, the predictionaccuracy will be significantly improved. However, runningdetailed simulation with such a large number of data pushesthe computational efforts and time beyond manageable limits.Thus, computationally efficient methods are required to dealwith a large number of simulation cases.

One way to reduce the computational efforts is avoidingrepeating simulations by executing one or a few representa-tive simulations for the case of similar pattern driving casesoff-line, then using the off-line simulation results in predict-ing the PHEV impact on the grid. This concept was proposedby Lee and Filipi (2011b) in a compact representation ofPHEV behavior using response surface models. The responsesurface approach enables prediction of the PHEV electricitydemand from the grid and the amount of fuel consumedamount without detailed driving cycle profiles and drivingpattern dependency on the trip distance is captured.

The electric energy consumption and the fuel consumptionare expressed as functions of driving distance and batteryinitial State of Charge (SOC). The PHEV responses are pre-dicted by a series PHEV simulation model constructed usingPowertrain System Analysis Toolkit (PSAT) developed byArgonne National Laboratory (ANL) and in-house Matlabcodes. The model has been validated based on published lit-erature (Carlson et al., 2009; Duoba et al., 2009). Table 6shows the powertrain model specification for the selectedseries PHEV. To generate response surfaces over the possibledriving distance range and the initial battery SOC, full factor-ial experiments are design including five representativecycles in the Midwestern cycle set and five additional cyclesat each variable.

TABLE 6

Powertrain model specification

Component Specification

Engine 53 kW, based on a scaled 1 L Honda Insight

gasoline engine

Motor/generators 120 kW peak UQM power phase PM motor

Battery Li-ion, 6 Ah 75 cell SAFT model scaled to 16 kWh

Response surfaces are constructed as shown in Figure 8and they were originally proposed by Lee and Filipi (2011b).The simulation results were generated to cover wide rangesof trip distances and different battery initial SOCs. To avoid

shifted to the higher speed region with a narrowed accelera-tion range compared to shorter distance cycles as shown inFigure 7c. Medium distance cycles show widely distributeddriving patterns as shown in Figure 7b. The distribution indi-cates that local way and freeway driving patterns are evenlymixed.

40 50 (miles)

(km)

302010

70 806050403020100

0

Vel

ocity

60

0

50

40

30

20

10

a)Trip distance

80

(mph) Mean positive velocity(km/h)

0

60

40

20

10 50 (miles)

(km)

20 30 40

8010 20 30 40 50 60 700

0

Acc

eler

atio

n

1.2

0

0.2

0.4

0.6

0.8

1.0

b)Trip distance

Mean positive acceleration(m/s2)

10 50 (miles)

(km)

20 30 40

8010 20 30 40 50 60 700

0

Num

ber

of s

tops

(1/

mile

)

1.2

0

0.2

0.4

0.6

0.8

1.0

c)Trip distance

Number of stops per mile

Figure 6

Trends of driving cycle variables with respect to drivingdistances: a) mean positive velocity, b) mean positiveacceleration, c) number of stops per mile.

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possible small fluctuations on the response surfaces causedby the random characteristics inherent from the stochasticcycle synthesis process and vehicle supervisory control andto ensure monotonic trend with respect to the initial SOC(SOCini) and the trip distance, the response surfaces aresmoothened through regression analysis.

The response surface models can be used to predict thePHEV impact on the grid under different driving patterns.Figure 9 shows the electricity demand prediction results (Leeand Filipi, 2011b) under two charging scenarios, “chargingovernight” and “charging whenever possible” and underthree driving patterns:– naturalistic driving;– UDDS;– HWFET.

Fuel consumption

a) Trip distance (m

iles)

Initial SOC (-)

mfu

el (

gallo

n)

0.90

0.5

1.0

1.5

2.0

0.80.7

0.60.5

0.4 0

10

20

3040

Electric energy consumption

b) Trip distance (miles)Initial SOC (-)

Bat

tery

ene

rgy

(kW

h)

0

010

2030

400.80.7

0.60.5

0.4

2

4

6

8

10

Figure 8

Response surfaces of PHEV behavior computed from thePHEV simulation using representative synthetic real-worldcycles: a) the amount of fuel consumption, b) the amount ofelectric energy consumption.

Driving distance: 4.87 miles

Velocity (mph)a)

b)

c)

Acceleration (m/s2 )

Velocity (mph)Acceleration (m/s

2 )

Velocity (mph)Acceleration (m/s

2 )

0.05

0.04

0.03

0.02

0.01

00

30

60

-2

-10

1

2

90

Nor

mal

ized

dis

trib

utio

n (-

)


0.05

0.04

0.03

0.02

0.01

00

30

60

-2-1

0

1

2

90

Nor

mal

ized

dis

trib

utio

n (-

)


0.05

0.04

0.03

0.02

0.01

00

30

60

-2-1

0

1

2

90

Nor

mal

ized

dis

trib

utio

n (-

)

Figure 7

Velocity vs acceleration distributions of Michigan DrivingCycles (MDC): a) 4.78 mile cycle, b) 15.5 mile cycle,c) 40.9 mile cycle.

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“Charging overnight” scenario assumes that chargingprocess starts when PHEVs arrive at home and no more tripsexist during the day. “Charging whenever possible” scenariosassumes that charging is always possible when a vehicle isparked at any location. The prediction provides quantitativeassessment of the electricity demand per vehicle with respectto different charging scenarios. The prediction can be used topredict the total electricity demand of PHEV fleet from thegrid by multiplying a penetrated PHEV number.

CONCLUSIONS

A set of driving cycles is synthesized to represent real-worlddriving patterns in an urban/suburban area in Midwest US ina compact way. The proposed Midwestern US cycles consistof five one-way trips ranging from 4.87 miles to 40.9 miles.The driving patterns are reconstructed using informationextracted from a database of naturalistic driving informationin a form of Transfer Probability Matrices (TPMs). The data-base of naturalistic driving patterns was generated inSoutheast Michigan gathered through the Field OperationalTests (FOT) conducted by the University of MichiganTransportation Research Institute (UMTRI). The naturalisticdriving data includes 4 409 trips covering 830 independentdays and temporal distributions of departure and arrivaltimes.

The synthesis procedure is based on the Markov chain todeal with the random characteristics of driving cycles and thesubsequent statistical analysis to verify the representativeness.Five synthetic cycles are constructed using data groupedbased on the daily driving distance distribution. The synthesized

cycles show clear trends of statistical variables, such as meanpositive velocity, mean positive acceleration and number ofstops per mile. The proposed cycles include both local drivingpatterns and highway driving patterns and the portion of eachpattern changes with respect to the driving distance. Anapproach for the assessment of the impact of PHEVs on thegrid using response surface models is introduced as an exampleof the application of the Midwestern US cycle set. TheMidwestern US cycles will be applicable for Plug-in HybridElectric or Electric Vehicle design and control studies, as wellas for the assessment of the impact of electrified vehicles onthe grid.

ACKNOWLEDGMENTS

The authors would like to thank Zevi Bareket and Tim Gordonof UMTRI for providing the naturalistic driving data and fortheir valuable insight pertaining to driver behavior. Fundingfor this work was provided by the DOE US-China CleanEnergy Research Center on Clean Vehicle Collaboration(CERC-CVC) at the University of Michigan.

REFERENCES

André M. (2004) The ARTEMIS European driving cycles for mea-suring car pollutant emissions, Sci. Total Environ. 334-335, 73-84.

Carlson R.B., Lohse-Busch H., Duoba M., Shidore N. (2009) Drivecycle fuel consumption variability of plug-in hybrid electric vehicledue to aggressive driving, SAE Technical Paper 2009-01-1335.

Duoba M., Carlson R.B., Bocci D. (2009) Calculating results andperformance parameters for PHEVs, SAE Technical Paper 2009-01-1328.

20 241612840

MidnightNoonMidnight

Cha

rgin

g el

ectr

ic p

ower

dem

and

(kW

per

veh

icle

)

0.9

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

a)

Naturalistic drivingUDDSHWFET

Naturalistic drivingUDDSHWFET

Time (h)20 241612840

MidnightNoonMidnight

Cha

rgin

g el

ectr

ic p

ower

dem

and

(kW

per

veh

icle

)

0.9

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

b) Time (h)

Figure 9

Comparison of the predicted electricity demand at different driving patterns (naturalistic driving, UDDS and HWFET) under: a) “chargeovernight” scenario, b) “charge whenever possible” (Lee and Filipi, 2011b).

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Copyright © 2013 IFP Energies nouvellesPermission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not madeor distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of thiswork owned by others than IFP Energies nouvelles must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post onservers, or to redistribute to lists, requires prior specific permission and/or a fee: Request permission from Information Mission, IFP Energies nouvelles,fax. +33 1 47 52 70 96, or [email protected].

Fellah M., Signh G., Rousseau A., Pagerit S., Nam E., Hoffman G.(2009) Impact of real-world drive cycles on PHEV battery require-ments, SAE Technical Paper 2009-01-1383.

Kruse R.E., Huls T.A. (1973) Development for the federal urbandriving cycle, SAE Technical Paper 730553.

Kwon J., Kim J., Fallas E., Pagerit S., Rousseau A. (2008) Impactof Drive Cycles on PHEV Component Requirements, SAETechnical Paper 2008-01-1337.

LeBlanc D., Sayer J., Winkler C., Ervin R., Bogard S., Devonshire J.,Mefford M., Hagan M., Bareket Z., Goodsell R., Gordon T. (2006)Road Departure Crash Warning System Field Operational Test:Methodology and Results, UMTRI-2006-9-2.

Lee T.-K., Filipi Z.S. (2010) Synthesis and validation of representa-tive real-world driving cycles for plug-in hybrid vehicles, IEEEVehicle Power and Propulsion Conference (VPPC 2010), Lille,France, 1-3 Sept., pp. 1-6.

Lee T.-K., Filipi Z.S. (2011a) Synthesis of real-world driving cyclesusing stochastic process and statistical methodology, Int. J. VehicleDesign 56, 1, 43-62.

Lee T.-K., Filipi Z.S. (2011b) Response surface modeling approachfor the assessment of the PHEV impact on the grid, IEEE VehiclePower and Propulsion Conference (VPPC 2011), Chicago, IL,6-9 Sept.

Patil R., Adornato B., Filipi Z.S. (2009) Impact of naturalisticdriving patterns on PHEV performance and system design, SAETechnical Paper 2009-01-2715.

Final manuscript received in July 2012Published online in February 2013

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representative midwestern us cycles: synthesis and

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