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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 58, NO. 9, NOVEMBER 2009 4741

Intelligent Vehicle Power Control Based on MachineLearning of Optimal Control Parameters and

Prediction of Road Type and Traffic CongestionJungme Park, Zhihang Chen, Leonidas Kiliaris, Ming L. Kuang, M. Abul Masrur, Senior Member, IEEE,

Anthony M. Phillips, and Yi Lu Murphey, Senior Member, IEEE

Abstract—Previous research has shown that current drivingconditions and driving style have a strong influence over a vehicle’sfuel consumption and emissions. This paper presents a methodol-ogy for inferring road type and traffic congestion (RT&TC) levelsfrom available onboard vehicle data and then using this informa-tion for improved vehicle power management. A machine-learningalgorithm has been developed to learn the critical knowledge aboutfuel efficiency on 11 facility-specific drive cycles representing dif-ferent road types and traffic congestion levels, as well as a neurallearning algorithm for the training of a neural network to predictthe RT&TC level. An online University of Michigan-Dearbornintelligent power controller (UMD_IPC) applies this knowledge toreal-time vehicle power control to achieve improved fuel efficiency.UMD_IPC has been fully implemented in a conventional (non-hybrid) vehicle model in the powertrain systems analysis toolkit(PSAT) environment. Simulations conducted on the standard drivecycles provided by the PSAT show that the performance of theUMD_IPC algorithm is very close to the offline controller that isgenerated using a dynamic programming optimization approach.Furthermore, UMD_IPC gives improved fuel consumption in aconventional vehicle, alternating neither the vehicle structure norits components.

Index Terms—Fuel economy, machine learning, road typeand traffic congestion (RT&TC) level prediction, vehicle powermanagement.

I. INTRODUCTION

CUSTOMER demand for improved fuel economy is chal-lenging the automotive industry to produce affordable

new vehicles that deliver better fuel efficiency without sacri-ficing performance, safety, emissions, or reliability. To meetthis challenge, it is very important to optimize the architectureand the various devices and components of the vehicle system,as well as the energy-management strategy that is used to

Manuscript received October 1, 2008; revised March 12, 2009 and May 14,2009. First published July 17, 2009; current version published November 11,2009. This work was supported in part by the State of Michigan through the21st Jobs Fund under a grant and in part by the Institute of Advanced VehicleSystems, University of Michigan-Dearborn, under Grant 06-1-p1-0727. Thereview of this paper was coordinated by Dr. M. S. Ahmed.

J. Park, Z. Chen, L. Kiliaris, and Y. L. Murphey are with the Departmentof Electrical and Computer Engineering, University of Michigan-Dearborn,Dearborn, MI 48128 USA (e-mail: [email protected]).

M. L. Kuang and A. M. Phillips are with the Ford Motor Company, Dearborn,MI 48120 USA.

M. A. Masrur is with the U.S. Army RDECOM-TARDEC, Warren, MI49307 USA.

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TVT.2009.2027710

efficiently control the energy flow through the vehicle system.Our research focuses on the latter. Vehicle power managementhas been an active research area in the past decade and hasintensified recently by the emergence of hybrid electric vehicletechnologies. Most of the previous approaches were developedbased on mathematical models or knowledge derived fromstatic vehicle operation data. The application of optimal controltheory to power distribution and management has been themost popular approach, which includes linear programming [1],optimal control [2], and, particularly, dynamic programming(DP) [3]–[5]. In general, these techniques do not offer an onlinesolution because they assume that the future drive cycle isentirely known. However, these results can be used as a bench-mark for the performance of online power control strategies. Ifonly the present state of the vehicle is considered, optimizationof the operating points of the individual components can still bebeneficial, but the benefits will be limited [6]–[8]. Interestingtechniques for deriving effective online control rules based onthe results generated by offline DP and quadratic programming(QP) can be found in [3] and [9].

Recent research has shown that current driving conditionsand the driver’s driving style have a strong influence over avehicle’s fuel consumption and emissions [10], [11]. Drivingpatterns exhibited by a real-world driver are the product ofthe instantaneous decisions of the driver to respond to the(physical) driving environment. Specifically, varying road typeand traffic conditions, driving trends, driving styles, and vehicleoperating modes have had varying degrees of impact on vehiclefuel consumption. However, most of the existing vehicle powercontrol approaches do not incorporate knowledge about drivingpatterns into their vehicle power-management strategies. Themain contribution of this paper is an algorithm for optimizationof vehicle power management that utilizes inferred knowledgeof road type and traffic congestion (RT&TC). Only recently hasthe research community in vehicle power control begun to ex-plore ways to incorporate knowledge about driving patterns intoonline control strategies [12]–[15]. A comprehensive overviewof intelligent system approaches for vehicle power managementcan be found in [16].

This paper presents our research on intelligent vehicle powermanagement using machine learning. Specifically, we willpresent machine-learning algorithms for learning about theoptimal power control parameters for all 11 standard facility-specific (FS) drive cycles proposed in [17] and [18] and

0018-9545/$26.00 © 2009 IEEE

4742 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 58, NO. 9, NOVEMBER 2009

Fig. 1. Intelligent power control in a vehicle system.

about predicting road types and traffic congestion, as well asan online University of Michigan-Dearborn intelligent powercontroller (UMD_IPC) that applies the knowledge obtainedthrough machine learning to online vehicle power control withthe online prediction of driving environment by a neural net-work. UMD_IPC has been fully implemented in a conventionalvehicle model built using the powertrain systems analysis tool-kit (PSAT) (http://www.transportation.anl.gov/software/PSAT/index.html) simulation program and tested on 11 drive cyclesprovided by the PSAT library. PSAT is a high-fidelity sim-ulation software developed by Argonne National Laboratory,Argonne, IL, under the direction of and with contributionsfrom Ford, General Motors, and Chrysler. PSAT is a “forward-looking” model that simulates vehicle fuel economy and per-formance in a realistic manner—taking into account transientbehavior and control system characteristics. It can simulate abroad range of predefined vehicle configurations (conventional,electric, fuel cell, series hybrid, parallel hybrid, and power splithybrid).

In this research project, the PSAT software is used to builda high-fidelity vehicle model; simulate drive cycles to gener-ate numerical data, such as fuel consumption and emissionsand vehicle performance; and implement an intelligent powercontroller UMD_IPC. Experiments will show that the onlineperformances of UMD_IPC are very close to the offline optimalcontroller built based on DP. In comparison with the defaultcontroller used by the vehicle model in PSAT, our resultsshowed a maximum of 3.95% fuel reduction in an urban drivecycle. Furthermore, the implementation of UMD_IPC does notrequire the change of any vehicle components. Although theresearch results presented in this paper were generated basedon a conventional vehicle model, the proposed technology canbe extended to a hybrid vehicle system, which is the authors’ongoing effort.

This paper is organized as follows. Section II presents themachine-learning process of optimal power control in a con-ventional vehicle, Section III presents a neural network sys-

tem for predicting roadway type and traffic-congestion level,Section IV presents the intelligent online vehicle power-management system, namely, UMD_IPC, Section V presentsthe experiment results, and Section VI presents the conclusion.

II. OPTIMAL POWER CONTROL IN A CONVENTIONAL

VEHICLE SYSTEM USING MACHINE LEARNING

Fig. 1 illustrates the interaction between the proposed in-telligent power controller UMD_IPC and the major powercomponents in a conventional vehicle system. At any given timeduring a drive cycle, based on the current vehicle state, which isrepresented by the current vehicle speed, driver power demand,electrical load and state of charge (SOC) of the battery, theUMD_IPC calls the neural network NN_RT&TC to predict thecurrent RT&TC level and calculates the electric power set pointto the battery controller and a resultant feedforward torque com-pensation to the engine controller. The variable Ps, representingthe power actually to be charged (Ps > 0) or discharged (Ps <0) from the battery, is set by the UMD_IPC with the aim ofminimizing fuel consumption. The desired engine power Peng,which is calculated based on the optimal value of Ps, is used tofind the feedforward torque compensation through the enginefuel-efficiency map. The functional relationship between Peng

and Ps is shown as follows:

Peng =Pd + Ge2m(Pe, ω) (1)

Pe =Pl + Pb (2)

Pb = ηin2out(Ps, SOC, T ) (3)

whereω engine speed;Pd driver demanded power at the wheels;Pe electrical power from the alternator;Ge2m(Pe, ω) mechanical power required by the alternator

based on alternator efficiency map Φalt toproduce a given electrical power Pe at agiven speed;

PARK et al.: VEHICLE POWER CONTROL BASED ON MACHINE LEARNING OF OPTIMAL CONTROL PARAMETERS 4743

Fig. 2. Battery efficiency map Φbat.

Pl electrical power required by the various ve-hicle electrical loads;

Ps actual power stored in and drawn out of thebattery;

SOC battery state of charge;Pb power output at the battery controller, which

is a function of the internal battery powerPs, SOC, and battery temperature T and isdenoted as ηin2out(Ps, SOC, T ).

ηin2out(Ps, SOC, T ) reflects power losses in the battery. In thispaper, ηin2out is derived by modeling the battery-efficiency mapΦbat shown in Fig. 2. The battery-efficiency map contains thebattery charge/discharge curves generated by the battery modelin PSAT for SOC = 40%, 50%, and 60%. It appears that withinthe battery-efficiency range of 40%–60%, the battery chargeand discharge curves have very little variation.

The machine-learning algorithm, namely, learning minimumpower consumption on FS drive cycles (LMPC_FSDC), ispresented in Section II-B. LMPC_FSDC attempts to learn thevalues of parameters that minimize the vehicle fuel consump-tion function γ, which is empirically modeled as a quadraticfunction of Ps. Using this quadratic function, a fuel consump-tion cost index is defined and solved using a QP approach toproduce optimal values of Ps. The resultant optimal solutionis dependent on the drive cycles. The machine-learning algo-rithm LMPC_FSDC learns the optimal values of the empiricalparameters generated by the QP for a defined set of roadwaytypes and congestion levels. The QP approach is based onthe research presented in [9], which is briefly summarized inSection II-A.

A. Vehicle Power-Optimization Model

The power-optimization problem is modeled as a multistepdecision problem in a drive cycle with N steps that minimizesa performance index J , i.e.,

minP s

J = minPs

N∑t=1

γ (Ps(t), t) =N∑

t=1

minPs

γ (Ps(t), t) (4)

where γ(Ps, t) is the fuel consumed as a function of Ps(t) attime t. The fuel-consumption function γ(Ps, t) is approximatedas a convex quadratic function, i.e.,

γ(Ps, t)≈ϕ2(t)Ps(t)2+ϕ1(t)Ps(t)+ϕ0(t), ϕ2 >0 (5)

where ϕi represents time-varying coefficients. The objectivefunction then becomes

minP s

J =N∑

t=1

minPs

γ (Ps(t), t)

≈N∑

t=1

minPs

(ϕ2(t)P 2

s (t) + ϕ1(t)Ps(t) + ϕ0(t))

(6)

where P s contains the optimal values of Ps(t) for t =1, . . . , N . To create a well-posed problem, the constraint thatthe energy in the battery at the end of the drive cycle Es(N)must match the energy at the beginning of the cycle Es(0), i.e.,Es(N) = Es(0) is applied to the optimization. This constraintcan be written as

Es(N) − Es(0) =N∑

t=1

Ps(t) = 0. (7)

By adjoining this constraint to our objective function us-ing a Lagrange multiplier, we obtain the following Lagrangefunction:

L (Ps(1), . . . , Ps(N), λ) =N∑

t=1

{ϕ2(t)Ps(t)2 + ϕ1(t)Ps(t)

}

+ϕ0(t) − λN∑

t=1

Ps(t). (8)

By taking the partial derivatives of the Lagrange function Lwith respect to Ps(t), t = 1, . . . , N , and λ, respectively, andby setting the equations to 0, combined with (7), the optimalbattery power setting at each time step t can be obtained asfollows:

Ps(t) =λ − ϕ1(t)2ϕ2(t)

(9)

where

λ =

N∑t=1

ϕ1(t)2ϕ2(t)

N∑t=1

12ϕ2(t)

.

The above formula for calculating λ requires the knowledgeof ϕ1(t) and ϕ2(t) over the entire drive cycle, which is notavailable in advance to the online controller during normal real-world driving. To solve this problem, we adopt the methodproposed in [9] that uses a proportional–integral controller toproduce a value of λ online based on the measured energy levelin the battery Es, i.e.,

λ(t) = λ0 + KP (Es(0) − Es(t − 1))

+KI

t−1∑p=1

(Es(0) − Es(p)) . (10)


TABLE ISTATISTICS OF THE 11 FS DRIVE CYCLES

The optimal parameters ϕ0(t), ϕ1(t), ϕ2(t), λ0, and KP

and KI are obtained by the machine-learning algorithmdescribed in Section II-B with the constraints of the up-per and lower bounds of Ps(t), which are also discussedin Section II-B.

B. Machine Learning About Optimal Power Settings

We model the road environment of a driving trip as a se-quence of different roadway types, such as local, freeway, andarterial/collector, augmented with different traffic congestionlevels. Sierra Research Inc. has shown that fuel efficiency andemissions are connected to roadway types as well as trafficcongestion levels. They developed a set of 11 standard drivecycles presented in [17] and [18], called facility-specific (FS)cycles, to represent passenger car and light truck operationsover a broad range of facilities and congestion levels in urbanareas. Table I shows the most recent definition of these roadtypes [18], along with the labels that we assigned, where Vavg

is the average vehicle speed in meters per second, Vmax is themaximum vehicle speed in meters per second, and Amax is themaximum acceleration. The 11 drive cycles are divided intothe following four categories of roadway types: 1) freeway;2) freeway ramp; 3) arterial; and 4) local. The two categories,freeway and arterial, are further divided into subcategoriesbased on a qualitative measure called level of service (LOS)that describes operational conditions within a traffic streambased on speed and travel time, freedom to maneuver, trafficinterruptions, comfort, and convenience. Six types of LOS aredefined with labels, i.e., A through F, with LOS A representingthe best operating conditions and LOS F the worst. Each LOSrepresents a range of operating conditions and the driver’sperception of those conditions; however, safety is not includedin the measures that establish service levels [18], [19]. Inthis paper, we use this set of 11 FS cycles as the standardmeasure of roadway types and traffic-congestion levels. Forthe convenience of description, we label these 11 FS cycles asRT1, . . . , RT11.

The problem of optimal vehicle power management is formu-lated as follows. Assume that for any given drive cycle DC(t)(t ∈ [0, te], where te is the ending time of the drive cycle) at anygiven time t, the vehicle is operating according to one of the 11road types and traffic-congestion levels, i.e., RTi, i = 1, . . . , 11.

Then, the online optimal power controller UMD_IPC choosesthe optimal battery power settings based on RTi. The machine-learning algorithm LMPC_FSDC has been developed to learnthe optimal power settings for all 11 FS drive cycles, i.e., RTi,i = 1, . . . , 11.

Fig. 3 shows the major computational steps in LMPC_FSDC.The algorithm requires the use of a high-fidelity vehicle sys-tem modeling and simulation program F, such as PSAT orADVISOR. Two major steps in the algorithm require the useof such a simulation program. First, we need the simulationprogram to build a vehicle model V of a particular interest.Second, we run the vehicle model V in the simulation programF to generate step-by-step system state data: Pd(t), Pl(t), andω(t), t = 1, . . . , N , every standard FS drive cycle, RTi, i =1, . . . , 11.

The fuel matrix F_Ri is generated for all the Ps valueswithin the specific upper and lower bounds of Ps, denotedas Ps_min(t) ≤ Ps(t) ≤ Ps_max(t), which are calculated asfollows. Let Peng_max(ω(t)) be the maximum engine powerwith engine speed ω(t) and Palt_max(ω(t)) be the maximummechanical alternator power with the given speed ω(t). BothPeng_max(ω(t)) and Palt_max(ω(t)) are defined by the vehiclemodel V. At each time t, the maximum electrical power at thealternator Pe_max(ω(t)) can be calculated by

Pe_max (ω(t)) = Gm2e {min [Peng_max (ω(t))

− Pd(t), Palt_max (ω(t))] , ω(t)} (11)

where Gm2e(Palt, ω) is a function that calculates the elec-trical power based on the alternator efficiency map Φalt fora given mechanical power Palt and rotational speed ω. Themin[Peng_max(ω(t)) − Pd(t), Palt_max(ω(t))] in (11) repre-sents the maximum mechanical power at time t. Based on theengine and alternator constraint, the upper and lower bounds ofPs are calculated by

Ps_max1(t) = ηout2in (Pe_max (ω(t)) − Pl(t))

Ps_min1(t) = ηout2in (0 − Pl(t)) (12)

where ηout2in is a function that calculates the internal batterypower Ps, namely the power to be stored or drawn fromthe battery for a given battery power Pb at the battery ter-minal, by using the battery efficiency map Φbat shown inFig. 2. (Pe_max(ω(t)) − Pl(t)) and (0 − Pl(t)) represent, re-spectively, the maximum and minimum battery power Pb at thebattery terminal at time t.

Since the boundary of Ps(t) is also constrained (or restricted)by current SOC, i.e., SOC(t), as shown in Fig. 14, the upper andlower bounds of Ps are

Ps_max(t) = min (Ps_max1(t), Ps_max2(t))

Ps_min(t) = max (Ps_min1(t), Ps_min2(t)) (13)

where Ps_max2(t) and Ps_min2(t)) can be calculated based onSOC(t). A fuel-rate matrix F_R(Ps(t), t| ω(t), Pd(t), Pl(t)) isgenerated for each time step t as a function of Ps(t), which is


Fig. 3. Computational steps of machine-learning algorithm LMFC_FSDC.

the charge and discharge power within the system constraintsspecified in (13) at time t for the given engine speed ω(t),required drivetrain power Pd(t), and electric load power Pl(t).The tth column of matrix F_R(∗, t) is represented as a convexquadratic cost function of Ps. By using a regression function,we can obtain the coefficients ϕ2(t), ϕ1(t), and ϕ0(t) suchthat ϕ2(t)P 2

s (t) + ϕ1(t)Ps(t) + ϕ0 ≈ F_R(∗, t) with thebest fit.

Fig. 4 shows a few example of the actual fuel rates andthe convex quadratic cost functions calculated at varioustime steps associated with the vehicle model Ford Taurusin the Arterial AB drive cycle, i.e., RT8. Note the fuel ratehas been multiplied with the chemical-energy contents offuel, i.e., Hf = 44 kJ/g [20], to obtain a suitable scaling,and Ps values have been normalized as follows: {Ps(t) −mean(Ps(·))}/σ(Ps(·)), where mean(Ps(·)) is the mean of Ps,and σ(Ps(·)) is the standard deviation of Ps. This illustratesthat a quadratic function is a good choice to represent thefuel function F_R(Ps(t), t| ω(t)Pd(t), Pl(t)). Fig. 5 showsthe coefficients {ϕ2(t), ϕ1(t), ϕ0(t)| t = 511, . . . , 533} of thequadratic cost function of Ps(t) for the same drive cycle usedin Fig. 4.

These coefficients obtained at all time steps for each drivecycle RTi, i = 1, . . . , 11 are then used to calculate the power

control parameters ϕ̃i1, ϕ̃i

2, λi, KiP , and Ki

I as follows:

ϕ̃i1 =

1N

N∑t=1

ϕi1(t) ϕ̃i

2 =1N

N∑t=1

ϕi2(t) (14)

λi =

N∑t=1

ϕi1(t)

2ϕi2(t)

N∑t=1

12ϕi

2(t)

(15)

KiP = ϕ̃i

2 · 4 · 10−2 KiI = ϕ̃i

2 · 6 · 10−6. (16)

The machine-learning algorithm to the 11 standard FS drivecycles and the results are shown in Table II, which serves asthe knowledge base for the online controller UMD_IPC.

III. PREDICTING ROADWAY TYPE AND

TRAFFIC CONGESTION LEVEL

The problem of roadway type prediction is formulated asfollows. Let SP(t) be the speed profile of a driver on the roadt = 0, 1, . . . , tc, where tc is the current time instance, and letR(t) be the roadway types that the driver needs to go throughto complete his trip 0 < t < te, where te is the time when the


Fig. 4. Actual and calculated fuel rate for the arterial AB drive cycle RT8.

Fig. 5. Coefficients of the quadratic fuel-rate function calculated from thearterial AB drive cycle RT8.

trip ended. At any given time tc, R(tc) ∈ {RTi| i = 1, . . . , 11}.The roadway type in the near future is to be predicted based onthe short-term memory of the driving environment during thetrip. Specifically, we attempt to develop a nonlinear functionF such that F (SP(t)| t ∈ [(tc − ΔZ), tc]) = RTj , 0 < j ≤ 11,where ΔZ > 0 is called the window size that characterizesthe length of the speed profile that should be used to exploredriving patterns. The variable RTj is the roadway type that thedriver will be on during the time interval [tc, (tc + Δt)], i.e.,R(t) = RTj for t ∈ [tc, (tc + Δt)]. We refer to Δt > 1 as the

TABLE IIOPTIMAL PARAMETER SETTINGS GENERATED BY THE

MACHINE-LEARNING ALGORITHM LMFC_FSDC,FOR 11 STANDARD FS DRIVE CYCLES

time step. To solve this problem, four different aspects of theroadway type predictor need to be determined as follows.

1) Select effective features that can be extracted from SP(t),tc − ΔZ < t ≤ tc, for the prediction of the current road-way type.

2) Determine the optimal window size ΔZ.3) Determine the optimal time step Δt.4) Develop a function F that has the capability of accurately

predicting roadway types in a sufficiently short timesuitable for online driving prediction. In this paper, F isa neural network described in Section III-C.

The following three key components developed for predict-ing road types and traffic congestion levels are described inSections III-A–C, respectively:

1) feature selection;2) prediction windows and time step;3) a neural network.

A. Feature Selection

Roadway types and traffic-congestion levels can generally beobserved in the speed profile of the vehicle. The statistics usedto characterize driving patterns include 16 groups of parameters(62 total) suggested by the Sierra Research, and parametersin nine out of these 16 groups critically affect fuel usage andemissions [10], [11]. However, it may not be necessary touse all these features to predict a specific drive pattern, and


additional new features may be explored as well. For example,Langari and Won [14] used only 40 of the 62 parameters andthen added the following seven new parameters: 1) trip time;2) trip distance; 3) maximum speed; 4) maximum acceleration;5) maximum deceleration; 6) number of stops; and 7) idle time(percent of time at a speed of 0 km/h). However, the use ofadditional parameters needs to be balanced with the “curseof dimensionality”: Too many features may degrade systemperformance. Furthermore, in onboard vehicle implementation,more features imply higher hardware cost and/or more com-putational time. The problem of selecting a subset of optimalfeatures is a classic research topic in pattern recognition and isa NP problem. Because the feature selection problem is com-putationally expensive, this research has focused on finding aquasi-optimal subset of features, where the term quasi-optimalimplies good, but not necessarily always, optimal classificationperformance. Interesting feature-selection techniques can befound in [21]–[23]. However, most of these feature-selectionalgorithms were developed for two-class classification prob-lems, and extensions to K-class (K > 2) will significantlyincrease the computational time. With this background in mind,we developed the following feature-selection algorithm basedon roadway types.

Feature-Selection Algorithm:

Step 1) Let X be the training data set and Ω be the initialset of n features, which can be obtained from thosesuggested by the research community, as discussedearlier.

Step 2) Relabel data in X with freeway samples as “1” andall others as “0.” Denote this training data set as X1.Select the best features from Ω that can classify allthe freeway data against all other data in X1. Denotethis feature set as Ω1.

Step 3) Relabel data in X with freeway ramp samples as “1”and all others as “0.” Denote this training data set asX2. Select the best features from Ω that are not inΩ1 and that can classify all the freeway ramp dataagainst all other data in X2. Denote this feature setas Ω2.

Step 4) Relabel data in X with arterial data samples as “1”and all others as “0.” Denote this training data set asX3. Select the features that are not in Ω1 ∪ Ω2 andthat can best classify all the arterial data against allother data in X3. Denote this feature set as Ω3.

Step 5) Relabel data in X with local roadway data samplesas “1” and all others as “0.” Denote this trainingdata set as X4. Select the features that are not inΩ1 ∪ Ω2 ∪ Ω3 and that can best classify all the localroadway data against all others in X4. Denote thisfeature set as Ω4.

Step 6) Output feature set Ωnew = Ω1 ∪ Ω2 ∪ Ω3 ∪ Ω4.

When the above feature-selection algorithm was applied to aninitial set (Ω) of 47 features, as suggested by Langari andWon [14], we obtained the set (Ωnew) of 14 features shownin Table III.

B. Optimal Window Size and Time Step in Online Predicting

Since we attempt to predict the roadway type in the nearfuture, the driving speed in the last segment, i.e., [tc − ΔZ, tc],where tc is the current time, is used to predict the road typeon which the driver is during the time period [tc, tc + Δt].The prediction is made at time steps kΔt, k = 1, 2, . . .. Asdiscussed above, the window size of the speed profile segmentsis ΔZ, and the time interval over which the prediction is madeis Δt. Fig. 6 illustrates these two parameters on the speedprofile of the urban dynamometer driving schedule (UDDS)drive cycle. The x-axis represents the time during a drivecycle, and the y-axis represents the vehicle speed in meters persecond. The segments shown have equal sizes of ΔZ = 150 s,and the time step Δt = 100 s. Please note that Δt = 100 s ischosen here only for clarity of illustration.

In reality, as we will show, Δt should be smaller than 100 s.The two parameters are important for the accuracy of pre-diction. Since features characterizing road types are extractedfrom the speed profile of the vehicle in the time interval[tc − ΔZ, tc], if ΔZ is too small, then the segment may betoo small to contain useful information. If ΔZ is too big, thenthe segment may contain obsolete information. Once ΔZ isdetermined, the 14 features presented in Table III are extractedfrom the speed profile within the time interval [tc − ΔZ, tc]and are used as the input feature vector to the neural networkdescribed in Section III-C. The time step Δt also needs to beproperly determined. If Δt is too short, then it would imply thatthe prediction routine would run often. If it is too long, then theroadway type may change during the near future horizon, i.e.,[tc, tc + Δt].

The optimal window size and optimal time step are deter-mined through a series of experiments by varying ΔZ in areasonable range, such as 50, 100, 150, and 200, and Δt = 1,2, 3, 5, 10, 15, and 20 s. To give a more realistic estimate ofgeneralization, we use a fivefold cross validation method inthis experiment. For every pair of window size and time step(ΔZ,Δt), we generate the signal segments with length = ΔZat every time step Δt on all the 11 standard FS drive cycles andanother 11 drive cycles provided in the PSAT library, namely,1) UDDS; 2) HWFET; 3) US06; 4) SC03; 5) LA92; 6) IM240;7) Rep05; 8) NY City; 9) HL07; 10) Unif01; and 11) Arb02. LetΨ(ΔZ,Δt) denote the set of all the signal segments generatedfrom these 22 drive cycles using the window size and timestep (ΔZ,Δt). We randomly partition Ψ(ΔZ,Δt) into fivesubsets, namely, Ψ1, Ψ2, Ψ3, Ψ4, and Ψ5. Five neural networksare trained and validated as follows: The ith neural network,for i = 1, . . . , 5, is trained on four subsets Ψj , 1 ≤ j ≤ 5 andj �= i and validated on the data set subset Ψi. Fig. 7 showsthe prediction accuracy of the classifiers trained with differentwindow sizes and time step sizes averaged through the fivefoldcross validations on all the training data sets [see Fig. 7(a)]and the validation data sets [see Fig. 7(b)]. The results obtainedfrom both the training and test data show that performances arestabilized when ΔZ increases to 150 s since the performancesof ΔZ = 150 s and ΔZ = 200 s are very close. As for the timesteps, the performance generally improves when Δt decreases,and Δt = 1 gives the best performances over all window sizes.This makes sense since the more frequently we predict, the


TABLE IIIFOURTEEN FEATURES SELECTED FOR ROADWAY-TYPE PREDICTION

Fig. 6. Segments of a speed profile.

more likely we will catch all the road type transitions. However,as we stated before, smaller time steps demand more computa-tional power. As a tradeoff, Δt = 3 and ΔZ = 150 were usedin the experiments presented in this paper since they gave goodperformances on both the training and test data. In Section V,we also analyzed the fuel efficiency with three different timesteps.

C. Training a Neural Network to Predict Road Types

We developed a multilayered multiclass neural network,namely, NN_RT&TC, for the prediction of road types andtraffic congestion levels. Fig. 8 shows the architecture ofNN_RT&TC. The input layer has 14 nodes for the featuresspecified in Table III, a hidden layer of 20 nodes, and 11output nodes representing the 11 class labels {RT1, . . . , RT11}

to represent the 11 FS drive cycles. One important issue in amulticlass neural network classifier is the proper encoding ofthe classes in the output nodes of the neural network. We choseto use a “one-hotspot” method [24] described as follows. Sincethis is an 11-class classification problem, we need an 11-bitoutput layer. Each class is assigned a unique binary string(codeword) of length k. For example, class 1 is assigned acodeword of 00000000001, class 2 is assigned a codeword of00000000010, class 3 is assigned of a codeword 00000000100,etc. The advantage of this encoding is that it gives enoughtolerance among different classes.

The neural network is trained using the well-known back-propagation algorithm for weight update. Based on the studyresults presented in the last section, we use ΔZ = 150 sand Δt = 3 s. The training and test data are generated from11 Sierra data and 11 PSAT drive cycles as follows. Thefeature vector 〈x1, x2, . . . , x14〉 is generated as follows. Foreach drive cycle DC(t) (0 ≤ t ≤ te), DC segments are gen-erated on the intervals s0 = [t0,ΔZ), . . . , sk = [kΔt,ΔZ +kΔt), . . . , ske = [te − ΔZ, te], where k ≥ 1. From the speedfunction of each segment, we extract a vector of the 14 featuresspecified in Table III. The feature vector extracted from everyspeed signal segment is labeled by the roadway type of its nextsegment since we are training the prediction function.

There are totally 4399 segments generated from these22 drive cycles. The separation of training and test data isthrough a random stratified sampling procedure. The resultingtraining data contain 3519 feature vectors, and the test datacontain 880 feature vectors. The performance of the neuralnetwork on the fivefold cross validation is 95% on the trainingdata and 94% on the test data.

When NN_RT&TC is used inside a vehicle to predict theroadway type at time tc, the vector of the 14 features is extractedfrom the vehicle speed during the time interval [tc − 150 s, tc].


Fig. 7. Prediction accuracies using various window sizes and time steps.

Fig. 8. Architecture of NN_RT&TC.

The output from NN_RT&TC is the roadway type that is usedto produce the optimal power distribution during time interval[tc, tc + 3 s]. We use 11 PSAT drive cycles as test data toevaluate the UMD-IPC system. The PSAT drive cycles can beconsidered as composites of the 11 classes of roadway typesand traffic congestion levels. Fig. 9 shows an example of adrive cycle LA92 that is segmented and labeled according tothe definition of the 11 standard FS RT&TC classes, as definedin [18]. The x-axis indicates the time, and the y-axis indicatesthe speed in meters per second. The prediction results generated

Fig. 9. Example of segmented, labeled, and predicted drive cycle LA92.

by the neural network NN_RT&TC are shown in blue. Noticethat there is a delay in the prediction for the first 150 s.

IV. UMD_IPC: AN INTELLIGENT ONLINE

VEHICLE POWER CONTROLLER

The intelligent power controller UMD_IPC, which containsthe neural network NN_RT&TC, has been fully implementedin the PSAT simulation environment. Fig. 10 gives the majorcomputational steps of UMD_IPC at any given time t during areal-world drive cycle. The UMD_IPC has the knowledge baseKB = {ΔZ = 150 s,Δt = 3 s, ϕ̃i

1, ϕ̃i2, λ

i,KiP , and Ki

I | i =1, . . . , 11}, which is generated by the machine-learning algo-rithm presented in Section III.


Fig. 10. Computational flow of UMD_IPC: an intelligent vehicle power controller.

At any time t during a real-time drive cycle in a vehicle sys-tem, UMD_IPC is able to obtain the vehicle state V_state(t) ={vs(t), Pd(t), Pl(t), ω(t), SOC(t)}. If the vehicle is at thestart mode, i.e., t < ΔZ, UMD_IPC uses the default powercontrol. When t = ΔZ, UMD_IPC gets the current vehiclestate V_state(t) and calls the neural network NN_RT&TCto make the first prediction of the roadway type and trafficcongestion level. Based on the road type R(t) predicted byNN_RT&TC, UMD_IPC retrieves the optimal control parame-ters associated with the road type r = R(t), ϕ̃r

1, ϕ̃r2, λr, Kr

P ,and Kr

I . If this is the first prediction, λr is used as the initialvalue, i.e., λ0.

The battery energy at the current time Es(t) is calculatedbased on the current SOC(t) followed by the calculation of λ(t)using the following formula:

λ(t) = λ0 + KrP (Es(0) − Es(t − 1))

+KrI

t−1∑p=1

(Es(0) − Es(p)) . (17)

Fig. 11 shows the λ(t) values in the simulation of all 11 PSATdrive cycles, which change with time and the road-predictionresults.


Fig. 11. λ values for all 11 PSAT drive cycles.

Fig. 12. Peng_ max(ω): max engine power with engine speed.

The instantaneous fuel-rate matrix at time t, i.e.,F_R(Ps(t), t| Pd(t), Pl(t), ω(t)), is calculated throughthe following procedure based on the current constraints ofPs(t), the engine power Peng, the engine speed ω(t), and theengine efficiency map Φeng, which is provided by the vehiclesystem as a function of engine power and engine speed.

For every Ps(t) such that Ps_min(t) < Ps(t) < Ps_max(t),the engine power at time t is calculated as follows:

Peng(t) = Pd(t) + Ge2m (Pl(t) + ηin2out (Ps(t)) , ω) (18)

where Ge2m calculates the mechanical power based on thealternator efficiency map Φalt for the given electrical powerPe(t) = Pl(t) + ηin2out(Ps(t)) at the given speed ω, andηin2out calculates the corresponding battery power output atthe terminal based on the battery efficiency map Φbat shownin Fig. 2 for the given the internal battery power Ps. Then,F_R(Ps(t), t |Pd(t), Pl(t), ω(t)) = Φeng(Peng(t), ω(t)).

The optimal power to be charged to or discharged from thebattery is obtained by searching through the fuel-rate matrix forthe Ps that minimizes the following quantity:

P os (t) = arg min

Ps(t){F_R (Ps(t), t) − λ(t)Ps(t)} (19)

where Ps_min(t) ≤ Ps(t) ≤ Ps_max(t).

Fig. 13. Palt_ max(ω): max mechanical alternator power with alternatorspeed.

Fig. 14. Ps boundary at time t as a function of SOC(t) value.

The optimal engine power at time t is calculated using theformula

P oeng(t) = Pd(t) + Ge2m (Pl + ηin2out (P o

s (t)) , ω) . (20)

Both P os (t) and P o

eng(t) are sent to the vehicle system, and theUMD_IPC continues the process at time t + 1.

V. EXPERIMENTS

UMD_IPC has been implemented in a conventional vehiclemodel provided by the PSAT software, namely, a Ford Tauruswith a 95-kW 1.9-L Spark Ignition engine, five-gear manualtransmission, a 12–14-V 2-kW alternator, and a 66-A · h/12-Vlead acid battery. Since electrical loads in passenger vehicles,usually, are no larger than 1000 W, a constant electrical loadPl = 1000 W is used in all the simulations. Figs. 12 and 13show the engine and alternator constraints of the Ford Taurusmodel provided by the PSAT program, and Fig. 14 shows theconstraints of Ps with various SOC values.

The online controller UMD_IPC is applied to all 11 PSATdrive cycles. Figs. 15 and 16 show the detailed experimentresults generated from the three most interesting drivingcycles, namely, UDDS, LA92, and UNIF01. UDDS, which issometimes called FTP72, represents city driving conditionsin an urban area with frequent stops. LA92, which is alsocalled unified cycle, was constructed from segments of anactual driving recording in Los Angeles. It is a more aggressivedriving cycle than the federal test procedure (FTP) as it hashigher speeds, higher acceleration rates, fewer stops per meter,and less idle time (see Fig. 9). The UNIF01 cycle developedby Sierra Research for the California Air Resources Board isa modified form of the LA92. For the purpose of comparison,we applied offline DP controller to these three drive cycles inthe attempt of finding the optimal benchmark performances.


Fig. 15. SOC comparison on three driving cycles. The x-axis represents time measured in seconds, and the y-axis represents the SOC measured in percentages.(a) SOC compensation during driving cycle UDDS. (b) SOC compensation during driving cycle UNIF01. (c) SOC compensation during driving cycle LA92.

It should be kept in mind that the DP controller is not applicableto online control [3], [9] since it requires full knowledge of theentire drive cycle to optimize the power management strategyat each time.

In Fig. 15, we show that the battery SOC generated duringthe simulation run of the three drive cycles using the three dif-

ferent power controllers, namely, the offline DP controller, theFord Taurus controller provided by PSAT, and the UMD_IPCcontroller. It can be observed that the SOC curves generatedby the UMD_IPC from all three drive cycles behave quitesimilarly to the respective ones generated by the offline DPcontroller, whereas the SOC curves generated by the Ford


Fig. 16. Comparisons of the battery power Ps generated by the three controllers DP, UMD_IPC, and Ford Taurus. (a) Battery power Ps generated during drivecycle UDDS.(b) Battery power Ps generated during drive cycle UNIF01.

Taurus controller are significantly different from the optimalcurves. Fig. 16 presents battery power Ps dynamically gener-ated by the three controllers for UDDS and UNIF01 cycles. Thebattery power for DP and UMD_IPC controllers are discretizedby a step size of 50 W. These graphs clearly show that thebattery powers generated by the UMD_IPC controller are closeto the optimal ones generated by DP.

Fig. 17 presents the performance comparison with respectto fuel consumption on all 11 PSAT drive cycles generatedby the same three power controllers; (a) presents the fuel

consumption, and (b) presents the fuel saved. We use thefuel consumed by the Ford Taurus in PSAT as the baselineto measure the fuel saved by the DP and UMD_IPC con-trollers. The UMD_IPC gave more than 2% savings on fuelconsumption from six drive cycles, namely, UDDS, UNIF01,LA92, 505UDDS, SC03, and TripEPA. In particular, for theUDDS, UNIF01, and LA 92 drive cycles, UMD_IPC’s perfor-mances are very close to the optimal (DP) controller: For theUDDS drive cycle, UMD_IPC saved 3.95% fuel, while the DPcontroller saved 4.05%; for the UNIF01 drive cycle, UMD_IPC


Fig. 17. Performance comparison on fuel consumption. (a) Total fuel consumption with Pl = 1000 W. (b) Fuel saving with Pl = 1000 W.

Fig. 18. Fuel efficiency comparisons on different time steps used by UMD_IPC.


saved 3.29% fuel, while the DP controller saved 3.47%; andfor the LA 92 drive cycle, UMD_IPC saved 3.05% fuel, whilethe DP controller saved 3.15%. These results demonstrate thatUMD_IPC is able to realize good fuel-economy improvementsover the existing conventional control strategy in all drivecycles, and on some drive cycles, it can give near-optimalperformances.

We also studied the fuel efficiency with three different timesteps, i.e., Δt = 1, 3, and 5 s, and the results are shown inFig. 18. It appears that UMD_IPC gave similar performancesfor all three time steps within the precision of two digits. Toanalyze the computational efficiency of UMD_IPC, the follow-ing experiment is conducted using a computer that has a 3-GHzIntel Core 2 Duo processor and a 4-GB DDR3 random accessmemory. We applied UMD_IPC to the drive cycle UDDSwith a time step of Δt = 3 s for neural network predictionand update the optimal battery power Ps every second. Theentire simulation was completed in 300 s. Since UDDS has1370 s, the time takes to calculate the optimal power setting,which includes the time needed for neural network predictionof roadway types, is 0.22 s on the average.

VI. CONCLUSION

The authors have presented an intelligent vehicle powercontroller, namely, the UMD_IPC, which has been devel-oped through machine learning of optimal control parame-ters with respect to 11 FS road types and traffic conges-tion levels. UMD_IPC contains a neural network, namely,the NN_RT&TC, designed and trained for in-vehicle pre-diction of 11 different roadway types and traffic-congestionlevels. We have also presented a feature extraction algorithmto extract effective features from vehicle speed segments asthe input to NN_RT&TC and showed the importance ofthe two parameters, namely, ΔZ, which is the signal win-dow size, and Δt, which is the prediction step, with respectto the accuracy of the prediction results of NN_RT&TC.An offline machine-learning algorithm was proposed togenerate a knowledge base that contains the optimal controlparameters for all 11 standard FS drive cycles. During anonline control process, UMD_IPC, which is the proposed on-line controller, applies the appropriate optimal control param-eters in the knowledge base to the current road type predictedby the neural network NN_RT&TC to generate optimal powerto be charged to or discharged from the battery and the optimalengine power. UMD_IPC has been fully implemented in a con-ventional vehicle model, Ford Taurus, in the PSAT simulationenvironment and tested on the 11 drive cycles provided by thePSAT library. Our simulation results show that UMD_IPC gavea performance within less than 0.15% of the optimal DP, whichis an offline optimal controller, for eight drive cycles, and lessthan 0.25% for the remaining drive cycles. The maximum fuelsaved in comparison to the Ford Taurus controller is 3.95%.UMD_IPC saved more than 2% fuels in six drive cycles andmore than 1% fuel in eight drive cycles. In conclusion, the pro-posed machine-learning technique, combined with roadway-type prediction, is an effective approach in real-time intelligentvehicle power management. Furthermore, the proposed vehiclepower control technology does not require any changes to the

drive train and is therefore easy to implement in an existingconventional vehicle configuration. Currently, we are develop-ing machine-learning technologies with applications to hybridvehicle power-management systems. We anticipate that moresignificant fuel reduction will be achieved in hybrid vehiclepower systems.

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Jungme Park received the B.S. degree in statisticsfrom Korea University, Seoul, Korea, in 1989 and theM.S. and Ph.D. degrees in computer science from theUniversity of Alabama, Tuscaloosa, in 2001.

She is currently a Research Scientist with theDepartment of Electrical and Computer Engineering,University of Michigan, Dearborn. Her current re-search interests include computer vision, optimiza-tion, and vehicle power management of conventionaland hybrid electric vehicles.

Zhihang Chen received the Ph.D. degree in appliedmathematics from Peking University, Beijing, China,in 2000.

He is currently a Research Scientist with the Uni-versity of Michigan, Dearborn. His research interestsinclude machine learning and intelligent systems,with applications to vehicle power management.

Leonidas Kiliaris was born in Trenton, MI, in1983. He received the B.Sc.Eng. and M.Sc.Eng. de-grees in electrical engineering from the University ofMichigan, Dearborn, in 2006 and 2009, respectively.

He is currently conducting research with the Uni-versity of Michigan in power management of light-and heavy-duty conventional and hybrid electricvehicles.

Ming L. Kuang received the B.S. degree in mechan-ical engineering from the South China Universityof Technology, Guangzhou, China, in 1982 and theM.S. degree in mechanical engineering from theUniversity of California, Davis, in 1991.

Since 1991, he has been with the Ford MotorCompany, Dearborn, MI, in various engineering po-sitions. He became a Technical Expert in 2000 forthe Escape Hybrid vehicle program and played acritical role in the development and implementationof the vehicle/powertrain control system, delivering

the first Ford Escape Hybrid and Mercury Mariner Hybrid vehicles to pro-duction. He is currently a Technical Leader in vehicle controls in researchand advanced engineering. His primary research interests include vehiclecontrol architecture, vehicle control system development, and implementationmethodologies, as well as advanced vehicle control algorithm development forhybrid and fuel-cell vehicles. He is the author or coauthor of 20 technical papersin various engineering journals and conferences. He is the holder of 36 U.S. andinternational patents.

Mr. Kuang was the recipient of the 2005 Henry Ford Technology Award andthe Society of Automotive Engineers 2007 Henry Ford II Distinguished Awardfor Excellence in Automotive Engineering.

M. Abul Masrur (M’84–SM’93) received the Ph.D.degree in electrical engineering from Texas A&MUniversity, College Station, in 1984.

Between 1984 and 2001, he was with Ford Re-search Laboratories and then joined the U.S. ArmyRDECOM-TARDEC, Warren, MI, where he hasbeen involved in various vehicular electric power-system architecture concepts, electric power man-agement, and inverter fault diagnostics.

Dr. Masrur was the recipient of the Best Auto-motive Electronics Paper Award from the IEEE

Vehicular Technology Society for his transactions papers in 1998 and the2006 Society of Automotive Engineers Environmental Excellence in Trans-portation Award. He is the current Chair of the Motor Subcommittee withinthe IEEE Power and Energy Society. He served as an Associate Editorfor the IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY from 1999to 2007.

Anthony M. Phillips received the B.A. degree(magna cum laude) in physics from Gustavus Adol-phus College, St. Peter, MN, in 1990 and the M.S.and Ph.D. degrees in mechanical engineering—control systems from the University of California,Berkeley, in 1993 and 1995, respectively.

Upon completing his study, he joined the FordMotor Company, Dearborn, MI as a Product Devel-opment Engineer. He was appointed as a TechnicalExpert when he joined the Research and AdvancedEngineering staff in 1998. In his current position as

a Senior Technical Leader, he has responsibility for Ford’s advanced vehiclecontrol system development for hybrid and fuel-cell electric vehicles. He isa member of the Editorial Board of the International Journal of AlternativePropulsion. His research interests include vehicle energy management, distrib-uted system control, and control system development tools and methods. He isthe holder of 29 U.S. and international patents in automotive controls.

Dr. Phillips is a member of the Society of Automotive Engineers and theAmerican Society of Mechanical Engineers.

Yi Lu Murphey (SM’97) received the Ph.D. de-gree in computer engineering from the University ofMichigan, Ann Arbor, in 1989.

She is currently a Professor and the Chair of theDepartment of Electrical and Computer Engineering,University of Michigan, Dearborn. Her current re-search interests include machine learning, computervision, and intelligent systems, with applications toengineering diagnostics, vehicle power management,and robotic vision systems.

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