ieee transactions on vehicular technology, vol. xx,...

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. XX, NO. XX, XXX 2018 1

A Hybrid Method Combining Markov Predictionand Fuzzy Classification For Driving Condition

RecognitionHaiming Xie, Guangyu Tian, Guangqian Du, Yong Huang, Hongxu Chen, Xi Zheng, and Tom H. Luan

Abstract—Driving condition adaptive control is an effectivevehicle fuel-saving technique, and the key challenge lies inimproving the recognition accuracy of current driving condition.The state-of-the-art approach is based on recognizing historicaldriving data with a fixed length sliding window to detect currentdriving condition. However, few research has been conducted todirectly recognize the occurring micro-trip (a speed time seriesbetween two starts). That is because at the beginning stageof an occurring micro-trip, its known speed time series is tooshort to be correctly recognized. In this work, we addressedthis issue by proposing a hybrid method for the occurringmicro-trip recognition, and two efforts are made to improverecognition accuracy. Firstly, a hybrid recognition procedureis proposed which combines Markov chain prediction modeland fuzzy classification model. Secondly, a statistic approach isproposed to estimate the best time to switch between above twomodels to achieve higher accuracy in detecting current drivingcondition. Our evaluation results on real-world driving data showthat our proposed solution has better accuracy than the state-of-the-art approach.

Index Terms—Driving condition recognition, hybrid recogni-tion, micro-trip, Markov prediction, fuzzy classification.

TABLE I. NotationAccuracy(k) the average accuracy of (prediction or recognition)

results within the first k sampling periodsCn the real type value of the nth micro-tripCPn the type prediction result of the nth micro-tripCP (k) the adopted type prediction result at the kth sam-

pling periodCR(k) the type recognition result of the occurring micro-

trip at the kth sampling periodC(k) the HDCR based type result of the occurring

micro-trip at the kth sampling periodF (k) the feature vector extracted from the sample S(k)I(k) the integrity degree of the occurring micro-trip at

the kth sampling periodS(k) the recognition sample at the kth sampling periodt15 the time of exceeding 15 km/htn,switching the time of switching from the prediction result to

the recognition resultvmax the vehicle maximum speed

Copyright (c) 2015 IEEE. Personal use of this material is permitted.However, permission to use this material for any other purposes must beobtained from the IEEE by sending a request to [email protected].

Haiming Xie is with the State Key Laboratory of Automotive Safety andEnergy, Tsinghua University, Beijing, China, e-mail: ([email protected]).

Guangyu Tian, Guangqian Du, Yong Huang and Hongxu Chen are with theState Key Laboratory of Automotive Safety and Energy, Tsinghua University,Beijing, China.

Xi Zheng is with Macquarie University, Australia and Tom H. Luan is withXidian University, China.

Manuscript received XXX, XX, 2017; revised XXX, XX, 2018.

I. INTRODUCTION

IN recent years, a variety of multi-energy-source vehicles,such as hybrid electric vehicles and extended-range electric

vehicles (E-REVs), have become attractive fuel-saving vehi-cles in the process of transition from traditional fuel vehicles topure electric vehicles [1–4], but there still remains potentialfor further improvement in their fuel economy. In fact, thefuel economy of those fuel-saving vehicles is very sensitive todriving conditions [5–9]. Therefore, driving condition adaptivecontrol is an effective fuel-saving technique to achieve furtherimprovement [10, 11]. In the fuel-saving technique, controlparameters are normally optimized for different types ofdriving conditions obtained from clustering results. For on-lineadaptive control, a specific set of control parameters is selectedaccording to the current driving condition type recognized.Obviously, the fuel-saving performance of this technique de-pends on the recognition accuracy of current driving condition.In previous researches, a micro-trip is usually used to indicatedriving condition in its covered time period [12]. A micro-tripis defined as a speed time series between two vehicle stops [13]or a speed time series between two vehicle starts [14]. Inthis paper, we use the second definition. The data of threeconsecutive complete micro-trips is depicted in Figure 1. As

0 50 100 150 2000

10

20

30

40micro−trip

1micro−trip

2micro−trip

3

t / s

v /

km

⋅ h

−1

Fig. 1. The data of three consecutive micro-trips (v: vehiclespeed; t: sampling time)

shown in Figure 1, how to improve the recognition accuracyof the occurring micro-trip is a key issue. Figure 2 depicts anoccurring micro-trip which includes two parts: (1) the knownspeed time series; (2) the future speed time series. Duringeach sampling period, only the known speed time series can beused to extract driving features, which brings a big challengeto ensure the recognition accuracy of an occurring micro-trip.This issue becomes more problematic at the beginning stage


350 360 370 380 390 400 410 420 430 440 450

t / s

0

10

20

30

40

v /

km·h

-1

vn,m

tn,m

The occurring micro-trip

The known speed time series The future speed time series

Fig. 2. The illustration of the occurring micro-trip (v: vehicle speed; t: sampling time, t = k · Ts; Ts: sampling interval; tn,m:current sampling time; vn,m: current sampling speed)

150 200 250 300 350 400 450 500

t / s

0

20

40

60

v /

km·h

-1

Recognition object based on the

sliding-window method

Fixed size window (150 s)Current

sampling time

Fig. 3. The recognition object based on the sliding-window method (v: vehicle speed; t: sampling time)

150 200 250 300 350 400 450 500

t / s

0

20

40

60

v /

km·h

-1

Recognition object based on the

proposed HDCR method

Current

sampling timeSize-varied window

Fig. 4. The recognition object based on the proposed HDCR method (v: vehicle speed; t: sampling time).

of an occurring micro-trip as the known speed time series istoo short to be correctly recognized.

Most existing methods, which are used to provide theinformation of driving condition, can be classified into threecategories: (1) global position system (GPS) or intelligenttransportation system (ITS) based driving condition prediction(DCP) methods [15–17]; (2) prediction model based DCPmethods [18–22]; and (3) historical driving data cluster anal-ysis based driving condition recognition (DCR) methods [6,13, 23]. In the first category of DCP methods, the accurategeographic information and traffic information provided byGPS and ITS can greatly help to reduce the uncertaintyof DCP results. In 2005, Musardo et. al [24] used GPSdata, vehicle speed and elevation data to predict future roadloads. Furthermore, based on GPS, Yang et. al [25] builta spatial domain trip model to predict the comprehensiveinformation of future driving conditions, including road grade,traveling time, total distance, etc. However, due to lack ofITS infrastructure and the existence of urban corners withoutGPS signal, this first category DCP methods is difficult to bepopularized in the near future [26, 27]. The second categoryof methods tends to establish various prediction models based

on historical driving data, rather than based on GPS or ITSinformation, and they tend to directly predict future vehiclespeed sequence or demand power sequence instead of futuredriving conditions. In this second category of DCP methods,Markov chain model is commonly used, such as using itto predict future demand powers [28–30], and using it topredict the types of future micro-trips [31]. However, theprediction accuracy relies on whether the statistic rules of real-time driving condition are consistent with the rules definedby the transition probabilities in the Markov chain model.Instead of prediction, the last category of methods utilizesfeature extraction and classification techniques to recognizethe driving condition type, and then assumes that this typeremains unchanged in the future few seconds [10, 32–34]. Inthis category of DCR methods, the specific recognition objectis a driving segment [35, 36] or a complete micro-trip [34],where a driving segment is a speed time series divided fromthe latest driving data by using a fixed size sliding window. Asshown in Figure 3, taking the 150 s window as an example,the driving segment is composed by the speed time series forthe latest 150 seconds. In fact, a driving segment is commonlytaken as the specific recognition object in the existing DCR


methods. Since a driving segment may include more than onemicro-trips [34], the features of the occurring micro-trip maybe hidden in the process of statistics. Therefore, when taking adriving segment as the recognition object, there is an uncertainlatency before correctly recognizing the type of the occurringmicro-trip. Similarly, when taking a complete micro-trip as therecognition object, the recognition results always represent thetype of the last micro-trip, rather than the type of the occurringmicro-trip. Based on the above analysis, it is better to treatthe known speed time series of the occurring micro-trip as thespecific recognition object. However, few research has beenconducted in this area due to that the occurring micro-trip isdifficult to be correctly recognized at its beginning stage.

To solve this problem, we propose a hybrid DCR (HDCR)method. As shown in Figure 4, our proposed HDCR methoddirectly takes the known speed time series of the occurringmicro-trip as the recognition object. In order to improve theDCR accuracy, two efforts are made in this method. (1)Toovercome the disadvantage of low confidence level of therecognition result at the beginning stage, a hybrid recognitionprocedure combining micro-trip prediction and recognition isproposed. At the beginning stage of the occurring micro-trip,its type value is set to the prediction result. With the increaseof the confidence level of the recognition result, its type valueis switched from the prediction result to the recognition result.(2) Based on statistic analysis on the real-world driving data,an approach is proposed to estimate the confidence level ofthe recognition result in real time to determine the best timeof implementing the above-mentioned switching.

The reminder of this paper is organized as follows. SectionII defines three driving condition types and designs the fuzzyclassifier based driving condition recognition model. In Sec-tion III, the hybrid recognition procedure of the HDCR methodis described. In Section IV, a prediction model for drivingcondition type is built based on the Markov chain. In SectionV, an approach of the best switching time determination isproposed. In Section VI, the performance simulation results ofthe HDCR method are analyzed, and conclusions are drawnin Section VII.

II. DRIVING CONDITION TYPE DEFINITION AND TYPERECOGNITION MODEL DESIGN

A. Real-world driving data collection and driving conditiontype definition

In order to collect real word-driving data, a remote monitor-ing system (RMS) for new energy vehicles is designed by ourgroup. As shown in Figure 5, in this RMS, the vehicle terminalcontroller (VTC) equipped on the vehicle collects vehicledriving data, and then sends the driving data to the backgrounddatabase by using the general packet radio service (GPRS)network and the communication base station. In addition, inorder to avoid losing data, there is a security digital memorycard in the VTC to backup the driving data. Based on thisRMS, 46-day real-world driving data of an 18-tons E-REVcity bus are collected in the year of 2015. The samplinginterval of those driving data is 1 second. In this work, thefirst 40-day driving data are taken as the training samples,

and the remaining 6-day driving data are taken as the testingsamples. According to the definition of micro-trip, 13812 validmicro-trips are extracted from the first 40-day driving data.This system and generated data are used across the paper toillustrate the research challenges, our contribution, and for theevaluation.

Sever and database

GPRS

Internet

Internet

Monitoring Clients

City bus with the VTC

Communication base stationSatellite

GPS

Fig. 5. Remote monitoring system for new energy vehicles.(GPS: global position system; GPRS: general packet radioservice; VTC: vehicle terminal controller).

As a precondition of driving condition recognition, drivingcondition types have been well defined [33, 37]. In ourprevious study [14], a density-based distribution methodologyis proposed for clustering driving data. On the basis of suchprevious study, the feature vector used to characterize micro-trip has been optimized in our following empirical study. Byusing the above-mentioned distribution methodology, clusteranalysis on the 13812 micro-trips has been conducted. As aresult, 3 types of micro-trips with different fuel consumptionlevels were obtained as the generic optimal results. In thiswork, those optimal clustering results of micro-trips are usedto give a guide for defining the driving condition type. There-fore, three driving condition types are defined to characterizeurban road driving environment, which are named congested,general, and smooth, respectively. Accordingly, three micro-trip types numbered 1, 2, 3 are defined to indicate the abovethree driving condition types.

B. Fuzzy classifier model

Based on the fuzzy theory, a fuzzy classifier is designedto achieve two purposes: (1) recognizing the type of theoccurring micro-trip at each sampling period; (2) classifyingthe historical micro-trip samples to extract the general rulesof micro-trip types.

For the first purpose, the type recognition process of theoccurring micro-trip based on the fuzzy classifier is shownin Figure 6. There are four key steps in this process, in-cluding feature extraction, fuzzification, fuzzy reasoning anddefuzzification. Firstly, based on the known speed time seriesof the occurring micro-trip, the two driving features namelythe maximum speed (vmax) and the time of speed exceeding15 km/h (t15) are extracted to build the inputs for the fuzzyclassifier at each sampling period. Secondly, the fuzzificationof the inputs is implemented based on the input membershipfunctions and the output membership functions, which areshown in Figure 6(a) and Figure 6(b), respectively. For theCongested type of micro-trips, their maximum speed is less


than 20 km/h, and their time of speed exceeding 15 km/h isless than 10 s. For the General type of micro-trips, their vmaxis more than 10 km/h, but less than 50 km/h, and their t15 isbetween 0 s and 50 s. For the Smooth type of micro-trips,their vmax is more than 20 km/h, and their t15 is more than10 s. Thirdly, the Mamdani fuzzy reasoning is carried out.The rule base are shown in Figure 6(c). Lastly, the maximumsubordination principle is employed for the defuzzification tospecify a type recognition result denoted by CR(k).

We have defined the input and output membership functionsbased on our empirical study. Based on our results, theclassification results based on the above membership functionskeep consistent with the above-mentioned clustering results.However, if we define the membership functions lower orhigher than the above functions, the misclassifying rate is notzero. For instance, the possibility of misclassifying Congestedtype into General type is increased by 3.41%, and misclassi-fying General type into Smooth type is increased by 31.11%,if we define the membership functions lower than these threefunctions as follows: (1) for the Congested type of micro-trips, their vmax is less than 16 km/h, and their t15 is lessthan 8 s; (2) for the General type of micro-trips, their vmaxis more than 8 km/h, but less than 45 km/h, and their t15 isbetween 0 s and 45 s; (3) for the Smooth type of micro-trips,their vmax is more than 16 km/h, and their t15 is more than8 s. Similarly, the possibility of misclassifying General typeinto Congested type is increased by 4.21%, misclassifyingSmooth type into General type is increased by 11.52%, ifwe define the membership functions higher than these threefunctions as follows: (1) for the Congested type of micro-trips, their vmax is less than 24 km/h, and their t15 is lessthan 12 s; (2) for the General type of micro-trips, their vmaxis more than 12 km/h, but less than 55 km/h, and their t15 isbetween 0 s and 55 s; (3) for the Smooth type of micro-trips,their vmax is more than 24 km/h, and their t15 is more than12 s. The results justify our choice of these three membership

functions.For the second purpose, the classification process is similar

to the recognition process depicted in Figure 6, and the onlydifference is that the recognition object is changed fromthe known speed time series of the occurring micro-trip todifferent historical micro-trips.

Based on the designed fuzzy classifier, 13812 micro-tripsare classified into three classes. Using a 2% sampling ratio,we randomly select micro-trips in each class, and align theirpeak speeds in Figure 7(a). Obviously, the micro-trips of thefirst type have the smallest maximum speed, and the micro-trips of the third type have the longest time of speed exceeding15 km/h, which are consistent with the fuzzy rule base definedin Figure 6(c).

Based on the classification results of 13812 micro-trips,through randomly selected samples from each class of micro-trips and then splicing them together, three types of typicaldriving conditions are generated. As shown in Figure 7(b),for the first driving condition, its maximum speed is verysmall, and its character is congestion. For the second drivingcondition, its maximum speed is moderate. And for the thirddriving condition, its maximum speed is very high, and itscharacter is rapid acceleration and rapid deceleration.

C. On-line recognition

The on-line recognition for the current driving conditionis implemented based on the fuzzy classifier. Supposing theoccurring micro-trip is the nth micro-trip, the mth point inthe occurring micro-trip is the kth sampling point from thevery first start of the vehicle to the present, the current speedoccurs at the mth point, the specific recognition object denotedby S(k) is defined as following,

S(k) = (vn,1, vn,2, · · · , vn,m) (1)

Recognition result

Fuzzification

Inputs Fuzzy classification based type recognition

maxv

15t

Congested General Smooth

Congested Congested — —

General — General Smooth

Smooth — Smooth Smooth

(s)Micro-trip type

(km/h)

Mamdani based fuzzy reasoning

Defuzzification

(a) Input membership functions (c) Rule base

Occurring micro-trip

Feature extraction

Micro-trip type

)(kCR

(b) Output membership functions

Fig. 6. The type recognition process of the occurring micro-trip based on the fuzzy classifier (t15: time of speed exceeding 15km/h, vmax: maximum speed, CR(k): micro-trip type recognition result at the kth sampling period)


0 100 200 300 400

t / s

0

20

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v /

km·h

-1

The 1st type of micro-trips

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t / s

0

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v /

km·h

-1

The 2nd type of micro-trips

0 100 200 300 400

t / s

0

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v /

km·h

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The 3rd type of micro-trips

(a) Classification results of 13812 micro-trips

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40

60

v /

km

⋅ h

−1

t / s

The 1st type of driving condition

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20

40

60

v /

km

⋅ h

−1

t / s

The 2nd type of driving condition

0 200 400 600 8000

20

40

60

v /

km

⋅ h

−1

t / s

The 3rd type of driving condition

(b) Three types of driving conditions

Fig. 7. Classification results and corresponding driving conditions (v: vehicle speed; t: sampling time, t = k ·Ts; Ts: samplinginterval with 1 second)

where vn,i is the speed value of the ith point in the occurringmicro-trip (i = 1, 2, · · · ,m). Using tn,m to represent theoccurrence time of vn,m, the relationship between tn,m andk is tn,m = k · Ts, where Ts is the sampling interval with 1second.

For describing the characteristics of the recognition objectS(k), a feature vector needs to be constructed. In exist-ing researches, maximum speed [38–42] and percentage oftime in a certain speed interval (e.g., 0-15 km/h and 15-30 km/h) [39, 43] are the most common driving features.Considering that knowing the length of the occurring micro-trip is the precondition of time percentage calculation, in thiswork, we directly calculate the time in a certain speed interval.As a result, we choose the maximum speed and the time ofexceeding 15 km/h as the driving features, and they are definedas following,

vmax = maxi=1,2,··· ,m

{vn,i} (2)

t15 = n15 · Ts (3)

where n15 is the times of speed exceeding 15 km/h in theknown speed time series. Then the feature vector in the kthsampling period (F(k)) is constructed.

F(k) = (vmax, t15) (4)

Based on the feature vector and a classification model, thetype recognition result (CR(k)) of the occurring micro-trip atthe kth sampling period is renewed by,

CR(k) = classifier(F(k)

)(5)

where classifier (·) is the fuzzy classifier model, and the letterR is the abbreviation of Recognition.

Obviously, the confidence level of this type recognition re-sult CR(k) depends on the integrity degree of the known speedtime series. The smaller the integrity degree, the lower theconfidence level of CR(k). Such integrity degree representedby I(k) is defined as following,

I(k) =length

(S(k)

)ln

(6)

where length (·) is a function used to count the number ofelements in S(k), and ln represents the number of samplingpoints in the occurring micro-trip. When I(k) = 1, therecognition result CR(k) equals the real type value of theoccurring micro-trip, which is represented by notation Cn(Cn ∈{1,2,3}).

III. HYBRID DRIVING CONDITION RECOGNITION METHODDESIGN

To solve the problem that the confidence level of CR(k)is low when I(k) is small, the HDCR method is proposedin this section. As shown in Figure 8, at the beginning ofthe occurring micro-trip, since the known speed time seriesis too short, the HDCR method turns to predict the type ofthe occurring micro-trip instead of recognizing its type. Suchprediction is only implemented at the moment of detectingthe start point of the occurring micro-trip, and the prediction


350 360 370 380 390 400 410 420 430 440 450

t / s

0

10

20

30

40

50

v /

km·h

-1

tn,switching

The first peak occurs at tn,i

The moment of firstly

detecting a peak (tn,i+1

)

The range of implementing dynamic recognition to obtain CR

(k) CP

n

The moment of making a

prediction to obtain

C(k)= CP

(k) = CP

n C(k)= C

R(k)

Fig. 8. Description of the hybrid recognition procedure for the proposed HDCR method (v: vehicle speed; t: sampling time,t = k · Ts; Ts: sampling interval with 1 second; tn,i: the sampling time of the ith point in the nth micro-trip; CPn : thetype prediction result for the nth micro-trip; CR(k): the recognition result for the nth micro-trip in the kth sampling period;tn,switching: prediction result and recognition result switching time; C(k): the final type result based on the HDCR method)

result is represented by CPn . The letter P is the abbreviationof Prediction. In another word, during the time intervalcovered by the occurring micro-trip, the type prediction resultmaintains as a constant. Specifically, the type prediction resultat the kth sampling period CP (k) is

CP (k) = CPn (7)

With the increase of sampling points, a series of speed peakswill appear in the known speed time series. Supposing thefirst speed peak occurs at tn,i, the HDCR method can detectthis peak at the next sampling period, tn,i+1. After tn,i+1, theHDCR method starts to dynamically recognize the type forthe occurring micro-trip at each sampling period until it ends.As a result, there are two type results after tn,i+1, includingthe prediction result and the recognition result. Therefore, aswitching time needs to be determined for switching the typevalue of the occurring micro-trip from the prediction result tothe recognition result. The principle is that after the determinedswitching time the confidence level of the recognition result(CR(k)) is higher than that of the prediction result (CP (k)).Based on the switching time (tn,switching), the final typevalue of the occurring micro-trip at the kth sampling periodrepresented by C(k) is given by,

C(k) =

{CP (k), if k · Ts < tn,switching;

CR(k), others.(8)

In addition to this hybrid recognition procedure, buildinga high accuracy type prediction model and proposing anapproach to determine the best switching time tn,switching arealso the key techniques to improve the recognition accuracyof the occurring micro-trip.

In order to discuss the driving condition prediction or recog-nition accuracy in the following section, a general definitionfor the accuracy in the first k sampling periods is defined asfollowing,

Accuracy(k) =CCN(k)

k(9)

where CCN(k) is the cumulative correct number (CNN) oftype prediction or recognition results between the first and thekth sampling periods, and a type result is considered as correctonly when it equals to the real type value of the correspondingmicro-trip. For example, when discussing the accuracy ofprediction results, the Accuracy(k) means the percentage ofcorrect results in the sequence {CP (1), CP (2), · · · , CP (k)}.

IV. MARKOV CHAIN MODEL BASED TYPE PREDICTION

In the HDCR method, the type prediction result of theoccurring micro-trip (CPn ) is obtained by using a Markovchain model. As shown in Figure 9, the types of existingmicro-trips in a speed time series are numbered consecutivelyby C1, C2, · · · , Cn−1, · · · ∈ {1, 2, 3}. The sequence C ={C1, C2, · · · , Cn−1

}can be modeled as a Markov chain [31].

In this work, a first-order Markov chain model is built todescribe the rules of such sequence.

For any two adjacent micro-trips, we use the notation Cprevto represent the type of the previous one, and use Ccur torepresent the type of the current one. By using the three kindof micro-trip types to define the state space, the first-orderMarkov chain model is built based on an array of transitionprobabilities as following

P(Ccur = y | Cprev = x) = tx,y, x, y ∈ {1, 2, 3} (10)

Those transition probabilities are estimated by using frequencyanalysis.

tx,y =mx,y∑3y=1mx,y

, x, y ∈ {1, 2, 3} (11)

where mx,y is the number of occurrences of observingCprev = x and Ccur = y in historical driving data. Basedon this Markov chain model and the real type value of theprevious micro-trip Cn−1, the type with the maximum tran-sition probability from Cn−1 is chosen as the type predictionresult CPn for the occurring micro-trip.

CPn = arg maxy∈{1,2,3}

P(Ccur = y | Cprev = Cn−1) (12)


Type prediction

？1nCP

nC1C 2C 3C

Transition probability

Classification results of the 13812 micro-trips

)|( xCyCP prevcur

Fig. 9. The type prediction process of the occurring micro-trip based on the Markov chain model (v: vehicle speed; t: samplingtime, t = k · Ts; Ts: sampling interval with 1 second; Cn−1: the real type value of the (n − 1)th micro-trip; CPn the typeprediction result of the nth micro-trip)

0 5 10 15 20 25 30 35 40

Cycle number

0

20

40

60

80

100

Acc

ura

ccy(k

f) /

%

Average prediction accuracy on each cycle

E(Accuraccy(kf),Markov)

Fig. 10. Accuracy analysis for the Markov chain model based type prediction results (kf : the number of the final samplingperiod in a cycle; Accuracy(kf ): the accuracy of type results in the first kf sampling periods of a cycle)

where P(·) is the probability of event (·).According to the classification results of the 13812 micro-

trips in Section 3, the transition matrix is obtained as follow-ing,

M = (tx,y)3×3 =

0.5680 0.1844 0.24760.1392 0.2876 0.57320.0958 0.2108 0.6934

(13)

As a result, the type prediction model in Eq. 12 for theoccurring micro-trip is simplified.

CPn =

{1, Cn−1 = 1;

3, Cn−1 = 2, 3;(14)

For the first micro-trip, its type prediction result is set to 1 bydefault, namely CP1 = 1.

Taking each day driving data as a cycle, the expected valueof the type prediction accuracy on a cycle based on the Markovchain model in Eq. 14 can be obtained as following,

E(Accuraccy(kf ),Markov

)=

∑3y=1

(P(CPn = y | Cn = y) · N̄y · L̄y

)∑3y=1

(N̄y · L̄y

) (15)

where L̄y is the average number of sampling points in the yth(y ∈ 1, 2, 3) type of micro-trips, N̄y is the average occurrencenumber of the yth type of micro-trips in each cycle, kf is thenumber of the final sampling point in a cycle, and P(CPn =y | Cn = y) represents the probability of correct predictionfor the yth type of micro-trip.

P(CPn = 1 | Cn = 1) = P(Cn−1 = 1 | Cn = 1)

=m1,1∑3

x=1mx,1;

P(CPn = 2 | Cn = 2) = 0;

P(CPn = 3 | Cn = 3) = P(Cn−1 = 2 | Cn = 3)

+P(Cn−1 = 3 | Cn = 3)

=∑3

x=2mx,3∑3x=1mx,3

;

(16)

Also according to the classification results of the 13812micro-trips, we obtain P(CPn = 1 | Cn = 1) = 0.5678,P(CPn = 3 | Cn = 3) = 0.2210+0.6934 = 0.9144, N̄1 = 70,N̄2 = 77, N̄3 = 199, L̄1 = 60, L̄2 = 64, and L̄3 = 89. Basedon the Eq. 15, we have E

(Accuraccy(kf ),Markov

)= 69%.

Testing the prediction model in Eq. 14 on the first 40-daydriving data, the values of Accuracy(kf ) on each cycle areshown in Figure 10, which are distributed near the expectedvalue E

(Accuraccy(kf ),Markov

).


V. THE BEST SWITCHING TIME DETERMINATION

The confidence level of the recognition result CR(k) de-pends on the known speed time series integrity degree I(k)defined in Eq. 6. This confidence level is a key index todetermine when to switch the type value C(k) from theprediction result CP (k) to the recognition result CR(k).However, the fact is, during the driving process, the specificlength of the occurring micro-trip ln can not be obtained untilit ends, which leads to that the accurate value of I(k) can notbe determined most of the time. To solve this problem, insteadof directly calculating the I(k), we tend to determine a timethreshold tn,switching , which implies that the confidence levelof the recognition result exceeds a given value. In addition,we link the confidence level of the recognition result to theprobability of an event— F : the type recognition result basedon the known speed time series equals the real type value ofthe occurring micro-trip.

Referring to the way of dividing the process in the rain-flow-counting method [44], a process of transition from a peakspeed point to the last point, whose speed is still less than thepeak speed in a micro-trip, is defined as a generalized speedvalley (GSV). In each GSV, two time points—tn,α1

and tn,α2,

are counted, which are defined as following.• tn,α1

: the time of speed reducing to no more than 15km/h for the first time during a GSV.

• tn,α2 : the time of speed reducing to no more than γ ·vmaxfor the first time during a GSV.

where γ is a scale factor between 0 and 1, vmax is the peakspeed in the current GSV, and vmax is also the peak speed ofthe known speed time series of the occurring micro-trip. Thereason of choosing 15 km/h as the threshold for determiningtn,α1

is that the second driving feature is the time of speedexceeding 15 km/h (t15). In another word, the tn,α1

is designedto increase of the confidence level of the second driving featuret15, which is extracted from the known speed time series ofthe occurring micro-trip. In addition, the tn,α2 is designed toincrease of the confidence level of both two driving features,including t15 and vmax. The smaller the γ, the larger theprobability of that after tn,α2

there will be no new GSV in thefuture speed time series of the occurring micro-trip, and thehigher the confidence level of vmax and t15. And if the currentGSV is the last GSV of the occurring micro-trip, the value of

vmax equals to the maximum speed of the whole occurringmicro-trip. Based on tn,α1

and tn,α2, the tn,switching is defined

as following.

tn,switching =tn,α2

, if vmax ≤ 15;

max{tn,α1

, tn,α2

}, if vmax > 15; tn,α1

, tn,α2can be

detected in the same GSV;

+∞, others.

At the beginning of the occurring micro-trip, the tn,α1

and tn,α2are set to NULL, and they are not assigned until

the corresponding time points are detected. For example,when γ=0.5, the determination process of tn,switching in threecases are introduced: (1) : vmax ≤ 15 km/h. As shownin Figure11(a), the tn,α2 is determined by the occurrence timeof point 2, because the point 2 is in a GSV and its speedvalue is no more than half of the maximum speed vmax whichoccurs at point 1. And since vmax ≤ 15 km/h, we obtaintn,switching = tn,α2

. (2) : vmax > 15 km/h and tn,α2< tn,α1

.As shown in Figure 11(b), tn,α2 and tn,α1 are detected bythe occurrence time of point 2 and 3, respectively. Settingtn,switching = tn,α1

is necessary for accurately calculatingthe value of t15, since the speed at point 2 is greater than 15km/h but the t15 is defined as the time of speed exceeding 15km/h. (3) : vmax > 15 km/h and tn,α2 ≥ tn,α1 . As shown inFigure 11(c), there are five GSVs in the micro-trip. During thefirst GSV, the occurrence time of point 2 is assigned to tn,α1

,but tn,α2

is NULL. Therefore, tn,switching keeps the +∞ inthis GSV. During the last GSV both the tn,α1

and tn,α2can

be detected, and tn,switching is determined by the occurrencetime of point 5.

On the one hand, a small γ leads to a large tn,switching ,which then leads to a large micro-trip integrity degree, whichin turn leads to a large probability P(F). On the other hand,a large tn,switching means a big delay of using the recognitionresult CR(k) to revise the prediction result CP (k). In otherwords, the action range ratio of recognition results, which isdefined as 1 − (tn,switching − tn,1)/(ln × Ts) (tn,1 is thesampling time of the first point in the nth micro-trip, ln isthe number of sampling points in the nth micro-trip, and Tsis the sampling interval with 1 second), is small. Therefore, we

0

t / s

0

5

10

15

v /

km·h

-1

1

2

tn,α

2

(a) vmax ≤ 15 km/h

0

t / s

0

10

20

30

40

v /

km·h

-1

1

3

2

tn,α

1

tn,α

2

(b) vmax > 15 km/h & tn,α2 < tn,α1

0

t / s

0

10

20

30

v /

km·h

-1 1

3

24

5

tn,α

1

tn,α

2

(c) vmax > 15 km/h & tn,α2 ≥ tn,α1

Fig. 11. Time threshold detection in three cases (v: vehicle speed; t: sampling time, t = k · Ts; Ts: sampling interval with 1second; vmax: maximum vehicle speed)


0.1 0.3 0.5 0.7 0.9

γ

0.6

0.8

1

P(F

)

30

40

50

Action

ra

nge

ra

tio

/ %

Fig. 12. The impacts of γ on the P(F) and on the action range ratio of the fuzzy classifier based recognition result (P(F): theprobability of event F at t = tn,switching; Event F : the type recognition result based on the known speed time series equalsthe real type value of the occurring micro-trip)

0 2 4 6 8

t / h

0

20

40

60

80

100

Acc

ura

cy o

f re

sult

s /

%

Cycle 1

Cycle 2

Cycle 3

Cycle 4

Cycle 5

Cycle 6

85% level

(a) The HDCR method based recognition accuracy

0 2 4 6 8

t / h

0

20

40

60

80

100

Acc

ura

cy o

f re

sult

s /

%

Cycle 1

Cycle 2

Cycle 3

Cycle 4

Cycle 5

Cycle 6

78% level

(b) The sliding-window method based recognitionaccuracy

50 100 150 200

Window size / s

0

20

40

60

80

100

Accura

cy / %

Cycle 1

Cycle 2

Cycle 3

Cycle 4

Cycle 5

Cycle 6

(c) Recognition accuracy and sliding window size

Recognition period / s

82

77

2015

Accu

racy /

%

72

10

67

5

62

0

(d) Recognition accuracy and recognition period

0 2 4 6 8

t / h

0

20

40

60

80

100

Acc

ura

cy o

f re

sult

s /

%

Cycle 1

Cycle 2

Cycle 3

Cycle 4

Cycle 5

Cycle 6

69% level

(e) The Markov chain model based prediction accu-racy

Fig. 13. Accuracy comparison (the black dash-dotted line in sub-graph (a) represents the level of 85%; (b) represents the levelof 78%; (e) represents the level of 69%; t: sampling time, t = k · Ts/3600; Ts: sampling interval with 1 second)

have to trade off between the P(F) and the action range ratiothrough selecting the value of γ according to the statisticallaw of historical micro-trips.

In order to select a proper γ to trade off P(F) (at t =tn,switching) and the action range ratio of recognition results,the statistical associations between γ and P(F), γ and theaction range ratio are studied based on those 13812 validmicro-trips, and the results are shown in Figure 12. Withthe increase of γ, the P(F) decreases while the action rangeratio increases. Considering the function of real-time micro-trip type recognition is to revise the type prediction result,in order to guarantee the accuracy of the final type valueof the occurring micro-trip, it is essential to ensure that the

P(F) is greater than the type prediction accuracy. Accordingto the average prediction accuracy obtained in Section 4, weset P(F) > 0.8. To satisfy this condition, the γ must be nomore than 0.8. Meanwhile, to keep a larger action range ratio,the γ is the larger the better. Therefore, 0.8 is taken as theproper value of the γ to make a compromise (Action rangeratio=47.48%), and the corresponding tn,switching defines thebest time of switching C(k) from CP (k) to CR(k) for theoccurring micro-trip.

In addition, when CR(k) = 3, the C(k) is also set to 3even if the current time dose not exceed tn,switching, becausea micro-trip is recognized as the third category only when itsvmax is at smooth level or its t15 is at smooth level or the


both. In that case, the future speed time series of the occurringmicro-trip may only increase or at least not decrease the valuesof vmax and t15, which will enhance the result that the typevalue of the occurring micro-trip is 3.

VI. RESULTS AND DISCUSSION

A. Accuracy analysis of the proposed HDCR method

In order to test the performance of the proposed HDCRmethod, simulations are implemented on the remaining 6-dayreal-world driving data of the 18-tons E-REV city bus, whichare consecutively numbered from cycle 1 to cycle 6, respec-tively. According to the simulation results, at each samplingperiod (sampling interval is 1s), the Accuracy (Accuracy(k)is the average accuracy of the results in the first kth samplingperiods) of the type recognition result based on the proposedHDCR method is calculated.

As shown in Figure 13(a), the initial Accuracy values ofrecognition results on the 6 cycles are equal to 0% or 100%.In this proposed HDCR method, the initial recognition resultsof the first micro-trip in each cycle are determined by thedefault setting of the Markov chain model, which is set to1, namely that the type of the first micro-trip is assumed tobe 1 (CP1 = 1). In that case, when the default setting thatCP1 = 1 is correct, the initial Accuracy values equal to 100%,otherwise, equal to 0%. In addition, with the increase of timet (where t = k · Ts, k is the sampling period number, and Tsis the sampling interval with 1 second), the average accuracyAccuracy(kf ) (kf is the number of the final sampling periodin a cycle) of recognition results on the 6 cycles tends to arelative stable level, 85%.

In addition to the accuracy, the time complexity is anotherimportant metric of the proposed HDCR method. In orderto analysis its time complexity, the statistic values of thesingle step computation time of the proposed HDCR methodare calculated. As shown in Table II, for the single stepcomputation time of the HDCR method over 6 cycles, theaverage minimum value is 0.0093 ms, the average maximumvalue is 1.8723 ms, the average standard deviation value is0.0951 ms, and the average mean value is 0.7763 ms. Theabove statistic values are obtained in a simulation environmentwhere the HDCR method is run on a Lenovo computer. Thecomputer model is 10C1-A00KCV, and the CPU is i5-4460S.

TABLE II. Time complexity analysis for the HDCR method.

Cycle Single step computation time [millisecond (ms)]

minimum maximum standard deviation mean

Cycle 1 0.0106 1.9763 0.0995 0.7815Cycle 2 0.0099 1.9681 0.0959 0.7744Cycle 3 0.0074 1.8747 0.0943 0.7762Cycle 4 0.0103 1.7176 0.0928 0.7744Cycle 5 0.0074 1.8743 0.0965 0.7756Cycle 6 0.0099 1.8227 0.0918 0.7757average 0.0093 1.8723 0.0951 0.7763

B. Comparison of accuracy with the sliding-window recogni-tion method

In order to make a comparison, the traditional sliding-window recognition method [35, 36], which takes each drivingsegment as the recognition object (a driving segment is a speedtime series divided from the latest driving data by using afixed length sliding window), is also tested on the 6 cycles.For the sliding-window recognition method, there are two keyparameters which affect its recognition accuracy, includingthe period of implementing recognition (recognition period)and the sliding window size. By setting different recognitionperiods and sliding window sizes, the sliding window basedrecognition method is tested on the 6 cycles. As shown inFigure 13(c), when the window size equals 100 s (samplinginterval is 1s), the average recognition accuracy Accuracy(kf )on each cycle can achieve the highest value. And as shownin Figure 13(d), taking the recognition result of cycle 1 as anexample, the average recognition accuracy Accuracy(kf ) de-creases with the increase of the recognition period. Therefore,the best size of the sliding window is selected as 100 s, and thebest recognition period is selected as 1 s. Based on the valuesof the above two key parameters, as shown in Figure 13(b),the average accuracy Accuracy(kf ) of sliding window basedrecognition results on the 6 cycles tends to 78%.

Compared with the sliding-window recognition method, theproposed HDCR method improves the accuracy of drivingcondition recognition by 7%.

C. Comparison of accuracy with the Markov chain modelbased prediction method

The Markov chain model based prediction method is alsoa common method used to obtain the driving condition typeat real-time, which is taken as a part of the proposed HDCRmethod. In order to compare the performance of such type pre-diction method with the performance of the proposed HDCRmethod, the Markov chain model is also individually testedon the 6 cycles, and the results are shown in Figure 13(e). Toquantify the accuracy improvement of the HDCR results onthe Markov prediction results, two ratios are calculated aftereach switching: (1) the ratio of the condition that the typerecognition result is False (F) while the type prediction resultis True (T). In this case, the average accuracy improvement isnegative; (2) the ratio of the condition that the type recognitionresult is True (T) while the type prediction result is False (F).In this case, the average accuracy improvement is positive.The specific calculation results are shown in Table III. Theaverage ratio of the condition (Recognition result is F andPrediction result is T) is about -1.35%, and the average ratio ofthe condition (Recognition result is T and Prediction result isF) is about +17.49%. The results show that the switching timetn,switching determined in this work is suitable, which reducesthe ratio of incorrect type revision due to the switching whileincreases the ratio of correct type revision. Compared with theMarkov chain model based prediction method, the proposedHDCR method improves the accuracy of driving conditionrecognition by 16%.


TABLE III. Quantitative analysis to the accuracy improvement (γ = 0.8; F: False; T: True).

CycleAverage accuracy [%] Average precision Improvement [%]

Prediction result HDCR based result Recognition result = F Recognition result = T TotalPrediction result = T Prediction result = F

Cycle 1 68.07 85.00 (−) 1.34 (+) 18.27 16.93Cycle 2 65.03 83.27 (−) 1.00 (+) 19.24 18.24Cycle 3 68.91 86.17 (−) 1.20 (+) 18.46 17.26Cycle 4 71.00 84.50 (−) 1.84 (+) 15.34 13.50Cycle 5 68.58 84.66 (−) 1.28 (+) 17.36 16.08Cycle 6 73.24 88.09 (−) 1.44 (+) 16.29 14.85

0 2 4 6 8

t / h

0

20

40

60

v /

km·h

-1

(a) Driving data.

4.2 4.3 4.4 4.5 4.6

t / h

0

20

40

60

v /

km·h

-1

(b) Partial enlarged drawing of driving data.

0 2 4 6 8

t / h

0

1

2

3

4

Ty

pe

Real type CP

(k) C(k)

(c) Micro-trip type results.

4.2 4.3 4.4 4.5 4.6

t / h

0

1

2

3

4

Ty

pe

(d) Partial enlarged drawing of type results.

Fig. 14. Type prediction and hybrid recognition results of cycle 1 (t: sampling time, t = k · Ts/3600; Ts: sampling intervalwith 1 second; CP (k): type prediction result based on the Markov chain model; C(k): the final type result based on the HDCRmethod)

Taking cycle 1 as an example, the vehicle speed, micro-trip type prediction and recognition results at each samplinginterval are drawn in Figure 14(a), and 14(c), respectively. Tolook into the details, the results during the partial simulationinterval from 4.2 to 4.6 hours are depicted in the partial en-larged drawings. As shown in Figure 14(d), the real type valuesof the partial enlarged driving cycle (see Figure 14(b)) aremarked by the red solid line. It is noticeable that the proposedHDCR (hybrid driving condition recognition) method basedresult C(k) (marked by the blue dash-dotted line) has betteraccuracy than the Markov chain model based prediction resultCP (k) (marked by the black solid line). Indeed, the accuracyis improved through using the recognition result CR(k) torevise the prediction result CP (k).

VII. CONCLUSIONS

In this work, a HDCR method is proposed to directly rec-ognize the type of current driving condition, which solves theproblem that the occurring micro-trip is difficult to be correctlyrecognized at its beginning stage. In order to improve the

type recognition accuracy, a Markov chain based probabilisticmodel and a fuzzy classification algorithm are used to predictand recognize the type of the occurring micro-trip respectively,and a statistic approach is proposed to determine when touse the type recognition result to revise the prediction result.In addition, to test the performance of the proposed HDCRmethod, we test it on the 6 days real-world driving data.

From the simulation results, two conclusions are drawn asfollows: (1) For this hybrid method, the recognition accuracydepends on two factors—(a) the type prediction accuracywhich relies on whether the statistic rules of the real-timedriving condition are consistent with the rules defined inthe Markov chain model; (b) the best switching time. (2)Compared with the existing methods, such as Markov chainbased prediction method and sliding-window based recogni-tion method, the HDCR can improve the recognition accuracyof the occurring micro-trip, which is used to indicate currentdriving condition.


TABLE IV. AbbreviationsCCN cumulative correct numberDCP driving condition predictionDCR driving condition recognitionE-REVs extended-range electric vehiclesGSV general speed valleyGPS global position systemHDCR hybrid DCRITS intelligent transportation system

ACKNOWLEDGMENT

This work is supported by the National Natural ScienceFoundation of China (Grant No. 51775291).

REFERENCES

[1] E. Tate, M. O. Harpster, and P. J. Savagian, “The elec-trification of the automobile: From conventional hybrid,to plug-in hybrids, to extended-range electric vehicles,”SAE International Journal of Passenger Cars - Electronicand Electrical Systems, vol. 1, no. 1, pp. 156–166, 2009.

[2] S. Overington and S. Rajakaruna, “Review of phev andhev operation and control research for future direction,”in IEEE International Symposium on Power Electronicsfor Distributed Generation Systems, 2012.

[3] M. A. Hannan, F. A. Azidin, and A. Mohamed, “Hybridelectric vehicles and their challenges: A review,” Renew-able and Sustainable Energy Reviews, vol. 29, no. 2014,pp. 135–150, 2014.

[4] N. C. Onat, M. Kucukvar, and O. Tatari, “Conventional,hybrid, plug-in hybrid or electric vehicles? state-basedcomparative carbon and energy footprint analysis in theunited states,” Applied Energy, vol. 150, pp. 36–49, 2015.

[5] R. Langari and J. S. Won, “Intelligent energy manage-ment agent for a parallel hybrid vehicle-part i: systemarchitecture and design of the driving situation identifi-cation process,” IEEE Transactions on Vehicular Tech-nology, vol. 54, no. 3, pp. 925–934, 2005.

[6] R. Wang and S. M. Lukic, “Review of driving conditionsprediction and driving style recognition based controlalgorithms for hybrid electric vehicles,” in Vehicle Powerand Propulsion Conference (VPPC), 2011 IEEE, 2011,pp. 1–7.

[7] L. Raykin, M. J. Roorda, and H. L. Maclean, “Impacts ofdriving patterns on tank-to-wheel energy use of plug-inhybrid electric vehicles,” Transportation Research Part DTransport and Environment, vol. 17, no. 3, pp. 243–250,2012.

[8] H. Wang, X. Zhang, and M. Ouyang, “Energy con-sumption of electric vehicles based on real-world drivingpatterns: A case study of beijing,” Applied Energy, vol.157, pp. 710–719, 2015.

[9] M. F. M. Sabri, K. A. Danapalasingam, and M. F.Rahmat, “A review on hybrid electric vehicles archi-tecture and energy management strategies,” Renewableand Sustainable Energy Reviews, vol. 53, pp. 1433–1442,2016.

[10] A. Safaei, M. R. Ha’Iri-Yazdi, V. Esfahanian, M. Esfaha-nian, M. M. Tehrani, and H. Nehzati, “Designing an in-

telligent control strategy for hybrid powertrains utilizinga fuzzy driving cycle identification agent,” Proceedingsof the Institution of Mechanical Engineers Part D Journalof Automobile Engineering, vol. 229, no. 9, 2015.

[11] Y. Hu, L. Yang, B. Yan, T. Yan, and P. Ma, “An onlinerolling optimal control strategy for commuter hybridelectric vehicles based on driving condition learning andprediction,” IEEE Transactions on Vehicular Technology,vol. 65, no. 6, pp. 4312–4327, 2016.

[12] A. Fotouhi and M. Montazeri-Gh, “Tehran driving cy-cle development using the k-means clustering method,”Scientia Iranica, vol. 20, no. 2, pp. 286–293, 2013.

[13] A. Fotouhi, R. Yusof, R. Rahmani, S. Mekhilef, andN. Shateri, “A review on the applications of driving dataand traffic information for vehicles energy conservation,”Renewable and Sustainable Energy Reviews, vol. 37, pp.822–833, 2014.

[14] H. Xie, G. Tian, H. Chen, J. Wang, Y. Huang, H. Xie,G. Tian, H. Chen, J. Wang, and Y. Huang, “A distribu-tion density-based methodology for driving data clusteranalysis: A case study for an extended-range electric citybus,” Pattern Recognition, vol. 73, pp. 131–143, 2018.

[15] F. A. Bender, M. Kaszynski, and O. Sawodny, “Drivecycle prediction and energy management optimizationfor hybrid hydraulic vehicles,” IEEE Transactions onVehicular Technology, vol. 62, no. 8, pp. 3581–3592,2013.

[16] M. Tao, K. Ota, and M. Dong, “Foud: integrating fogand cloud for 5g-enabled v2g networks,” IEEE Network,vol. 31, no. 2, pp. 8–13, 2017.

[17] H. Li, M. Dong, and K. Ota, “Control plane optimizationin software-defined vehicular ad hoc networks,” IEEETransactions on Vehicular Technology, vol. 65, no. 10,pp. 7895–7904, 2016.

[18] Z. Chen, R. Xiong, C. Wang, and J. Cao, “An on-linepredictive energy management strategy for plug-in hybridelectric vehicles to counter the uncertain prediction of thedriving cycle,” Applied Energy, vol. 185, pp. 1663–1672,2017.

[19] D. Huang, H. Xie, H. Ma, and Q. Sun, “Driving cycleprediction model based on bus route features,” Trans-portation Research Part D Transport and Environment,vol. 54, pp. 99–113, 2017.

[20] H. He, J. Cao, and J. Peng, “Online prediction withvariable horizon for vehicle’s future driving-cycle ,”Energy Procedia, vol. 105, pp. 2348–2353, 2017.

[21] X. Zheng, C. Julien, H. Chen, R. Podorozhny, andF. Cassez, “Real-time simulation support for runtime ver-ification of cyber-physical systems,” ACM Transactionson Embedded Computing Systems (TECS), vol. 16, no. 4,p. 106, 2017.

[22] L. Li, K. Ota, and M. Dong, “Human-like driving: Empir-ical decision-making system for autonomous vehicles,”IEEE Transactions on Vehicular Technology, 2018.

[23] Z. Lei, D. Qin, Y. Liu, Z. Peng, and L. Lu, “Dynamicenergy management for a novel hybrid electric systembased on driving pattern recognition,” Applied Mathe-matical Modelling, vol. 45, pp. 940–954, 2017.


[24] C. Musardo, G. Rizzoni, Y. Guezennec, and B. Staccia,“A-ecms: An adaptive algorithm for hybrid electric vehi-cle energy management,” European Journal of Control,vol. 11, no. 4-5, pp. 1816–1823, 2005.

[25] B. Yang, Y. Li, Q. Gong, and Z. R. Peng, “Multi-information integrated trip specific optimal power man-agement for plug-in hybrid electric vehicles,” in Confer-ence on American Control Conference, 2009, pp. 4607–4612.

[26] A. B. Nkoro and Y. A. Vershinin, “Current and futuretrends in applications of intelligent transport systems oncars and infrastructure,” in IEEE International Confer-ence on Intelligent Transportation Systems, 2014, pp.514–519.

[27] V. Osipkov, T. I. Ksenevich, B. Belousov, O. Karasev,S. Sokolov, M. Rudynets, M. Skalyga, S. Naumov,V. Stepnov, and A. Klimachkova, “Intelligent transportsystems: Revolutionary threats and evolutionary solu-tions,” in SAE 2016 World Congress and Exhibition,2016, pp. 29–30.

[28] G. Ripaccioli, D. Bernardini, S. D. Cairano, and A. Be-mporad, “A stochastic model predictive control approachfor series hybrid electric vehicle power management,” inAmerican Control Conference, 2010, pp. 5844–5849.

[29] C. C. Lin, H. Peng, and J. W. Grizzle, “A stochasticcontrol strategy for hybrid electric vehicles,” in Proceed-ings of the 2004 American Control Conference, 2004, pp.4710–4715.

[30] S. J. Moura, H. K. Fathy, D. S. Callaway, and J. L.Stein, “A stochastic optimal control approach for powermanagement in plug-in hybrid electric vehicles,” IEEETransactions on Control Systems Technology, vol. 19,no. 3, pp. 545–555, 2011.

[31] A. Balau, D. Kooijman, I. Rodarte, and N. Ligterink,“Stochastic real-world drive cycle generation based ona two stage markov chain approach,” SAE InternationalJournal of Materials and Manufacturing, vol. 8, no. 2,pp. 390–397, 2015.

[32] JEON, SoonIl, JO, SungTae, PARK, YeongIl, LEE, andJangMoo, “Multi-mode driving control of a parallel hy-brid electric vehicle using driving pattern recognition,”2002, pp. 489–494.

[33] M. Montazeri-Gh, A. Ahmadi, and M. Asadi, Drivingcondition recognition for genetic-fuzzy HEV Control,2008.

[34] S. Zhang and R. Xiong, “Adaptive energy management ofa plug-in hybrid electric vehicle based on driving patternrecognition and dynamic programming,” Applied Energy,vol. 155, pp. 68–78, 2015.

[35] J. Wang, Y. Huang, H. Xie, and G. Tian, “Drivingpattern recognition and energy management for extendedrange electric bus,” in Vehicle Power and PropulsionConference (VPPC), 2014 IEEE, 2014, pp. 1–6.

[36] J. Wang, Q. N. Wang, X. H. Zeng, P. Y. Wang, andJ. N. Wang, “Driving cycle recognition neural networkalgorithm based on the sliding time window for hybridelectric vehicles,” International Journal of AutomotiveTechnology, vol. 16, no. 4, pp. 685–695, 2015.

[37] C.-C. Lin, S. Jeon, H. Peng, and J. M. Lee, “Drivingpattern recognition for control of hybrid electric trucks,”Vehicle System Dynamics, vol. 42, no. 1-2, pp. 41–58,2004.

[38] M. Kuhler and D. Karstens, “Improved driving cyclefor testing automotive exhaust emissions,” SAE TechnicalPaper No.78650, 1978., 1978.

[39] E. Ericsson, “Variability in urban driving patterns,”Transportation Research Part D Transport and Environ-ment, vol. 5, no. 00, pp. 337–354, 2000.

[40] M. Montazeri-Gh, A. Fotouhi, and A. Naderpour, “Driv-ing patterns clustering based on driving feature analysis,”ARCHIVE Proceedings of the Institution of MechanicalEngineers Part C Journal of Mechanical EngineeringScience 1989-1996 (vols 203-210), vol. 225, no. 6, pp.1301–1317, 2011.

[41] T. K. Lee, B. Adornato, and Z. S. Filipi, “Synthesis ofreal-world driving cycles and their use for estimatingphev energy consumption and charging opportunities:Case study for midwest/u.s,” IEEE Transactions on Ve-hicular Technology, vol. 60, no. 9, pp. 4153–4163, 2011.

[42] D. S. Han, N. W. Choi, S. L. Cho, J. S. Yang, K. S.Kim, S. Y. Wan, and C. H. Jeon, “Characterization ofdriving patterns and development of a driving cycle in amilitary area,” Transportation Research Part D Transportand Environment, vol. 17, no. 7, pp. 519–524, 2012.

[43] E. Ericsson, “Independent driving pattern factors andtheir influence on fuel-use and exhaust emission factors,”Transportation Research Part D Transport and Environ-ment, vol. 6, no. 5, pp. 325–345, 2001.

[44] M. Matsuishi and T. Endo, “Fatigue of metals subjectedto varying stress,” 1968.

Haiming Xie received the Ph.D. degree in 2018from Tsinghua University, China. Currently, he isa post-doctoral in the Department of AutomotiveEngineering, Tsinghua University, China. He focuseson driving condition recognition, driving style recog-nition, driving data cluster analysis, powertrain pa-rameters matching and adaptive energy managementstrategy development for plug-in hybrid electric ve-hicles and extended-range electric vehicles.

Guangyu Tian received the Ph.D. degree in 1995from Tsinghua University, China. Currently, he isa professor in the Department of Automotive En-gineering, Tsinghua University, China, and he isthe chairman of the Electric Vehicle Branch of theSociety of Automotive Engineers (SAE) in China.His research area is the key technologies of varietiesof electric vehicles, including pure electric vehicles,hybrid electric vehicles, plug-in hybrid electric ve-hicles and extended-range electric vehicles.


Guangqian Du received the B.Eng. degree in 2016from Northwestern Polytechnical University. Cur-rently, he is a master candidate in the Departmentof Automotive Engineering, Tsinghua University,China. He focuses on energy management strategydevelopment for plug-in hybrid electric vehicles.

Yong Huang received the Ph.D. degree in 1997from Tsinghua University, China. Currently, he isa senior engineer in the Department of AutomotiveEngineering, Tsinghua University, China, and he isthe Secretary-General of the Electric Vehicle Branchof the Society of Automotive Engineers (SAE) inChina. His research area include hardware-in-the-loop vehicle simulation platform, high voltage sys-tem design, vehicle powertrain control and vehiclecontrol unit development.

Hongxu Chen received the Ph.D. degree in 2016from Tsinghua University, China. Currently, he is apost-doctoral in the Department of Automotive En-gineering, Tsinghua University, China. He is focusedon modeling, control and analysis of hybrid systems.Specifically, he deals with advanced drive systemsfor electric vehicles. He is a visiting student in theDepartment of Electrical and Computer Engineering,University of Illinois at Urbana-Champaign (UIUC)from September 2012 to December 2013. Earlier, hereceived undergraduate degree in Automotive Engi-

neering from Jilin University, China. He has received Kwang-Hua Fellowshipin 2010, Chinese Government Scholarship in 2012, Tao Zhefu Fellowship in2012, Student Laboratory Construction Award in 2013.

Xi Zheng has earned his PhD in Software En-gineering from UT Austin, Master in Computerand Information Science from UNSW, Bachelor inComputer Information System from FuDan; ChiefSolution Architect for Menulog Australia, now as-sistant professor/lecturer in Software Engineering atMacquarie University. He is specialized in ServiceComputing, IoT Security and Reliability Analysis.Published more than 40 high quality publications intop journals and conferencesPerCOM, ICSE, ICCPS,IEEE Systems Journal, ACM Transactions on Em-

bedded Computing Systems). Awarded the best paper in Australian distributedcomputing and doctoral conference in 2017. Awarded Deakin Researchoutstanding award in 2016. Reviewer for top journals and conferencesIEEESystems Journal, ACM Transactions on Design Automation of ElectronicSystems, Pervasive and Mobile Computing, IEEE Transaction on CloudComputing, PerCOM.

Tom H. Luan recieved the BSc degree from Xi’anJiaotong University, Xian, China, in 2004, the MPhilfrom Hong Kong University of Science and Technol-ogy in 2007 and the PhD degree from the Universityof Waterloo, Waterloo, ON, Canada, in 2012. Since2013 to 2017, he was a Lecturer in Mobile andApps with the Deakin University, Burwood, VIC,Australia. He is currently a Professor with the Schoolof Cyber Engineering in the Xidian University,Xian, China. Dr. Luan’s research mainly focus onthe system model, algorithm design, performance

evaluation and system security in the area of mobile computing, wirelessmultimedia networks, vehicular networks, fog computing and mobile cloudcomputing.

ieee transactions on vehicular technology, vol. xx,...

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