Driving Style-Oriented Adaptive Equivalent ...

This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TVT.2018.2855146, IEEE Transactions on Vehicular Technology 1

Driving Style-Oriented Adaptive Equivalent Consumption Minimization Strategies for HEVs Sen Yang, Wenshuo Wang, Student Member, IEEE, Fengqi Zhang, Yuhui Hu, and Junqiang Xi

Abstract—The performance of energy management systems in hybrid electric vehicles (HEVs) is highly related to drivers’ driving style. This paper proposes a driving style-oriented adaptive equivalent consumption minimization strategy (AECMS-style) in order to improve fuel economy for HEVs. For this purpose, firstly, a statistical pattern recognition approach is proposed to classify drivers into six groups from moderate to aggressive using kernel density estimation and entropy theory. Then, the effects of driving style on EMS are discussed by analyzing the performance of equivalent consumption minimization strategy (ECMS). Based on the comprehensive analysis, we design a new optimal equivalent factor adjustment rule for the AECMS-style and also redesign the braking strategy of motors at driving charging mode for different driving styles. Finally, five drivers with typical driving styles participate in experiments to show the effectiveness of our proposed method. Experimental results demonstrate that the AECMS-style can improve the fuel economy and charging sustainability of HEVs, compared with ECMS. Index Terms—Hybrid electric vehicles, driving style recognition, adaptive equivalent consumption minimization strategy.

I. I NTRODUCTION A. Motivation

H

YBRID electric vehicles (HEVs) have been explored in past few decades to reduce air pollution, dependence on petroleum, and greenhouse gas emission. Fuel economy of HEVs is influenced by relevant energy management strategies (EMS) [1], [2], traffic environment [3], [4], and driver style [5]–[8]. Research demonstrates that leveraging driving style for HEVs can improve the fuel consumption from 25% to 68%, or even up to 100% [9] with the fact that human drivers’ preference for speed and acceleration does affect power allocation. In order to further improve the fuel economy of HEVs, it is of practical significance to develop such energy management strategy capable of adapting to driving style. Copyright (c) 2015 IEEE. Personal use of this material is permitted. However, permission to use this material for any other purposes must be obtained from the IEEE by sending a request to [email protected]. This work is supported by the National Natural Science Foundation of China (No.51505029). (Corresponding Author: Yuhui Hu) S. Yang, Y. Hu, J. Xi are with the Department of Mechanical Engineering, Beijing Institute of Technology, Beijing, China, 100081. (email:[email protected];[email protected];[email protected].) Wenshuo Wang is with the Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109 USA. He was also with the Department of Mechanical Engineering, University of California at Berkeley, USA. (email:[email protected]) Fengqi Zhang is a Lecturer in School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an, China. (email:[email protected])

B. Related Research Many studies have been conducted to clarify the relationship between driving style and fuel consumption. Existing research demonstrates that [10]–[12] the fuel consumption of HEVs is sensitive to driving style and driving habits, such as excessive acceleration and deceleration. Usually, aggressive drivers will cause substantially poor fuel economy, while calm drivers will improve the fuel economy [13]. In order to assist aggressive drivers driving in an economical way, the effects of driving style on fuel saving potentials based on real-world cycles has been investigated [14], and it has demonstrated that acceleration is one of the main factors of impacting on fuel consumption and emission and that [9] the regenerative braking limitation mainly causes great fuel economy variation of HEVs. For example, aggressive behavior with large acceleration will lead to high fuel consumption [15]. Based on this, instantaneous acceleration has been used to model the increments of fuel consumption with quadratic and exponential functions [16]. Although the above literatures demonstrated driver style has significant influence on fuel economy, in-depth analysis and explanation for why driver style has different fuel consumption for HEVs are limited. For driving style, various factors could impact it, such as personal characteristics, driving conditions and environment, and even weather [17], which raises a challenge for driving style recognition. Numerous studies have been conducted to quantitatively rate driving style. Generally, the features used for driving style recognition include driving parameters (e.g., brake pedal [5] and throttle opening [18]) and vehiclerelated parameters (e.g., vehicle speed [19], acceleration and deceleration [5], [20], jerk [6], [21]), and fuel consumption [22]. The recognition algorithms were then developed using the selected features and recognition methods [23] such as neural networks [20], [24], support vector machines [25], [26], and fuzzy recognition [27], [28]. For the optimal energy management strategy, drivers are usually classified into two or three categories [5], [6], [9], [14]: mild drivers (an economical driving style), normal drivers (a medium driving style), and aggressive drivers (a sporty driving style). However, a delicate division of driving style could provide flexible alternatives to optimal EMS and equivalent consumption minimization strategy (ECMS), since a slight deviation of optimal equivalent factor (EF) could lead to unexpected operations outside the boundaries of the state of charge (SoC). [29]. Therefore, further fracturing driving style could make control strategies of fuel consumption much applicable. Many optimization approaches have been developed to

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System of HEV

Human driver

Driving style level recognition model Original driving data

Driving style level Recognition module based on kernel density estimation and entropy

Traffic scenario and environment

Real-time energy management Optimal torque allocation

Tar get Vehicle

Guided Vehicle

The typical urban bus cycle i n china

Equivalent factor adjustment Adjustment result of EF Adjustment module of Equivalent factor based on the driving style level

Equivalent Consumption Minimization Strategy

Fig. 1. Framework of the proposed driving style-oriented AECMS model.

reduce fuel consumption of HEVs for individual drivers. For example, a powertrain component optimization approach was proposed [30] for HEVs to reduce the variability of fuel economy with different driving styles. Tang et al. [5] designed a dynamic strategy for aggressive drivers and an economy strategy for clam drivers. Although the careful adjustment of this scheme with two control strategies reaches an expected performance, it could not adapt to all driving styles. For optimization strategies, the dynamic programming (DP) – a global optimization approach – can obtain the optimal fuel economy for given driving cycles; however, it is difficult to be directly implemented to a real vehicle since it requires the prior knowledge of the whole driving cycle [31]. To overcome this problem, ECMS, a real-time optimization method, was introduced [32] to minimize instantaneous equivalent fuel consumption by converting electricity consumption into equivalent fuel consumption under the constraints of motor, engine, and battery [31]. Although ECMS can be used for real-time control, it is not optimal with respect to driving style due to the fixed optimal EF. Fortunately, the optimal solution could be obtained by only tuning EF according to the current driving cycle, which is called adaptive equivalent consumption minimization strategy (AECMS) [33]. With this advantage of AECMS, the optimal EF for three driving styles was off-line estimated and online regulated for individual drivers [6]. From the above discussions, the limitations in the aforementioned research mainly are three-fold: •

They basically classified drivers into only three patterns for EMS, which makes it inflexible to design a continuous controller for EMS. They did not investigate the specific relationships between driving behavior and poor fuel economy or chargesustainability. They improved the fuel economy without considering the improvement of both the battery’s charge-sustainability and the engine’s transient losses.

effects, as shown in Fig. 1. The main contributions of this paper are listed as follows. 1) Proposing a statistical pattern recognition approach by integrating kernel density estimation with entropy theory to precisely and rapidly recognize instantaneous driving style, which classifies drivers into six levels of driving style. 2) Summing up the causes behind the poor fuel consumption and low charging sustainability of battery for aggressive or moderate drivers by analyzing their specific performance parameters, which encourages a better design of EMS for different driving styles. 3) Proposing AECMS-style control strategy, consisting of a new adjustment rule of EF based on driving style and a redesigned braking strategy for motor, to reduce energy losses of mechanical braking and improve the charging sustainability1 . D. Paper Organization The remainders of paper are organized as follows. Section II describes the configuration and mathematical models of HEVs. Section III introduces the driving style recognition method. Section IV illustrates the proposed energy management strategy. Section V elaborates the results and discussions of our designed AECMS-style. Finally, conclusions are presented in Section VI. II. V EHICLE M ODEL

C. Contributions

In this section, a post-transmission parallel HEV is used and its powertrain configuration is shown in Fig. 2, where the engine and the motor are placed at either end of the clutch. The motor can work as an electric motor or a generator alternately. The coupling torque between the engine and the motor is in the automated mechanical transmission (AMT). The clutch and motor controllers are used to determine whether the power of the engine and the motor is engaged in the powertrain, to realize the switch among five driving modes: pure electrical driving mode, engine driving mode, driving charging mode,

This paper aims to design an adaptive energy management for HEVs with considering driving style to reduce the negative

1 The term ‘charge-sustainability’ is referred to as keeping the SoC of battery around its reference value.

•

•



HCU

700

Battery

Pb

600 MCU

Te , ne

Tc , nc

Tin , nin Tm , nm

Tw , nw

TCU

Tout , nout

Final drive

Tw , nw Clutch

Engine

Motor

0 800

Fig. 2. Structure of the HEV powertrain.

Engine Motor

Transmission Battery

Vehicle

AMT gear ratio / i0 Final gear ratio / if Maximum capacity / Qmax Open-circuit voltage / UOC Vehicle loaded mass / m Roll resistance coefficient / f Air resistance coefficient CD Windward area / A Wheel radius / r rotating mass coefficient / δ

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1400

1700 ne [r/min]

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386 407 2600

400

Fig. 3. Engine fuel consumption map.

TABLE I BASIC PARAMETERS OF THE HEV. Specification Maximum power / Pe max Maximum torque / Te max Maximum speed / ne max Minimum torque / Tm min Maximum torque / Tm max Nominal speed

300

200

300

200

250

400

100

Mechanical Communication Electrical

221

200

200

AMT

Fuel consumption rate [g/(kw"h)]

500 Te [N"m]

ECU

241

Value 125 600 2600 150 650 2600 6.25/3.583 /2.22/1.36/ 1/0.74 6.17 70 650 18000 0.01 0.65 6.73 0.5715 1.04

engine speed and torque [34] with (4) using experimental data (Fig. 3).

Unit kW N·m r/min N·m N·m r/min

be = f (ne , Te )

(4)

The engine torque and power are formulated by (5) and (6), respectively. Te = Te

Ah V kg m2 -

min (ne )

+ α(Te Pe =

regenerative braking mode, and hybrid driving mode. The main parameters of HEV are listed in Table I. Here, the HEV model considers the effect of torque distribution strategies on fuel economy, instead of the dynamic process of powertrain. Therefore, this paper simplifies the HEV model as follows. 1) Longitudinal Dynamic Model: Without considering the lateral dynamics, the relationship between the torque of wheel and the output torques of engine and motor is Tw = ηi · i0 · if (Te + Tm ) + Tb

(1)

Tb = Tmech + Tm

(2)

where Tw is the torque of wheel, ηi is the transmission efficiency, Tb is the braking torque, Tmech is the mechanical braking torque, Te and Tm are the engine output torque and the motor output torque, respectively. According to the longitudinal dynamic, Tw can also be formulated by 1 dv Tw = (mgf cos θ + CD ρd Av 2 + mg sin θ + δm ) · r (3) 2 dt where v is the vehicle speed. 2) Engine Model: Engine transient characteristics are neglected using the quasi-static assumption of the engine, thus the fuel consumption of the engine is only determined by the

max (ne )

− Te

min (ne ))

Te ne 9550ηe

(5) (6)

where be is the engine fuel rate, ne is the engine speed, α is the throttle opening, Te max (ne ) and Te min (ne ) are the engine maximum and minimum torque at the current speed, and ηe is the engine efficiency. 3) Motor Model: The motor in Fig. 2 is used to drive vehicle and regenerative brake. Considering the ultimate capacity of the battery, the torque and power of the motor are described by (7) and (8), respectively. ( min(Tm req , Tmax dis (nm )), if Tm req > 0 Tm = (7) max(Tm req , Tmax char (nm )), if Tm req < 0 Tm nm , Tm > 0 9550η m Pm = (8)   Tm nm ηm , T ≤ 0 m 9550 where nm is the motor speed, Tm req is the required torque of the motor, Tmax dis (nm ) is the maximum output torque of the motor at the current speed when the battery is discharging, Tmax char (nm ) is the maximum output torque of the motor at the current speed when the battery is charging, ηm = ψ(nm , Tm ) is the motor efficiency (Fig. 4), and Pm is the motor power. 4) Battery Model: Neglecting the effect of temperature and transients, the battery is simplified as the Rint battery model [35]. Thus the SoC is derived from Kirchhoffs voltage law by p UOC (t) − UOC (t)2 − 4Rin (t)Pbatt (t) d (9) SoC(t) = − dt 2Rin (t)Qmax   

Pbatt (t) = UOC (t) · I(t) + Rin (t) · I(t)2

(10)



800

0.95

600 0.9

400 Tm [N" m]

wheel speed, i0 = ϕ(α, v) is the AMT gear ratio, as shown in Fig. 5. III. D RIVING S TYLE R ECOGNITION

200

0.85

0.8

Here, we mainly focus on instantaneous driving style. To recognize it, we develop a statistical method and validate it in a typical urban bus cycle.

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A. Method

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To understand our developed statistical pattern recognition method, we introduce some basic knowledge of kernel density estimation, Bayesian law, and entropy theory. 1) Kernel Density Estimation: Kernel density estimation is a common method to compute non-parametric probability density estimates of data [20], [36]. Given data set X = m N + {Xm }M and xm n = m=1 with Xm = {xn }n=1 , m ∈ N m,n m,n + (x1 , . . . , xJ ) , n ∈ N , we can estimate the density of the j th variable xj in the random point x = (x1 , x2 , . . . , xj ), j = 1, 2, . . . , J by ! N xj − xm,n 1 X j ˆ K (15) f (xj |Xm ) = N h n=1 h

2500

nm [r/min]

Fig. 4. Motor efficiency map.

100 Downshifting Upshifting

90 80

70  [%]

60 50 40 30

20 10

Gear 2 3 0 10 20

3 4

4 5 30

40 v [km/ h]

5 6 50

60

70

Fig. 5. AMT gear-shift curves with vehicle speed v and throttle opening α.

where Rin (t) is the battery resistance, I(t) is the current flowing through the battery, and Pbatt (t) is the required battery power. 5) Clutch Model: Without considering the dynamic process of mode switch for HEVs, the clutch has only two operation states – engaged and disengaged. The engagement is considered to be rigidly connected. Therefore, the torque and speed of clutch is calculated by ( Te ηc , (engaged) Tc = (11) 0, (disengaged) ( nc =

ne , (engaged) nm , (disengaged)

nw =

ne if i0

which yields the kernel density estimate function as  !  m,n 2  N  X x − x 1 1 j j √ exp − (17) fˆ(xj |Xm ) =  2  h N h 2π n=1 2) Bayesian Law: Given the prior probability p(Xm ), the posterior probability p(Xm |xj ) can be estimated using the Bayes formula p(xj |Xm )p(Xm ) p(Xm |xj ) = PM m=1 p(xj |Xm )p(Xm )

P (Xm |x) =

(13) (14)

where Tin and Tout are the input torque of the transmission and the output torque of final gear, respectively; nw is the

(18)

where the conditional probability p(xj |Xm ) = fˆ(xj |Xm ). 3) Entropy Weight: A entropy weight method is introduced to compromise the posterior probabilities of different variables comprehensively. The comprehensive posterior probability of x is represented by

(12)

6) Transmission Model: Without considering the torsional and lateral vibration during transmission, the output torque and the wheel speed are modeled using (13) and (14), respectively. Tout = Tin ηi if i0

where h is the kernel width and K(∗) is the Gaussian kernel function computed by 1 2 1 K(x) = √ exp − x (16) 2 2π

J X

p(Xm |xj )wjm

(19)

j=1

where P (Xm |x) is the posterior probability of x occurring given the data set Xm , and wjm is the weighted coefficient of the posterior probability for the j th variable in Xm . Information entropy [37] is defined as the degree of randomness or disorder in an information system. If the posterior probability of one variable has a large degree of variation, the weighted coefficient of this identification parameter is small, otherwise the weighted coefficient is large. Therefore, we use the entropy theory to determine the weighted coefficients. The



N ( , 2 ) 34.1%

2.2% -3

34.1%

13.6%

-2

 -3  -2

13.6%

 -

-1



1

 +

2

2.2% 3

P

 +2  +3

Fig. 6. The rule for discrimination values of driving-style levels

entropy weight coefficient of the posterior probability for each variable is calculated by 1 − Hjm 1 m (20) wj = 1 − PJ J −1 (1 − Hjm ) j=1

Hjm = −K

N X

pm,n ln pm,n j j

(21)

n=1

em,n j pm,n = P j N m,n n=1 ej

(22)

where Hjm is the error entropy of the posterior probability of all j th variable in Xm , pm,n is the error proportion of the j posterior probability of xm,n , K = 1/ln(N ) is a constant, N j is the sampling number of training data, and em,n is the error j of the posterior probability of xm,n . The posterior probability j error for each variable is computed by ) ( p(Xm ) − p Xm |xm,n j m,n (23) ej = min 1, p(Xm ) where p Xm |xm,n is the posterior probability of xm,n . j j 4) Decision Making: P (Xm |x) is used to quantify the driving style regarding aggressiveness. Take aggressive and moderate driving styles for example, the scale of driving style between them is computed by S(x) = {S|∆P = P (Xagg |x) − P (Xmod |x) ∈ (ε, ε)} (24) where P (Xagg |x) and P (Xmod |x) are the posterior probabilities of drivers in the aggressive and moderate driving styles, respectively; S is the set of driving style levels, and (ε, ε) are the threshold value. In this paper, the levels of driving style from aggressive to moderate are classified into six patterns, S = {−3, −2, −1, 1, 2, 3}. The threshold value (ε, ε) is determined by dividing the normal distribution (N (µ, σ 2 )) of ∆P into six intervals, as shown in Fig. 6. A statistical recognition algorithm of recognizing the aggressiveness level is proposed based on the above description, as shown in Algorithm 1. B. Data Collection and Feature Selection 1) Data Collection: All the training and testing datasets were collected in a driving simulator (Fig. 7) at the sample frequency of 50 Hz. The driving simulator consists of four

Algorithm 1 :Recognition algorithm of driving-style levels Training N k 1: Input training data sets Xk = {xk n }n=1 , where xn = k,n k,n k,n (x1 , x2 , . . . , xJ ), k = agg, mod. 2: for k = agg to mod do 3: for j = 1 to J do 4: Get fˆ(xkj |Xk ) ←− (17) 5: for n = 1 to N do 6: Calculate fˆ(xk,n j |Xk ) ←− (17) 7: Calculate p(Xk |xk,n j ) ←− (23) k,n 8: Calculate ej ←− (18) 9: end for 10: for n = 1 to N do 11: Calculate pk,n ←− (22) j 12: Calculate Hjk ←− (21) 13: Get wjk ←− (20) 14: end for 15: end for 16: end for Testing ∗ ∗ 1: Input new driving data x(∗) = (x∗ 1 , x2 , . . . , xJ ). 2: for k = agg to mod do 3: for j = 1 to J do 4: Calculate fˆ(x∗j |Xk ) ←− (17) 5: Calculate p(Xk |x∗j ) ←− (23) 6: end for 7: Calculate P (Xk |x∗ ) ←− (19) 8: end for 9: Determine S(x∗ ) ←− (24) 10: return S(x∗ )

Vehicle model of Matlab/simulink Steering wheel

Brake/Acceleration /Clutch pedal Virtual Scenario

Driver Gear shift

PreScan Virtual Scenario and Interface

Custom-built driving peripherals

Computer

Fig. 7. Schematic diagram of the driving simulator.

main parts: human driver, operation input equipment, vehicle model, and virtual environment. The custom-built driving peripherals, including steering wheel, brake/gas/clutch pedal and gear shift handle, were utilized to collect the driver’s operating signals such as steering wheel angle, brake pedal displacement, and throttle opening. The virtual scenarios, including the vehicle, roads, and traffic facilities, were designed using PreScan software. Each driver was labeled as aggressive or moderate using a questionnaire. All driver participants became familiar with the driving simulator before a formal trial. During trials, all drivers followed the rules:



The typical urban bus cycle in china

3

-3

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600 800 Time [s]

1000

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1200

(a) Speed

Fig. 8. Traffic scenario used in the test

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40 20 0

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-4 0 Acceleration [m/s2]

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Aggressive Normal Distribution

Velocity [km/h]

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(b) Distribution of speed

80

65

Normal Aggressive

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(c) Throttle opening

Normal Aggressive

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30 60 Velocity [km/h]

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120

(d) Distribution of throttle opening

26 1

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Fig. 9. The typical urban bus cycle in China.

Aggressive Normal

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13

Throttle opening

Velocity [km/h]

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Aggressive Normal Distribution

Guided Vehicle

Target Vehicle

Acceleration [m/s2]

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Normal Aggressive

10

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600 800 Time [s]

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(e) Acceleration

0.1

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1.2

(f) Distribution of acceleration

Fig. 10. Collected driving data for different driving styles.

The secondary tasks such as talking with others, making or answering a telephone were forbidden; • All participants were in mentally and physically normal states; • Each participant rested 2 minutes before the next trial; • All participants manipulated the subject vehicle in their own driving style without any guidance. We designed a traffic scenario as shown in Fig. 8, where the black vehicle is a leading vehicle tracking the speed profile of a typical urban bus cycle in China (Fig. 9) and the red car is the subject vehicle controlled by the human driver to follow the black car. 2) Feature Selection: Feature selection is very important for driving style recognition, which should allow pattern vectors to belong to different categories to occupy compact and disjoint regions as much as possible in a specified feature space [38]. Vehicle speed, throttle opening and acceleration have been demonstrated as the most effective features to capture the relationship between fuel economy and driving style [17]. Additionally, Fig. 10 illustrates the distribution of all features and implies that the discrimination of throttle opening and acceleration between aggressive and moderate drivers is greater than speed. Therefore, the throttle opening and acceleration are selected to distinguish the driving style compared with the speed in a urban bus cycle. •

C. Recognition Model Verification Based on the proposed recognition method and collected data, the weighted coefficients of the throttle opening for agg aggressive and moderate styles were set wthr = 0.361 and mod wthr = 0.258, respectively. The weighted coefficients of the acceleration for aggressive and moderate styles were

TABLE II D ISCRIMINATION VALUES OF D IFFERENT D RIVING STYLE L EVELS S 1 2 3

(ε, ε) (-0.0993,0.2733] (0.2733,0.6459] (0.6459,1.0185]

S -1 -2 -3

(ε, ε) (-0.4719,-0.0993] (-0.8445,-0.4719] (-1.2171,-0.8445]

agg mod set wacc = 0.639 and wacc = 0.742, respectively. The discrimination values of different driving styles are shown in Table II. In order to leverage historical driving data, we design a strategy to update the driving style level as ! N 1 X S(t + T ) = S x(t + nT0 ) (25) N n=1

where T is the update period, T0 is the sampling period, x is the sampling data in the last update period, N is the number of sampling data in one update period. In order to show the effectiveness of our designed recognition model, two groups of aggressive and moderate drivers are identified and the results are shown in Fig. 11 with T = 1 s. Note that the idle condition is meaningless and its driving level is 0. The recognition rate is calculated by (26), and the results of moderate and aggressive driving styles are λmod = 87.01% and λagg = 69.61%. Kcor,j , j = mod, agg λj = P Kall,j

(26)

where λj is the accuracy of j drivers. Kcor,j is the number of the sampling data that is correctly recognized as j driving style. Kall,j is the number of the sampling data for j drivers.


This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TVT.2018.2855146, IEEE Transactions on Vehicular Technology

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Fig. 11. Verification results of diving style recognition model with T = 1 s. (a) Results for aggressive style; (b) Results for moderate style; (c) Frequency of the level of aggressive style; (d) Frequency of the level of moderate style.

IV. P ROPOSED E NERGY M ANAGEMENT S TRATEGY In this section, we first introduce ECMS and analyze the performance of ECMS to reveal the underlying relationship between high fuel consumption or poor charging sustainability and driving style.

Fuel consumption [L/100km]

-1 0

Driver 1# Driver 2# Driver 3#

SoC

S (t)

S (t)

7

Driver 1# Driver 2# Driver 3#

55 50

45 40

35 30

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t [s]

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(b) Fuel consumption curve Fig. 12. Results of ECMS for different drivers with s(t) = 2.5.

A. ECMS For ECMS in the charge-sustaining HEVs, the battery is used only as an energy buffer and all energy ultimately comes from fuel [39]. Thereby, the electricity used during the battery discharge phase must be replenished later using the energy from the engine in order to balance the battery SoC. The equivalent fuel consumption and the optimal problem are formulated by (27) and (28), respectively. m ˙ eqv (u(t), t) = m ˙ fuel (u(t), t) + m ˙ elec (u(t), t) Pm (u(t), t) = be (u(t), t) · Pe (u(t), t) + s(t) QLHV [Te

opt , Tm opt ]

= arg min {m ˙ eqv (u(t), t)} [Te ,Tm ]

(27)

(28)

with  Tdem (t) = Te (t) + Tm (t)      0 ≤ nm (t) ≤ nm max     ne min ≤ ne (t) ≤ ne max  0 ≤ Te (t) ≤ Te max (ne (t))    SoCmin ≤ SoC(t) ≤ SoCmax    Tm min (nm (t)) ≤ Tm (t) ≤ Tm

max (nm (t))

where u(t) is the control input, m ˙ eqv is the equivalent fuel consumption, m ˙ fuel is the engine fuel rate, m ˙ elec is the equivalent fuel of converting the motor power, s(t) is the EF, QLHV is the fuel lower heating value, Tdem (t) is the total torque demand, Tm max (nm (t)) and Tm min (nm (t)) are the motor maximum and minimum torque, respectively;

Te max (ne (t)) is the engine maximum torque, nm max is the motor maximum speed, ne min is the engine minimum speed, ne max is the engine maximum speed, SoCmin and SoCmax are the minimum and maximum SoC, respectively; Te opt and Tm opt are the optimal engine and motor torque, respectively. Here, EF represents the energy cost in the battery, which affects the fuel consumption and the trend of the battery SoC in the power distribution between the engine and motor [40]. The battery will be discharged (charged) if EF is too low (high). In the standard ECMS, however, EF is constant and could not adapt to individual drivers, as shown in Fig. 12. It can be seen that the final SoC and fuel consumption with the standard ECMS are different over drivers. To figure out the causes of these differences, we analyze driving performance over some key features, as shown in Table III. More details are listed as follows: 1) With increasing aggressiveness of driving style(Driver #1 → Driver #2 → Driver #3), the mean of mechanical braking force increases, which dramatically leads to the energy losses of mechanical braking. This is the primary reason that the equivalent fuel consumption of aggressive drivers is greater than that of moderate drivers. 2) With increasing aggressiveness of driving style, the demand of hybrid driving mode (high power) and regenerative braking mode (sharp braking) get a proportional increase, which overruns the time of driving charging mode. This is one of the main reasons that the final



TABLE III P ERFORMANCE OF ECMS Parameters Driving style 1 EFC [L/100km] 2 Final SoC Engine ON/OFF times Fuel consumption rate of engine [g/(kw·h)] Driving charging 3 Hybrid driving 3 Regenerative brake 3 Mean mechanical braking force [N] Mechanical braking energy [J] Total consumption energy [J] 4 1 2 3 4

FOR DIFFERENT DRIVERS WITH

Driver #1 12.99% 39.75 0.6302 96

s(t) = 2.5.

Driver #2 27.63% 42.03 0.5969 126

Driver #3 73.05% 47.59 0.5128 244

Trend % % & %

198.75

198.96

200.92

%

27.41% 12.13% 26.82%

20.87% 17.30% 30.15%

8.85% 23.25% 33.61%

& % %

3.08×103

3.72 ×103

5.03 ×103

%

1.86×107

2.35×107

3.33×107

%

3.38×107

3.88×107

4.79×107

%

The proportion of aggressive driving style in the whole process with T = 1s. The EFC (equivalent fuel consumption) is calculated by (33) considering the losses of battery. The proportion of this mode in the whole process. The total consumption energy includes the fuel consumption of engine and the electric loss of battery

SoC (charge-sustainability) of aggressive drivers always stays low. 3) Due to the low SoC, the motor would not provide supplemental power for engine operating in optimum region and thereby resulting in a higher fuel consumption rate for aggressive drivers. That is one reason for poor fuel economy of aggressive drivers. 4) With the increasing requirements of acceleration and deceleration, the engine ON/OFF times soar, which increases engine’s transient losses. This is another reason for poor fuel economy of aggressive drivers. 5) For moderate drivers, due to the low power requirements, the driving charging mode accounts for a large proportion to keep engine working in an optimal region, which increases the battery SoC and the energy loss in the conversion process from fuel to electricity. In order to obtain a charge-sustaining solution and minimize the total fuel consumption, it is necessary to make EF adaptive for individual drivers. To do this, we can adjust EF to increase the motor’s aid in high power demand situations (aggressive drivers) or reduce the excessive generated power during low demand (moderate drivers), because EF is proportional to the energy requirements of the vehicle, regenerative braking energy, and the cost of battery charging and discharging [40].

to penalize electrochemical energy consumption. Based on the analysis in Section IV-A, drivers’ average energy requirements directly determine how the SoC level is going to change. More specifically, a high power requirement (aggressive drivers), which could not be satisfied only by the engine, needs the assisted torque of the motor to recover a low SoC level; on the contrary, a low power demand (moderate drivers) promotes the motor to save the redundant energy leading to a high SoC. Therefore, the average energy requirement with driving style level indicates the motor cost for individual drivers. These underlying relationship helps ones to tune EF according to driving style level, enabling to improve fuel economy and charge sustainability of HEVs. In this paper, EF is approximated by (29) based on the Pontryagin’s Minimum Principle [41]. s(t) =

QLHV ηm + 2ρ (SoC(t) − SoCr ) ηe Ebatt

(29)

where ρ is the penalty factor, SoCr is the reference of the SoC, and Ebatt is the energy capacity of the battery. The EF is related to the deviation of the current SoC from the reference SoC and energy conversion efficiency of the motor and engine. The first part of (29) can be regarded as a constant s0 since the energy conversion efficiency of motor and engine is slightly variated. Then, we substitute S(t) for (SoC(t) − SoCr ) to adapt to different driving styles. For the sake of simplification, (29) is rewritten as s(t + T ) = s0 + λ · S(t + T )

(30)

where s0 is the initial EF, S(t + T ) is the driving style level, λ is a constant coefficient. The adaption of EF with respect to driving style is detailed as follows: An aggressive driver accelerates or decelerates frequently, implying that more electrical power should be provided to allow the engine to operate under optimal conditions, thereby resulting in a decrease in SoC. To keep SoC at a reasonable level and the engine operating under optimal conditions, EF should be increased in the next time interval according to the aggressiveness of this driver. For normal drivers having a low power demand, the electricity is rarely needed for energy management strategy to provide power and the motor always operates as a generator, which makes the battery overcharge. Therefore, we reduce EF according to the level of moderate driving style, allowing the energy management strategy to exploit more electricity to maintain the charge balance and reduce energy losses.

B. Adaption of EF To overcome the shortcomings of ECMS with a constant EF, many adjustment rules are emerged based on the main objectives of the control strategy. For the charge sustainability, EF is usually updated according to the deviation from its reference (SoC(t) − SoCr ) [31], [40]. The basic idea with this approach is that when SoC(t)−SoCr is considerably positive, s(t) should be small values so that the electrochemical energy becomes less expensive than the fuel energy. On the contrary, when SoC(t) − SoCr is negative, s(t) should be large in order

C. Generative Braking Strategy Although the above adjustment rule achieves improvements in fuel economy and charging balance, it may fail to reduce the mechanical braking force and energy losses in generative braking mode, which is very important for aggressive drivers. To overcome this downside, we redesign the recovery torque of the motor for different diving styles using the short-time overload capacity of Permanent magnet synchronous motors (PMSM) by



Electric dynamo -meter

s, 100 s, 54 s when the overload coefficient is 1.5, 2.0, and 2.5, respectively. Since most of braking conditions are short, we can set δa = 1.2 and δm = 1.

Engine

AMT

V. E XPERIMENT V ERIFICATION AND R ESULT D ISCUSSION A. Verification of Vehicle Model Wireless torquemeter

Permanent magnet synchronous motor

DC-AC

(a) Test rig of HEV.

0.52

Simulation Rig test

0.51 0.5 SoC

B. Analysis of Different Energy Management Strategies

0.49 0.48

0.47 0.46

0

300

600 900 Time [s]

1200 1400

(b) SoC curve.

Fuel consumption [L/h]

35

Simulation Rig test

28 21 14 7 0 300

400

500 600 Time [s]

700 750

(c) Fuel consumption curve. 7 0

S im u la tio n R ig te s t

6 0

v e lo c ity [k m /h ]

We verify our vehicle model on a bench-mark from three aspects, i.e., the vehicle speed, fuel consumption, and battery SoC, as shown in Fig. 13. It can be seen that the speed in simulation matches the speed with the benchmark testing well. Both the fuel consumption and SoC in the simulation are basically consistent with that of the benchmark testing.

To better investigate the effectiveness of AECMS-style on fuel economy and charge sustainability, three moderate drivers and two aggressive drivers participated to evaluate the performance through comparing with the standard ECMS. Because the optimal EF for ECMS cannot be pre-determined without knowing the entire drive cycle [40] in real conditions, we set the constant part of (29) as an approximation of the optimal EF. Thus the initial EF of the standard ECMS and the proposed AECMS-style is s0 = 2.6. We convert the change of the battery electrical energy into equivalent fuel consumption for comparison since the SoC value at the end of each random urban cycle is not exactly equal to the reference SoC (SoCr = 0.6). The changes of battery electrical energy were calculated by (32) and the fuel consumption is referred to as the equivalent fuel consumption, computed by (33). E∆SoC = Ncell · (SoCf − SoCr ) · Qmax Z · Vcell d(1 − SoC(t))

ξ=

FC −

E∆SoC Qdiesel ·ηdiesel ·ηe ·ηm

(32)

(33)

5 0

ρdiesel · dcycle

4 0

where E∆SoC is the electrical energy caused by the SoC change, SoCf is the SoC at the end of random urban cycle, Ncell is the number of cells, Vcell is the open-circuit voltage of the cell, ξ is the equivalent fuel consumption, F C is the fuel consumption of the engine, Qdiesel is the heating value of the diesel, ηdiesel is the mechanical efficiency of the diesel engine, ρdiesel is the diesel density, and dcycle is the driving distance. Here, λ is estimated at about 0.1 through tentative simulations with s0 = 2.6. To fully investigate the performance of AECMS-style, different constant coefficients, λ = 0.05, 0.1, 0.15, are assessed for these five drivers. In order to show a clear comparison, the detailed simulation results of the moderate drivers and the aggressive drivers are listed in Table IV and V, respectively. For the three moderate drivers, AECMS-style provides a better fuel economy and charging balance than that of using ECMS. For example, AECMSstyle improves the equivalent fuel consumption for Driver #1 by 12.173% (λ = 0.15), compared with ECMS, and a satisfied final SoC of 0.6061. One of the primary reason is

3 0 2 0 1 0 0

0

2 0 0

4 0 0

6 0 0 8 0 0 T im e [s ]

1 0 0 0

1 2 0 0

1 4 0 0

(d) Velocity curve. Fig. 13. Results of the bench-mark test.

( δa max(Tm req (t), Tmax char (t)), S(t) > 0 Tm (t) = (31) δm max(Tm req (t), Tmax char (t)), S(t) < 0 where δa and δm are the overload coefficients for aggressive and moderate driving styles, respectively. Based on this, we can decrease the mechanical braking torque of aggressive divers by setting a large δa , thus reducing the energy losses. Moreover, we can reduce the recovery torque of the motor by setting a small δm to decrease the high SoC for moderate drivers. Shen [42] tested that PMSM can maintain at least 350



TABLE IV S IMULATION R ESULTS FOR M ODERATE D RIVERS W ITH λ = 0.05, 0.1, 0.15. Moderate Drivers Driving style 1 λ ξ [L/100km]

SoCf 3

Driving charging

Mean mechanical braking force ×103 [N] Mechanical braking energy ×107 [J] 1 2 3 4

The The The The

ECMS AECMS-style Improvement ECMS AECMS-style Decrement2 ECMS AECMS-style Decrement 4 ECMS AECMS-style Decrement ECMS AECMS-style Decrement

Moderate Driver 87.01% 0.05 0.1 42.42 40.98 39.17 3.39% 7.66% 0.655 0.645 0.6292 0.01 0.0258 30.45% 29.42% 27.06% 1.03 3.39 3.0921 2.9797 2.9799 3.64% 3.63% 1.8738 1.8178 1.8179 2.99% 2.98%

#1 0.15 37.02 12.73% 0.6061 0.0489 23.54% 6.91 2.9784 3.68% 1.8131 3.24%

Moderate Driver 72.37% 0.05 0.1 43.84 42.88 41.96 2.19% 4.29% 0.6207 0.6202 0.6067 0.0005 0.014 23.46% 22.44% 21.24% 1.02 2.22 3.7293 3.5305 3.5306 5.33% 5.33% 2.3655 2.2519 2.2527 4.80% 4.77%

#2 0.15 41.13 6.18% 0.5969 0.0176 19.95% 3.51 3.5328 5.27% 2.2522 4.79%

Moderate Driver 66.59% 0.05 0.1 42.54 41.49 40.55 2.47% 4.68% 0.6204 0.6199 0.6081 0.0005 0.0123 22.86% 21.29% 20.10% 1.57 2.76 3.5582 3.2057 3.2064 9.91% 9.89% 2.2095 2.0784 2.0803 5.93% 5.85%

#3 0.15 39.39 7.40% 0.5931 0.0135 18.27% 4.59 3.2140 9.67% 2.0843 5.67%

proportion of moderate driving style in the whole process with T = 1s. decrement here refers to the decrement of the difference between SoCf and SoCr = 0.6 proportion of driving charging mode in the whole process. decrement here refers to the reduced percentage points. TABLE V S IMULATION R ESULTS FOR AGGRESSIVE D RIVERS W ITH λ = 0.05, 0.1, 0.15. Aggressive Drivers Driving style1 λ ξ [L/100km]

SoCf Driving charging2 Mean mechanical braking force ×103 [N] Mechanical braking energy ×107 [J] 1 2 3 4

The The The The

ECMS AECMS-style Improvement ECMS AECMS-style Improvement3 ECMS AECMS-style Increment4 ECMS AECMS-style Decrement ECMS AECMS-style Decrement

Aggressive Driver #1 73.05% 0.05 0.1 0.15 48.04 47.10 47.20 47.14 1.96% 1.75% 1.87% 0.5224 0.5353 0.5354 0.5346 0.0129 0.0130 0.0122 10.44% 11.27% 11.10% 10.92% 0.83 0.66 0.48 4.9833 4.6542 4.6897 4.6822 6.60% 5.89% 6.04% 3.3078 3.1203 3.1607 3.1604 5.67% 4.45% 4.46%

Aggressive Driver #2 61.65% 0.05 0.1 0.15 41.54 41.04 40.08 40.75 1.20% 3.51% 1.90% 0.5815 0.5922 0.5890 0.5858 0.0107 0.0075 0.0043 15.20% 14.78% 14.29% 13.72% -0.42 -0.91 -1.48 3.9375 3.5997 3.5893 3.5780 8.58% 8.84% 9.13% 2.5431 2.3745 2.3685 2.3623 6.63% 6.87% 7.11%

proportion of aggressive driving style in the whole process with T = 1s. proportion of driving charging mode in the whole process. improvement here refers to the increment of the difference between SoCf and SoCr = 0.6 increment here refers to the increased percentage points.

that AECMS-style significantly reduces the driving charging mode (30.45% → 23.54%), thus decreasing the energy losses in conversion process from fuel to electricity and a high SoC. Another main reason is that AECMS-style can reduce the mean mechanical braking force (by 3.68%) and decrease the braking energy losses. For aggressive drivers, AECMSstyle also provides a better performance in fuel economy and charging balance than ECMS. But the improvement of aggressive drivers is obvious lower than that of moderate drivers. This is because the aggressive drivers’ driving habits waste too much braking energy in sharp decelerations. With the increasing of λ, both the fuel economy and charging sustainability of moderate drivers are improved, but aggressive drivers have no obvious changing regular pattern. Synthetically considering the equivalent fuel consumption and final SoC,

λ = 0.15 is the best choice for AECMS-style. To clearly illustrate the effectiveness of AECMS-style, we take moderate diver #3 with λ = 0.15 for an example. The adjustment results of EF following driving style are shown in Fig. 14. The profiles of SoC and fuel consumption are shown in Fig. 15 and 16, respectively. It can be seen that AECMSstyle obtains a more stable SoC and a better fuel economy, compared to ECMS. Considering the magnified part in Fig. 14 and 15, the process of adapting to EF based on driving style is explained as follows. The driver tends to drive in a moderate way during 140 s∼180 s. Accordingly, the EF of AECMS-style is decreasing from 2.6 to 2.45 according to the recognition driving style level (S(t) = −1), as shown in Fig. 14. Consequently, AECMS-style will exploit more electricity to reduce the fuel consumption, thereby preventing the battery



2.6

2.75

2.45

2.6 140

160

180

55

ECMS AECMS-style 600

610

620

ECMS AECMS-style

50 9 [L/100km]

3

S(t)

2.8

2.6

30 0

200

400

600 t [s]

800

1000

1200

Fig. 14. EF of the two strategies for the moderate driver 3# with λ = 0.15.

0.655

0.608 0.606

0.64

0.602

0.62

0.598 130 165 200

SoCf

40 35

2.4

ECMS AECMS-style

0.6

0.615 0.61

0.58

0.6

0.56

45

0.59 0

200

400

600 t [s]

800

1000

1200

Fig. 15. SoC of the two strategies for the moderate driver #3 with λ = 0.15.

overcharge. In ECMS, the EF always stays the fixed value of 2.6 and the control strategy still requires more fuel, which then overcharges the battery, as shown in Fig. 15. During 590 s∼ 620 s, on the contrary, the driver tends to drive in an aggressive way and AECMS-style increases EF from 2.6 to 2.75 according to the driving style level (S(t) = 1). This causes a higher cost of electricity so that the SoC increases faster than ECMS as shown in Fig. 15. VI. C ONCLUSIONS This paper proposes an adaptive energy management strategy for HEVs considering driving style, wherein a statistical pattern recognition method to quantify driving styles into six levels from moderate to aggressive was developed, with recognition accuracy of 87.01% and 69.61% for moderate and aggressive drivers, respectively. Additionally, the underlying relationship between driving style and fuel consumption as well as charging sustainability were revealed: 1) a sharp deceleration of aggressive drivers greatly leads to energy losses of mechanical braking; 2) a low SoC leads to a high fuel consumption of engine, and high-frequency accelerations lead to large transient losses of the engine; 3) the overmuch driving charging mode wastes

200

400

600 t [s]

800

1000

1200

Fig. 16. Fuel consumption of the two strategies for the moderate driver #3 with λ = 0.15.

too much energy in conversation from fuel to electric and overcharges battery for moderate drivers. According to these analysis and summaries, we proposed the AECMS-style method and designed an adaptive rule of EF to driving styles. Moreover, we also redesigned the braking strategy for the motor in driving charging mode with respect to different driving styles in order to reduce the mechanical braking losses for aggressive drivers. Finally, a validated experiment with five drivers was conducted to evaluate AECMS-style in a typical urban cycle, compared with ECMS. Experimental results show that AECMSstyle achieves an improvement of fuel economy by 1.20% ∼ 12.73% and charging sustainability by 0.0043 ∼ 0.0489. In particular, AECMS-style reduces the duration of driving charging mode by up to 6.91% and the energy losses of mechanical braking by up to 5.85% for moderate drivers. For aggressive drivers, the AECMS-style reduces the energy losses of mechanical braking by up to 7.11%. R EFERENCES [1] Q. Jiang, F. Ossart, and C. Marchand, “Comparative study of real-time hev energy management strategies,” IEEE Transactions on Vehicular Technology, vol. 66, no. 12, pp. 10 875–10 888, 2017. [2] S. F. Tie and C. W. Tan, “A review of energy sources and energy management system in electric vehicles,” Renewable and Sustainable Energy Reviews, vol. 20, no. 4, pp. 82–102, 2013. [3] D. Fredette and U. Ozguner, “Dynamic eco-driving’s fuel saving potential in traffic: Multi-vehicle simulation study comparing three representative methods,” IEEE Transactions on Intelligent Transportation Systems, pp. 1–9, 2017. [4] A. Stevanovic, J. Stevanovic, K. Zhang, and S. Batterman, “Optimizing traffic control to reduce fuel consumption and vehicular emissions: Integrated approach with vissim, cmem, and visgaost,” Transportation Research Record Journal of the Transportation Research Board, vol. 9, no. 2128, pp. 105–113, 2009. [5] X. Z. Tang, S. J. Yang, and H. C. Xia, “Control strategy of hybrid electric vehicles based on driving style identification,” Advanced Materials Research, no. 945-949, pp. 1587–1596, 2014. [6] D. Qin, S. Zhan, Y. Zeng, and L. Su, “Management strategy of hybrid electrical vehicle based on driving style recognition,” Journal of Mechanical Engineering, vol. 52, no. 8, pp. 162–169, 2016. [7] J. E. Meseguer, K. T. Chai, C. T. Calafate, J. C. Cano, and P. Manzoni, “Drivingstyles: a mobile platform for driving styles and fuel consumption characterization,” Journal of Communications and Networks, vol. 19, no. 2, pp. 162–168, 2017.



[8] N. Fonseca Gonzlez, J. Casanova Kindeln, and F. Espinosa Zapata, “Influence of driving style on fuel consumption and emissions in dieselpowered passenger car,” pp. 1–6, 2010. [9] J. Thomas, S. Huff, B. West, and P. Chambon, “Fuel consumption sensitivity of conventional and hybrid electric light-duty gasoline vehicles to driving style,” vol. 10, no. 3, 2017. [10] B. V. Padmarajan, A. Mcgordon, and P. A. Jennings, “An investigation on the effect of driver style and driving events on energy demand of a phev,” World Electric Vehicle Journal, vol. 4, no. 1, 2012. [11] L. Feng and B. Chen, “Study the impact of drivers behavior on the energy efficiency of hybrid electric vehicles,” in ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 2013, p. V004T08A040. [12] E. Kim and E. Choi, “Estimates of critical values of aggressive acceleration from a viewpoint of fuel consumption and emissions,” in Transportation Research Board 92nd Annual Meeting, 2013. [13] R. Wang and S. M. Lukic, “Review of driving conditions prediction and driving style recognition based control algorithms for hybrid electric vehicles,” in Vehicle Power and Propulsion Conference, 2011, pp. 1–7. [14] Z. Liu, “Impacts of real-world driving and driver aggressiveness on fuel consumption of 48v mild hybrid vehicle,” Sae International Journal of Alternative Powertrains, vol. 5, no. 2, 2016. [15] L. R. Peter Bakhit, Dalia Said, “Impact of acceleration aggressiveness on fuel consumption using comprehensive power based fuel consumption model,” Civil and Environmental Research, vol. 7, no. 3, 2015. [16] E. Choi and E. Kim, “Critical aggressive acceleration values and models for fuel consumption when starting and driving a passenger car running on lpg,” International Journal of Sustainable Transportation, vol. 11, no. 6, pp. 395–405, 2017. [17] J. Son, M. Park, K. Won, Y. Kim, S. Son, A. Mcgordon, P. Jennings, and S. Birrell, “Comparative study between korea and uk: relationship between driving style and real-world fuel consumption,” International Journal of Automotive Technology, vol. 17, no. 1, pp. 175–181, 2016. [18] L. Xu, J. Hu, H. Jiang, and W. Meng, “Establishing style-oriented driver models by imitating human driving behaviors,” IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 5, pp. 2522–2530, 2015. [19] D. Chu, Z. Deng, Y. He, C. Wu, C. Sun, and Z. Lu, “Curve speed model for driver assistance based on driving style classification,” Iet Intelligent Transport Systems, vol. 11, no. 8, pp. 501–510, 2017. [20] W. Wang, J. Xi, and X. Li, “Statistical pattern recognition for driving styles based on bayesian probability and kernel density estimation,” ArXiv e-prints, 2016. [21] Y. L. Murphey, R. Milton, and L. Kiliaris, “Driver’s style classification using jerk analysis,” in Computational Intelligence in Vehicles and Vehicular Systems, 2009. CIVVS ’09. IEEE Workshop on, 2009, pp. 23– 28. [22] K. K. Sibi, R. Sunitha, and P. Pathiyil, “Driver classification for hybrid electric vehicles based on fuel consumption index,” in International Conference on Computation of Power, Energy Information and Commuincation, 2016, pp. 321–325. [23] C. M. Martinez, M. Heucke, F. Y. Wang, B. Gao, and D. Cao, “Driving style recognition for intelligent vehicle control and advanced driver assistance: A survey,” IEEE Transactions on Intelligent Transportation Systems, vol. 19, no. 3, pp. 666–676, 2018. [24] P. Brombacher, J. Masino, M. Frey, and F. Gauterin, “Driving event detection and driving style classification using artificial neural networks,” in IEEE International Conference on Industrial Technology, 2017. [25] W. Wang, J. Xi, A. Chong, and L. Li, “Driving style classification using a semi-supervised support vector machine,” IEEE Transactions on HumanMachine Systems, vol. 47, no. 5, 2017. [26] Y. Zhang and D. Kaber, “Evaluation of strategies for integrated classification of visual-manual and cognitive distractions in driving,” Human Factors, vol. 58, no. 6, 2016. [27] D. Drr, D. Grabengiesser, and F. Gauterin, “Online driving style recognition using fuzzy logic,” in IEEE International Conference on Intelligent Transportation Systems, 2014, pp. 1021–1026. [28] Z. Wei, Z. Xu, and D. Halim, “Study of hev power management control strategy based on driving pattern recognition ,” Energy Procedia, vol. 88, pp. 847–853, 2016. [29] C. Sun, F. Sun, and H. He, “Investigating adaptive-ecms with velocity forecast ability for hybrid electric vehicles,” Applied Energy, vol. 185, 2017. [30] H. K. Roy, A. Mcgordon, and P. A. Jennings, “A generalized powertrain design optimization methodology to reduce fuel economy variability in hybrid electric vehicles,” IEEE Transactions on Vehicular Technology, vol. 63, no. 3, pp. 1055–1070, 2014.

[31] F. Zhang, J. Xi, and R. Langari, “Real-time energy management strategy based on velocity forecasts using v2v and v2i communications,” IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 2, pp. 416–430, 2017. [32] G. Paganelli, S. Delprat, T. M. Guerra, J. Rimaux, and J. J. Santin, “Equivalent consumption minimization strategy for parallel hybrid powertrains,” in Vehicular Technology Conference, 2002. Vtc Spring 2002. IEEE, 2002, pp. 2076–2081 vol.4. [33] N. W. Kim, D. H. Lee, C. Zheng, C. Shin, H. Seo, and S. W. Cha, “Realization of pmp-based control for hybrid electric vehicles in a backward-looking simulation,” International Journal of Automotive Technology, vol. 15, no. 4, pp. 625–635, 2014. [34] D. P. Bertsekas, Dynamic Programming and Optimal Control: Approximate Dynamic Programming. Athena Scientific, 2012. [35] H. He, R. Xiong, H. Guo, and S. Li, “Comparison study on the battery models used for the energy management of batteries in electric vehicles,” Energy Conversion and Management, vol. 64, no. 4, pp. 113–121, 2012. [36] A. D. Rhodes, M. H. Quinn, and M. Mitchell, “Fast on-line kernel density estimation for active object localization,” ArXiv e-prints, 2017. [37] A. D. Wyner, Claude Elwood Shannon: collected papers. IEEE Press, 1993. [38] R. A. Dunne, A Statistical Approach to Neural Networks for Pattern Recognition (Wiley Series in Computational Statistics). WileyInterscience, 2007. [39] L. Serrao, S. Onori, and G. Rizzoni, “A comparative analysis of energy management strategies for hybrid electric vehicles,” Journal of Dynamic Systems Measurement and Control, vol. 133, no. 3, 2009. [40] H. Kazemi, Y. Fallah, A. Nix, and S. Wayne, “Predictive aecms by utilization of intelligent transportation systems for hybrid electric vehicle powertrain control,” IEEE Transactions on Intelligent Vehicles, vol. 2, no. 2, pp. 75–84, 2017. [41] A. Chasse, A. Sciarretta, and J. Chauvin, “Online optimal control of a parallel hybrid with costate adaptation rule,” IFAC Proceedings Volumes, vol. 43, no. 7, pp. 99–104, 2010. [42] S. Qiping, “Control strategy of hybrid electric vehicles based on driving style identification,” Ph.D. dissertation, Shenyang University of Technology, 2012.

Sen Yang received the B.S. degree in vehicle engineering from Hubei University of Automotive Technology, Hubei, China, in 2014. He is currently working toward the Ph.D. degree in mechanical engineering with Beijing Institute of Technology (BIT), Beijing, China. His research interests include driver model with artificial intelligence, pattern recognition of human driver characteristics, and human-intelligent vehicle collaboration.

Wenshuo Wang (S’15) received his B.S. degree from ShanDong University of Technology in 2012 and Ph.D. degree in Mechanical Engineering from Beijing Institute of Technology (BIT) in June 2018. He studied as a Research Scholar at University of California at Berkeley (UCB) from September 2015 to August 2017 and at University of Michigan (UM), Ann Arbor from September 2017 to July 2018. Currently, he works as a PostDoc at the Department of Mechanical Engineering, Carnegie Mellon University (CMU), Pittsburgh, PA. His research interests include driver model, human-vehicle interaction, machine learning, nonparametric Bayesian learning, recognition and application of human driving characteristics.



Fengqi Zhang received his M.E. in vehicle engineering from Lanzhou Jiaotong University, Lanzhou, Gansu province, in 2012, and the Ph.D. degree in vehicle engineering from Beijing Institute of Technology, Beijing, China, in 2016. He is currently a Lecturer in School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an, China. His research interests include modeling, optimal control of hybrid vehicles and powertrain control.

Junqiang Xi received the B.S. degree in automotive engineering from Harbin Institute of Technology, Harbin, China, in 1995, and the Ph.D. degree in vehicle engineering from Beijing Institute of Technology (BIT), Beijing, China, in 2001. In 2001, he joined the State Key Laboratory of Vehicle Transmission, BIT. During 2012–2013, he conducted research as an Advanced Research Scholar with the Vehicle Dynamic and Control Laboratory, Ohio State University, USA. He is currently a Professor and Director of the Automotive Research Centre, BIT. His research interests include vehicle dynamic and control, powertrain control, mechanics, intelligent transportation systems, and intelligent vehicles.

Yuhui Hu received his Ph.D. from Beijing Institute of Technology, Beijing, China, in 2008. He is currently an Associate Professor at Beijing Institute of Technology. His research interests include modeling, optimal control of hybrid vehicles and electric vehicles and powertrain control.


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