City-Bus-Route Demand-based Efficient Coupling Driving Control for Parallel Plug-in Hybrid Electric Bus
Wang et al. Chin. J. Mech. Eng.
City-Bus-Route Demand-based Efficient Coupling Driving Control for Parallel Plug-in Hybrid Electric Bus
Qin‑Pu Wang 2
Chao Yang 0 1
Ya‑Hui Liu 0 1
Yuan‑Bo Zhang 0 1
0 State Key Laboratory of Automotive Safety and Energy, Tsinghua University , Beijing 100084 , China
1 State Key Laboratory of Automotive Safety and Energy, Tsinghua University , Bei‐ jing 100084 , China
2 Zhongtong Bus Hold Co., Ltd. , Liaocheng 252000, Shandong , China
Recently, plug‑ in hybrid electric bus has been one of the energy‑ efficient solutions for urban transportation. However, the current vehicle efficiency is far from optimum, because the unpredicted external driving conditions are difficult to be obtained in advance. How to further explore its fuel‑ saving potential under the complicated city bus driving cycles through an efficient control strategy is still a hot research issue in both academic and engineering area. To realize an efficient coupling driving operation of the hybrid powertrain, a novel coupling driving control strategy for plug‑ in hybrid electric bus is presented. Combined with the typical feature of a city‑ bus‑ route, the fuzzy logic inference is employed to quantify the driving intention, and then to determine the coupling driving mode and the gear‑ shifting strategy. Considering the response deviation problem in the execution layer, an adaptive robust controller for electric machine is designed to respond to the transient torque demand, and instantaneously compensate the response delay and the engine torque fluctuation. The simulations and hard‑ in‑ loop tests with the actual data of two typical driving conditions from the real‑ world city‑ bus‑ route are carried out, and the results demonstrate that the proposed method could guarantee the hybrid powertrain to track the actual torque demand with 10.4% fuel economy improvement. The optimal fuel economy can be obtained through the optimal combination of working modes. The fuel economy of plug‑ in hybrid electric bus can be significantly improved by the proposed control scheme without loss of drivability.
Hybrid electric vehicle; Single‑ shaft parallel electromechanical powertrain; Coupling driving mode; Adaptive robust control
As representative of new energy vehicles, plug-in hybrid
electric vehicle is always a hot topic in the field of recent
vehicle technology . Especially in areas of urban
public transport, the excellent performance of low energy
consumption and low emissions makes the plug-in
hybrid electric bus (PHEB) become the primary
solution [2, 3]. Recently, with the application of the idle-stop
technology , the all-electric range of PHEB might be
extended by utilizing more pure electric driving .
Considering the traffic congestion in the big city of China,
the vehicle launch and accelerating condition might
frequently appear in the driving cycles of the city bus .
However, in most cases the electric energy stored in the
PHEB might not cover the whole city-bus-route, the
optimal coordinated operation between the engine and
the electric machine (EM) is very worthy of study .
Because of the configuration features, the coordinated
control becomes very difficult especially for the
singleshaft parallel hybrid powertrain with the automated
mechanical transmission (AMT) [8–10]. To solve this
problem, several solutions have been presented for
realtime optimization of the steady-state energy flows
utilizing dynamic programming presented by Li et al. 
and Lin et al. , equivalent consumption minimization
strategy presented by Yang et al.  and Geng et al. ,
or model predictive control (MPC) strategy presented by
Yan et al. . Nevertheless, the transient process, such
as the complicated electromechanical coupling working
mode and the multi-modes transition, was not
considered in these control strategies.
During a vehicle launch and accelerating process,
PHEB might fulfill an electromechanical
coupling-driving mode after a pure electric driving mode to ensure
the operation efficiency until the engine torque
satisfies the demand torque on a high efficient zone [16–18].
Therefore, the coupling driving mode, which refers to the
hybrid driving mode or the engine active charging mode,
is crucial for the vehicle launch and accelerating process
of PHEB .
Considering the difference between dynamic
characteristics of the engine and the EM, it is necessary to
further study the coordinated control method for an
efficient solution of the hybrid powertrain. Therefore,
a novel torque-demand control approach based on the
MPC was proposed by He et al. , to implement the
torque control of parallel hybrid powertrain. In
addition, for a parallel hybrid powertrain, the coordinated
control method using dynamic input allocation, MPC,
and sliding mode control method presented by Cordiner
et al. , Minh et al. , and Metin et al. ,
respectively. Using the fast response behavior of the EM, an
electromechanical coupling driving control scheme was
proposed by Yang et al. [23, 24] to achieve good torque
The coordinated control strategies can ensure the
torque tracking performance during a coupling
driving process. However, the instantaneous variation of the
traffic flows, road conditions, and the passenger loads in
a city-bus-route, might greatly affect the robustness of
the control system. To adaptively deal with the stochastic
driving intention, a city-bus-route demand-based
coupling driving control approach is designed for the
singleshaft parallel PHEB with AMT. Firstly, the time-varying
driving intention is quantified with a fuzzy logic, and
then the coupling driving mode and the AMT
gear-shifting strategy are determined with a strategy determination
module. Secondly, considering the dynamic
characteristics of the EM, the adaptive robust controller is designed
for the EM to respond to the transient torque demand.
Meanwhile, the response deviation and the transient
fluctuation of the engine torque are compensated with
the fast response behavior of the EM.
The rest of the paper is organized as follows: Section 2
gives the models of the single-shaft parallel PHEB. The
efficient coupling driving control approach is developed
in Section 3. The results of simulation and hard-in-loop
(HIL) test are given in Section 4. Finally, the conclusion
and discussion are given in Section 5.
2 Model Descriptions
A typical single-shaft parallel hybrid powertrain is
illustrated as Figure 1.
As shown in Figure 1, the EM is placed between the
output of clutch and the input of AMT, and the clutch
could implement the mode transition of this powertrain
with engagement and disengagement operations. AMT
could assist the vehicle to adapt to the demand from
different conditions, and help the engine and the EM to
work in their efficient zones as well. Furthermore, the
parameters of the studied PHEB are shown in Table 1.
2.1 Energy Demand Analyses of City Bus Route
During the operation, PHEB with that powertrain
could fulfil six basic working modes, including idling
stop mode, pure electrical driving mode, engine
driving mode, hybrid driving mode, engine active charging
mode, and regenerative braking mode. The diagram of
the PHEB energy demand corresponded to the working
modes mentioned above is shown in Figure 2. As shown
in Figure 2, between two bus stops, the expected engine
operation in PHEB might contain two states,
engineoff and engine high-efficiency operation. The engine-off
state occurs at the idling stop mode, pure electrical
driving mode, and the regenerative braking mode. With the
increasing driving demand torque the engine will be
started and engaged into the driveline. Then the
problem of fuel saving becomes the optimization problem of
engine working points. It should be noted that the EM
might be utilized to assist the engine running in its
highefficiency area both in the hybrid driving mode and the
engine active charging mode. Thus, those two modes are
very important for improving the fuel economy of PHEB.
2.2 Diesel Engine Model
The simplified model of the diesel engine is employed for
the torque control, which could be shown in Figure 3.
Figure 1 Diagram of the single‑shaft parallel PHEB
Actuator EM EM
In Figure 3, φ represents the accelerator pedal
position. Je is the moment of inertia of the crankshaft; ωe is
the rotational speed of the crankshaft; φ(dωe/dt) is the
dynamic compensation factor; ke and τe are the
proportional coefficient and the time constant, respectively; Te
and TL are the effective torque and the load torque of the
engine, respectively; Tes is the static torque of the engine,
which could be obtained from the engine map; ΔTe
represents a dynamic correction item of the engine torque,
which could be described as follows:
Te = Je ddωte · ϕ
Jeω˙ e = Te − TL,
Te = Tes − Te − f1(dφ/dt).
Only considering the rotational dynamics of the
crankshaft, the engine model could be written as follows:
2.3 EM Model
The control-oriented dynamic model of the EM
consists of two modes: the driving mode and the generating
mode. In order to realize the transient torque tracking
control, the axis models are employed, when the EM
operates in the driving mode:
dωm = J1m Tm − BJmμ ωm − J1m Tl,
ddditdt = − RLs id + Pωmiq + L1 ud,
dditq = − RLs iq − Pωmid − PLΦ ωm + L1 uq.
When the EM operates in the generating mode:
ddditdt = − RLs id + Pωmiq − L1 ud,
dωm = J1m Te − J1m Tm − BJmμ ωm − J1m Tl,
dditq = − RLs iq − Pωmid + PLΦ ωm − L1 uq,
where id and iq are the d and q axis stator currents,
respectively; ud and uq are the d and q axis stator
voltages, respectively; Rs, L, P and Φ represent the stator
resistance, the stator inductance, the number of the pole
pairs, and magnet’s flux linkage, respectively; Jm and Bμ
are the moment of inertia of the EM output shaft and the
damping coefficient, respectively; Te is the engine torque
when the PHEB runs in the active charging mode, and Tm
is the EM torque, which can be described as the following
3 City‑Bus‑Route Demand‑based Efficient
Coupling Driving Control Strategy
In this section, a novel city-bus-route demand-based
coupling driving control strategy is presented. Firstly,
the fuzzy logic controller quantifies the driver’s driving
intention. Then the AMT gear-shifting strategy and the
coupling driving mode are determined with the
quantified driving intention in the strategy determination
module. Secondly, the designed PI controller for engine and
adaptive robust controllers for EM implement the torque
tracking control in the coupling driving mode. Moreover,
considering the properties of the single-shaft parallel
hybrid powertrain, the response error of engine torque is
compensated by the accurate EM torque control to
guarantee the torque tracking performance of powertrain.
3.1 Fuzzy Logic Inference for Driving Intention
The driving intention is generated from the driver’s
maneuvers during the PHEB accelerating process.
However, it is difficult to describe the intention accurately
by the mathematical expressions. Therefore, the fuzzy
logic based on the test data and experience is adopted
to implement the quantification of the driving
intention. Diagram of the fuzzy logic controller is shown in
As shown in Figure 4, the input variables are vehicle
speed vveh, the relative accelerator pedal position φrel, and
the absolute value of the change rate of accelerator pedal
position dφ/dt, the output variable is driving intention Id.
The relative accelerator pedal position could be obtained
by the equation as follows:
100 × φ − φequ ,
where φ is actual accelerator pedal position, φequ is
equilibrium accelerator pedal position reflecting the
accelerator pedal position which maintains the vehicle driving
on flat road with a uniform speed, and the value of φequ
might be obtained through looking up the steady-state
table with the inputs of the engine torque and the engine
rotational speed, and the engine torque could be obtained
by the vehicle longitudinal dynamics equation as follows:
Te = (Ff + Fw)r ηTigif,
where Ff and Fw are the rolling resistance and the
aerodynamic drag, respectively. r, ηT, ig, and if are wheel radius,
transmission efficiency, AMT gear ratio, and differential
According to the test data of actual vehicle, the fuzzy
logic rule base shown in Table 2 can be obtained. In
Figure 5, the memberships of vveh are L, M, and H, which
represent the low speed, the middle speed, and the high
igif r ωe
d u dφ dt
Figure 4 Diagram of the fuzzy logic controller
Fuzzy rule base
Fuzzy logic inference
speed, respectively. The memberships of frel are Nb, Ns,
Z, Ps, and Pb, which are the negative big, the negative
small, the zero, the positive small, and the positive big
of the equilibrium accelerator pedal open, respectively.
The memberships of df/dt are S, Mi, and Bi, which are
the small, the middle, and the big of the change rate of
accelerator pedal open, respectively. Moreover, the
output variable Id obtained by the defuzzification are
quantified as St, D, K, A, and B, which represent the intention of
stop, decelerating, keep, accelerating, urgent accelerating.
3.2 Balanced AMT Gear‑shifting Strategy and Driving
Mode Determination Module
The double-parameters balanced AMT gear-shifting
strategy (BGS), which balances the dynamic gear-shifting
(DGS) maneuver and the economic gear-shifting (EGS)
maneuver, is employed. This strategy tends to dynamic
or economic depending on the driving intention, which
could be quantified by the designed fuzzy inference.
Taking the effects of engine operating points for
example, the full DGS ensures that engine might work on the
external characteristic line, and the full EGS reflects that
engine would work on the optimal operating line with
the highest efficiency. According to the quantified
driving intention, the AMT gear-shifting maneuver might
be determined that the gear-shifting maneuver tends to
DGS with urgent accelerating intention and conversely
tends EGS with keep and normal accelerating intention.
Therefore, the energy-efficient operation of PHEB
without drivability loss might be fulfilled by the proposed
is the driving intention when the engine engages into the
driveline at the time tswitch, and Ith represents the logic
threshold of driving intention, and its value could be
obtained by repeated tests.
According to the description of PHEB working modes,
the coupling driving modes can ensure the efficient
operation of PHEB. As shown in Figure 7, the engine
working points are improved into its high-efficiency area by
EM in the coupling driving modes. However, during the
simultaneous operation of engine and EM, the
difference of dynamic characteristics between the engine and
the EM causes the torque deviation of powertrain, which
might significantly influence the drivability of PHEB. In
this paper, the coordinated control scheme utilizes the
EM to compensate the torque response deviation. Thus,
the EM controller is very important that some
uncertainties should be taken into account in the controller design.
Then the design of the torque controllers for the engine
and EM will be given in the next two parts.
3.3 PI torque Controller for Diesel Engine
In order to response the demand torque, a PI torque
controller is designed. The expression can be written as:
where ueng is the engine control input that represent the
fuel injection quantity, Tre is demand powertrain speed,
kp, ki are proportional and integral gains, respectively.
3.4 Adaptive Robust Controllers for the EM Torque
To ensure the torque balance of the whole powertrain,
the EM control system design might become the chief
task. Then the objective of EM controller becomes that
ensures the accurate trajectory tracking control with the
existence of model uncertainties and external
disturbances. First, the error models of EM in the driving mode
and generating mode are necessary. Therefore, the error
variables can be defined as follows:
x1 x2 x3
x¯1 x¯2 x¯3 T
ωm − ωmr id − idr iq − iqr T,
ωm − ωm∗ id − id∗ iq − iq∗ T,
0 20 40 60 80
Change rate of pedal position dφ /dt
(c) Change rate of accelerator pedal position
Driver's intention Id
(d) Driver’s intention
Combined with the quantified driving intention, the
AMT gear-shifting strategy and the mode selection
during the coupling driving mode can be determined, the
logic of which is shown in Figure 6. In Figure 6, Id(tswitch)
where x and x¯ are the error variables when the EM works
in the driving mode and generating mode, respectively.
ωrm, ird, irq are the desired values of the EM speed, the
z = [ ρ1x1 ρ2x2 ρ3x3 ]T,
z¯ = [ ρ¯1x¯1 ρ¯1x¯1 ρ¯1x¯1 ]T,
θ1 = BJmμ , θ2 = RLs ,
τ1 = BJmμ , τ2 = RLs ,
θ˜i and τ¯i(i = 1, 2) are the error between the uncertain
parameters and the adaptive estimated value, which
would be described in later part. Moreover, w1, w2
represent the load disturbances of EM control system. Then
the performance vectors are defined as follows:
where ρi and ρ¯i (i = 1, 2, 3) are the weighting factors. TE
is an error term. Moreover, u1, u2, u3, u4 are equivalent
control inputs described as follows:
d-axis, and the q-axis stator currents when the EM
operates in the driving mode, respectively. ω*m, i*d , i*q are the
desired values of the EM speed, the d-axis, and the q-axis
stator currents when the EM operates in the
generating mode, respectively. Combining with the EM model
described in Eqs. (
) and (
), the error equations that
have considered the uncertainties can be defined when
EM operates in the driving mode:
x˙1 = 32PJmΦ x3 − θ1x1 − θ˜1ωmr − w1,
x˙2 = −θ2x2 + P(x1x3 + x1iqr + ωmrx3) + u1,
x˙3 = −θ2x3 − P(x1x2 + ωmrx2) − PLΦ x1 − θ˜2iqr + u2.
When EM operates in the generating mode:
x¯˙1 = TE − 32PJmΦ x¯3 − τ1x¯1 − τ˜1ωm∗ − w2,
x˙¯2 = −τ2x¯2 + P(x¯1x¯3 + e1iq∗ + ωm∗x¯3) + u3,
x¯˙2 = −τ2x¯3 − P(x¯1x¯2 + ωm∗x¯2) + PLΦ x¯1 − τ˜2iq∗ + u4,
where the uncertainty parameters θ1, θ2, τ1, and τ2 are
given as follows:
u1 = Pωmriqr + L1 ud,
u2 = θˆ2iqr − PLΦ ωmr − ˙iqr + L1 uq,
u3 = Pωm∗iq∗ − L1 ud,
u4 = τ˜2iq∗ + PLΦ ωm∗ − ˙iq∗ − L1 uq,
Then the control objective of EM controller becomes
that the closed-loop system is stable with L2-gain, that is,
when w ≠ 0, the system from the disturbance inputs wi
(i = 1, 2) to the penalty outputs z and z¯ has finite L2-gain
not larger than γi (i = 1, 2).
00TT zz¯((tt)) 22ddtt ≤≤ γγ21 00TT
where T > 0 is any given scalar. γi (i = 1, 2) are the
evaluating factors. Thus, regarding the system described in
) and (
), an adaptive robust controller for EM is
designed when EM operates in the driving mode:
u1 = θˆ2x2 − P(x1x3 + x1iqr + ωmrx3) − k2x2,
PΦ − 32PJmΦ x1 + θˆ2x3 + P(x1x2 + ωmrx2)
+ ∂∂αx11 32PJmΦ x3 − ∂∂αx11 θˆ1x1 − ∂∂αx11 θˆ2 + ∂∂αθˆ11 θˆ˙1
− 2γ112 Z1 ∂∂αx11 2 − k3Z1.
And the parameter adaptive update laws are chosen as
θˆ˙2 = χ2(−x3Z1 − x22 − iqrZ1).
θˆ˙1 = χ1 ∂∂αx11 x1Z1 − x12 − ωmrx1 + ∂∂αx11 ωmrZ1 ,
where ki(i = 1, 2, 3) and k¯i(i = 1, 2, 3) are adjustable
parameters of the controller, Z1 = x3 − α1(x1, θˆ1) and
Z2 = x¯3 − α2(x¯1, τˆ1), α1(x1, θˆ1) and α2(x¯1, τˆ1) are virtual
controllers which could be chosen as follows:
α1(x1, θˆ1) = 32PJmΦ θˆ1x1 − k1x1 − 2γ112 x1 ,
α2(x¯1, τˆ1) = 32PJmΦ
TE − τˆ1x¯1 + k¯1x¯1 + 2γ122 x¯1 .
In addition, χi(i = 1, 2) and βi(i = 1, 2) are adjustable
parameters of adaptive laws.Taking the driving mode of
EM for example, the adaptive robust controller described
in Eqs. (
) and (
) might be designed by the close-loop
system Lyapunov stability analysis. First, a positive
definite Lyapunov function is defined as follows:
V = 21 x12 + 12 x22 + 21 Z12 + 1 θ˜TΓ −1θ˜.
Then, its time derivative could be calculated as follows:
V = x1
x3 − θ1x1 − θ˜1ωmr − w1
+ x2[−θ2x2 + P(x1x3 + x1iqr + ωmrx3) + u1]
+ Z1[−θ2x3 − P(x1x2 + ωmrx2) −
− ∂α1 3PΦ
x3 − θ1x1 − θ˜1ωmr − w1
x1 − θ˜2iqr + u2
− ∂α1 θ˙ˆ1 .
Remark: For the external disturbance w1, the inequality
transform might be used which can be written as follows:
where the adjustable gains ki(i = 1, 2, 3) should satisfy the
conditions as follows:
Therefore, combing with the LaSalle invariant set
principle, it can be concluded that the designed controller can
achieve the mentioned above control objectives. Because
the adaptive controller described in Eqs. (
) and (
for the generating mode of EM is deduced that is similar
with that of the driving mode of EM, so the proof
procedure will be omitted here.
4 Validation Results and Analysis
The simulation and HIL tests are performed on a
singleshaft parallel PHEB in MATLAB/Simulink. Basic
parameters are listed in Table 1. Moreover, control parameters
in Table 3 are selected among all parameters obtained
from repeatedly debugging model with the proposed
method. These selected parameters can show the best
To verify the effectiveness of the proposed
control approach, a driving condition of city-bus-route
613 in Chongqing, China, is selected as the simulation
A road section of city-bus-route is extracted from
KongGang-Square station to YuBei-Broadcast station
that the vehicle speed and the road grade information
are shown in Figure 8. It should be noted that, the grade
−x1w1 ≤ 2γ112 x12 + γ212 w12,
changes with the distance, so generally the
grade-distance curve is used to reflect the grade information of a
4.1 Driving Intention Quantification
To verify the intention recognition, the accelerator pedal
position and the actual gear position curve were collected
from the driving condition as shown in Figure 9.
As shown in Figure 9, two sections of routes are with
fully different driver’s maneuvers. The first section from
0 s to 117 s shows the higher acceleration and the larger
pedal open than that of the second section from 120 s
to 300 s. Therefore, the quantified driving intention is
shown as in Figure 10. The result shows that the
intention could essentially reflect the actual maneuver.
Moreover, the urgent accelerating intention appears in 4.5 s
and the rest is the normal accelerating intention.
Therefore, the threshold of Ith could be selected as 3.2. Because
the focus in this paper is the process of PHEB driving
mode, two vehicle launch process are extracted from the
selected driving condition and the results are shown in
Sections 4.2 and 4.3.
4.2 Results under Different Driving Intentions
A clear city-bus-route without traffic jams is simulated
in this part. The driver’s intention under this condition
inclines to drive through this section of road fast, so
according to the strategy determination described in
Section 3.2, the simulation results are shown in Figure 11.
As shown in Figure 11(a), the results of vehicle speed
obtained from the simulation are close to the test data,
and the deviation reflects the error between simulation
(a) Speed-time curve
50 100 150 200
(a) Actual pedal position
100 150 200
(b) Actual gear position
model and actual vehicle. With the increasing vehicle
speed, the AMT will execute the gear shifting
operation in accordance with BGS strategy. The engine torque
tracking performance can be ensured, which is shown
in Figure 11(c). The designed adaptive robust controller
can respond to the demand EM torque quickly and
accurately, as shown in Figure 11(d).
A congested city-bus-route is simulated to reflect the
driver maneuvers with the intention Id(tswitch) < Ith. The
simulation results are shown in Figure 12. As shown in
Figure 12(a), the vehicle speed of simulation is in
accordance with the test data, and with the intention of slow
driving, Figure 12(c) shows that the engine demand
torque is elevated by the generating torque of EM in the
driving condition of the low torque demand. In
addition, due to the engine response characteristics, the PI
controller with the control input of fuel injection cannot
eliminate the torque deviation. However, the proposed
coupling driving control approach utilizes the EM torque
to compensate the above torque deviation, and the good
tracking performance is ensured by the designed
adaptive robust controller for EM, the effect curves of which
is shown in Figure 12(d). Therefore, it can be concluded
that the efficient operation of PHEB is ensured. It should
be noted that Figure 11 and Figure 12 show two
different driving conditions with relevant driving intentions.
Under urgent driving condition, the test vehicle uses
less time than that under slow driving condition, when it
reaches the same speed. Thus, the timeline in Figure 11
shows less than that in Figure 12.
The working points of engine and EM in two
different driving intentions are shown in Figure 13. As seen
(b) Grade-distance curve
0 50 100 150
Figure 10 Result of driving intention
in Figure 13, the working areas of engine and EM are
improved by the coupling driving mode, especially in
engine active charging mode, the working points of
engine are moved into its high-efficiency area. To show
the improvement of working points more clearly, the
areas plot by the red dotted line are defined as the
highefficiency areas of engine and EM. The results show
that the working points of engine and EM are greatly
improved by the proposed control strategy.
4.3 HIL Test Results
To verify the real-time capability of the proposed control
method, the HIL test is carried out. The HIL test
platform is shown in Figure 14.
(b) Gear position
(a) Vehicle speed
5 10 15
(c) Engine torque tracking curves
T/Nme860000 235 192875 201 261209
As shown in Figure 14, the dSPACE real time test
system is employed for running the vehicle model. Hybrid
control unit (HCU) is the actual controller used in PHEB.
The PHEB model built in MATLAB/Simulink are loaded
into dSPACE through real time interface (RTI). The C
codes of proposed control strategy is generated with the
auto code function of real time workshop (RTW),
meanwhile, the controller drive program is compiled with the
main program in Tasking EDE tool. A PC is employed
with control desk to monitor the test operation. The test
is carried out in Chinese typical urban condition and its
results are shown in Figure 15.
The outstanding vehicle speed tracking performance
and the reasonable torque spilt effect can be shown in
Figure 15, which proves the proposed control strategy
is effective to the real time HIL test. To further show the
advantage, a rule-based control strategy is adopted as the
baseline strategy, which is commonly used in actual
vehicle . The FC and EC represent the fuel consumption
HIL test system
PHEB model Control strategy
Figure 14 HIL test platform
and the electricity consumption, respectively. The
average brake specific fuel consumption (BSFC) results reflect
the improvement of working points. As shown in Table 4,
the fuel consumption improvement is 10.4% under the
given driving condition.
) The driving intention is recognized by the designed
fuzzy logic inference.
) According to the quantified intention, the mode
selection method in the coupling driving mode and
AMT gear-shifting strategy given in the strategy
determination module by the pre-set threshold.
) The adaptive robust controller is designed for EM to
ensure the tracking effect with the uncertainties and
) The proposed control approach could guarantee the
torque tracking performance, and the fuel economy
can be improved 10.4% through adjusting the engine
working points under different driving intentions.
) The real time capability of the control approach has
been validated by HIL tests. The proposed control
method has the potential to apply in the actual vehicle.
Table 4 Comparison results of two control strategy
Q‑PW and CY were in charge of the whole trial; CY wrote the manuscript; Y ‑HL
and Y‑BZ assisted with sampling and laboratory analyses. All authors have read
and approved the final manuscript.
Qin‑Pu Wang, born in 1964, is the General Manager Assistant at Zhongtong
Bus Hold Co., Ltd., China. He received his bachelor degree from Xi’an Highway
Institute, China, in 1984. His research interests include design and manufacture
of hybrid electric vehicle. E‑mail: .
Chao Yang, born in 1986, is currently a postdoctoral fellow at State Key
Laboratory of Automotive Safety and Energy, Tsinghua University, China. He received
his PhD degree on Control Science and Engineering from Yanshan University,
China, in 2016. His research interests include energy‑ efficient control strategy
design for hybrid electric vehicles. E‑mail: .
Ya‑Hui Liu, born in 1980, is currently an associate professor at State Key Lab
oratory of Automotive Safety and Energy, Tsinghua University, China. He received
his bachelor degree from Jilin University, China, in 2003 and PhD degree from
Beihang University, China, in 2009, respectively. His research interests include
vehicle system dynamics, steering system and driver‑ vehicle system. E‑mail:
Yuan‑Bo Zhang, born in 1990, is currently working at State Key
Laboratory of Automotive Safety and Energy, Tsinghua University, China. His research
interests include regenerative braking control strategy for electrified vehicles.
The authors declare no competing financial interests.
Supported by National Natural Science Foundation of China (Grant No.
51605243), National Key Science and Technology Projects of China (Grant
No. 2014ZX04002041), and 1‑ class General Financial Grant from the China
Postdoctoral Science Foundation (Grant No. 2016M590094).
Springer Nature remains neutral with regard to jurisdictional claims in pub‑
lished maps and institutional affiliations.
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