Validation and Control Strategy to Reduce Fuel Consumption

for RE-EV

Wonbin Lee, Wonseok Choi, Hyunjong Ha, Jiho Yoo, Junbeom Wi, Jaewon Jung

and Hyunsoo Kim

School of Mechanical Engineering, Sungkyunkwan University, Suwon, Korea

Keywords: Range Extended Electric Vehicle (RE-EV), Equivalent Fuel Consumption, Optimal Operation Line.

Abstract: In this study, a control strategy of the target RE-EV was analysed using BMW i3 test data from Downloadable

Dynamometer Database (D

) at Argonne National Laboratory. In addition, vehicle model was developed

based on AVL Cruise and MATLAB/Simulink and validation of the developed model was carried out. Using

the simulation and test data, a control strategy which operates the engine on the optimal operation line was

proposed to reduce the fuel consumption. The performance of the engine control strategy was evaluated for

the city and highway driving cycle.

1 INTRODUCTION

As the regulations against CO

emission has been

strengthened, the demand of eco-vehicle has been

increasing. Electric vehicle (EV) exhausts no

emission, but its relatively short travel distance has

been pointed out as a major drawback (Pavlat, 1993).

Range extended-electric vehicle (RE-EV) is

considered to be a solution to overcome the short

travel distance of EV (Chih-Ming, 2013). RE-EV is a

series type plug-in hybrid vehicle (PHEV) in which

the internal combustion engine and generator are

added in EV (Wu, 2015). In series type, the engine is

used only to charge the battery through the generator

and the motor propels the vehicle using the battery

energy (Tate, 2008). Since the engine is the only

means to charge the battery when RE-EV drives, the

engine turned on/off timing (Pi, 2016), and how to

control the engine are the essential elements to

improve the fuel economy (Min, 2013).

In this study, the engine operation was

investigated for BMW i3 RE-EV using the

experimental data from Downloadable Dynamometer

Database(D

) at Argonne National Laboratory

(Anl.gov, 2015). In addition, dynamic model of the

RE-EV was obtained and a performance simulator

was developed based on AVL Cruise and

MATLAB/Simulink. The RE-EV model was

validated by comparing the test results for various

driving cycles. Using the simulation and experimental

results, an engine control algorithm was proposed to

improve the fuel economy.

2 MODELING AND VALIDATION

OF TARGET RE-EV

In Figure 1, the target RE-EV, BMW i3 is shown. The

target RE-EV consists of one engine, two

motor/generator, battery and reduction gear.

The target RE-EV utilizes charge depleting (CD)

mode and charge sustaining (CS) mode. In CD mode,

the vehicle is propelled by MG2 using the electric

power. In CS mode, the engine is turned on to operate

MG1 and the electric power of MG1 is charged in the

battery.

Figure 1: Vehicle configuration for RE-EV.

In Table 1, the vehicle specifications are shown.

Lee, W., Choi, W., Ha, H., Yoo, J., Wi, J., Jung, J. and Kim, H.

Validation and Control Strategy to Reduce Fuel Consumption for RE-EV.

DOI: 10.5220/0006232900520057

In Proceedings of the 3rd International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2017), pages 52-57

ISBN: 978-989-758-242-4

Table 1: Vehicle specifications for RE-EV (Insideevs.com,

2013).

Vehicle specifications

Engine

Max power(kW) 25

Max torque(Nm) 55

MG2 Max power(kW) 125

MG1 Max power(kW) 26.6

Battery

Battery energy (kWh) 22

Capacity(Ah) 60

Vehicle

Mass(kg) 1315

Tire radius(m) 0.33

2.1 Analysis of Test Data

In Figure 2, vehicle operating points and engine

operating points are shown. As shown in Figure 2,

When SOC is below 0.16, CD mode is changed to CS

mode that operates engine.

Figure 2: Vehicle and engine operating points.

In Figure 3, test data of the engine on/off for the

vehicle speed vs. battery SOC are shown (data from

ANL).

In CD mode, the engine is always off since only

the electric energy is used to propel the vehicle. It is

seen from Figure 3 that the engine on/off is

determined by the vehicle speed and battery SOC.

The engine is turned on when the battery SOC drops

below SOC=0.16. In CS mode, the engine is turned

on when the vehicle speed becomes higher than

20kph and turned off when vehicle speed is lower

than 10kph.

Figure 4 shows the engine speed vs. vehicle speed

for various battery SOC in CS mode. It is seen from

Figure 4 that the engine speed increases with the

vehicle speed. When the SOC is low, the engine is

operated at higher speed meanwhile the engine is

Figure 3: Points of engine turned on/off.

Figure 4: Engine speed vs. vehicle speed.

operated at lower speed for high SOC. It is noted that

the engine speed is maintained low when the engine

begins to operate at high SOC. This low engine speed

is considered to warm up the engine and catalyst

converter.

2.2 Modeling and Validation

The target RE-EV was modelled using Cruise. In

Figure 5, Cruise model is shown. Each module in

Figure 5 represents the dynamic model of the RE-EV

component based on mathematical equations

describing its characteristics. For the vehicle control,

MATLAB/Simulink based controller was developed

using test data. Co-simulation was performed using

the Cruise vehicle model and MATLAB/Simulink

controller.

In Figure 6 and Figure 7, simulation results are

compared with the test results for UDDS cycle (city

driving) and HWFET cycle (highway driving). As

shown in Figure 6 and Figure 7, the simulation results

of the vehicle speed, battery SOC, motor torque and

speed, engine speed and the fuel consumption are in

good accordance with the test results, which

demonstrates the validity of the Cruise simulation

model.

Validation and Control Strategy to Reduce Fuel Consumption for RE-EV

Figure 5: Cruise model of the target RE-EV.

3 CONTROL STRATEGY OF

ENGINE OPTIMAL

OPERATION

3.1 Optimal Operation Line (OOL)

Control

As shown in the test results (Figure 2 ~ Figure 4), the

engine operation of the target RE-EV was performed

according to the vehicle speed and battery SOC

without consideration of the engine thermal

efficiency. Since the target RE-EV is the series type,

Figure 6: CS mode validation results for UDDS cycle.

it is possible to operate the engine independent of the

vehicle speed.

In this study, a control algorithm was proposed to

operate the engine on the optimal operating line

(OOL) that provides the best thermal efficiency. As

shown in Figure 8, the OOL was obtained by

connecting the points which provide the minimum

VEHITS 2017 - 3rd International Conference on Vehicle Technology and Intelligent Transport Systems

Figure 7: CS mode validation results for HWFET cycle.

fuel consumption for the demanded engine power

(Ma, 2012).

The demanded engine power was obtained by the

motor input power and weight factor considering the

battery SOC balancing in CS mode. The battery

balancing was performed using the weight factor. The

weight factor was designed as a PI controller using

the difference between the target SOC and present

SOC as follows:

Figure 8: Determination of engine operating point in OOL

control.





SOC

























(1)

where 



SOC



is the weight factor, 



, 



is the P, I

gain of PI control, respectively.

The demanded engine power was obtained as











 ∙ 



(2)

where

_

is the engine demand power, 



the motor input power.

For the demanded engine power, the engine

operating point (torque and speed) is determined from

the OOL. MG1 controls the engine to operate on the

OOL.

The mode change timing and engine on/off timing

were used from the existing control based on the

vehicle speed and battery SOC.

3.2 Results and Discussion

Simulations were carried out to evaluate the

performance of the engine OOL control algorithm. In

simulation, the initial battery SOC and target SOC

were set as 0.18 and 0.16 respectively.

In Figure 9, simulation results are compared for

the OOL control and existing control when the

vehicle drives UDDS cycle. It is seen from Figure 9

that both control follow the driving cycle closely. The

engine speed and torque by the OOL control show

higher value than those of the existing control when

the vehicle speed is high

In Figure 10, simulation results for HWFET cycle

are compared. It is seen that the engine speed

remained around the OOL (250rad/s), which is lower

than that of the existing control, but the engine torque

showed higher value.

Validation and Control Strategy to Reduce Fuel Consumption for RE-EV

Figure 9: Simulation results for UDDS cycle.

The engine speed by the existing control varied

according to the vehicle speed and battery SOC

(Figure 4). The engine speed by the OOL control

shows relatively lower values than the existing

control. The engine speed and torque were

determined from the OOL using the battery SOC,

motor power and engine power.

To compare the fuel economy, the equivalent fuel

consumption was calculated as follows:























∆









(3)

where D is the distance of cycle, 



is the equivalent

gasoline energy of electric energy, is the battery

Figure 10: Simulation results for HWFET cycle.

capacity, ∆



is the fuel consumption, 



is the

fuel density.

In Table 2, the equivalent fuel consumptions are

compared for the OOL control and existing control.

It is seen from Table 2 that the equivalent fuel

consumption by the OOL control is lower than that of

the existing control for both UDDS and HWFET

cycle. It is noted that the improvement rate of the

highway cycle (HWFET) is much higher than that of

the city cycle (UDDS). This because the engine

operating points by the OOL control are almost close

to the operation points of the existing control when

the vehicle speed and demanded power are low in the

city driving. However, when the vehicle speed and

demanded power are high in highway driving, the

engine operation by the existing control is performed

VEHITS 2017 - 3rd International Conference on Vehicle Technology and Intelligent Transport Systems

Table 2: Comparison of control strategy for UDDS and

HWFET cycle.

Final

SOC

Fuel

consumption

(kg)

Equivalent

fuel

consumption

(l/km)

Im-

prove-

ment

Existing

control

0.1599 0.3277 0.0474 -

OOL

control

0.1597 0.3037 0.0449 5.3%

Existing

control

0.1608 0.5258 0.0500

OOL

control

0.1628 0.4303 0.0415 17%

at low torque region when the thermal efficiency is

relatively low meanwhile the engine operation by the

OOL control is carried out at high torque region with

high efficiency.

4 CONCLUSIONS

In this study, the target vehicle is modelled and

validated with test data, and an engine optimal

operation line (OOL) control strategy was proposed

for a range extended electric vehicle (RE-EV) to

reduce the fuel consumption.

The engine control strategy was derived by

analysing the test data from Argonne National

Laboratory. The mode and engine on/off timing are

determined by battery SOC and vehicle speed. The

engine speed is determined by vehicle speed. Using

the engine control strategy, dynamic model of the

target RE-EV which was developed based on Cruise

was validated. It was found that the simulation results

are in good accordance with the test results. Based on

the simulation results, an engine control strategy was

suggested, which operates the engine on the OOL for

the demanded engine power. The demanded was

determined by introducing the weight factor which

balances the battery SOC. From the simulation results,

it was found that the equivalent fuel consumption by

the OOL control is reduced as much as 5.3% for

UDDS and 17% for HWFET compared with that of

the existing control.

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Validation and Control Strategy to Reduce Fuel Consumption for RE-EV