A Control Strategy for Reducing Fuel Consumption

in a Hybrid Electric Vehicle

Babici Leandru Corneliu Cezar

and Alexandru Onea

Institute of Problem Solving, “Gh. Asachi” Technical University,

Str. Prof. dr. doc. Dimitrie Mangeron nr. 27, Iasi, Romania

Department of Automatic Control and Applied Informatics, “Gh. Asachi” Technical University,

Str. Prof. dr. doc. Dimitrie Mangeron nr. 27, Iasi, Romania

Keywords: Control, Command, Hybrid, Power Vehicle.

Abstract: Hybrid electric vehicles are one of the most suitable alternative for conventional automobiles. This paper

describes a control strategy for a hybrid electric vehicle, in order to reduce the fuel consumption, and to

maintain a reasonable state of charge (SOC), at the end of the drive cycle. The main goal is to split the

requested power from the driver between the internal combustion engine, and the electric motor, such way

to decrease the fuel consumption, and to maintain the dynamic performances. The algorithm was tested

using Matlab Simulink and ADVISOR interface. The results include statistical comparisons of the standard

drive cycles using default model and the modified control strategy.

1 INTRODUCTION

Hybrid electric vehicles (HEVs) receive increasing

attention due to their potential for reduced fuel

consumption and low emissions. Increasing fuel cost

and emissions standards across the globe have

popularized this trend in transportation. According

to a recent survey, 36% of motorists worldwide wish

to buy a car with hybrid drive, while 46% of them

showed interest in buying full-electric cars. Energy

efficiency and performances of the automobiles

depend on the control strategies, journey type, and

driver behavior (Chan, 2002). A typical HEV

powertrain has an internal combustion engine (IC)

with an associated fuel tank and an electric motor

with its associated energy storage devices such as

batteries and/or ultracapacitors. Because a hybrid

powertrain is much more complicated than a

conventional powertrain, the coordination and

appropriate control strategy for the energy

components have significant influences on vehicle

dynamic performance, fuel economy, and emissions

(Johnson et al., 2000). In HEV designing

configuration, the commonly constraints are: vehicle

range, acceleration, maximum speed, and road

grades. All these factors are directly related to

driving patterns. The required specifications in HEV

design are usually divided into two categories. The

first depends on consumer’s demand such as

acceleration performance, maximum speed and fuel

economy. This category of specifications is used in

sizing the vehicle components such as, electric

motor (EM), internal combustion engine (ICE) and

transmission system. The second category is based

on ecological issues such as vehicle emissions. The

control strategies should maintain vehicle emissions

within the regulation limits. There are three major

types of hybrid systems that are being used in the

hybrid vehicles market as: series, parallel and series-

parallel hybrid types. The real world drive cycle data

for this study was obtained using the National

Renewable Energy Laboratory’s (NREL) vehicle

level simulation software. ADVISOR, was used to

evaluate and compare the simulated performance of

the hybrid electric vehicle, on different drive cycles

using different strategies (Markel et al., 2002). In

parallel configuration the internal combustion engine

can assist the electric motor during times of high

power demand, according to the control strategy, if

first is sized with less power than the second one.

Energy storage system (ESS) and the electric motor,

are capable of providing all of the vehicle’s power

demands. Recent study has shown that a vehicle can

meet its performance requirements with minimum

power rating if the power train operates mostly in

constant power. The power rating of a motor that

543

Leandru Corneliu Cezar B. and Onea A..

A Control Strategy for Reducing Fuel Consumption in a Hybrid Electric Vehicle.

DOI: 10.5220/0004120205430547

In Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics (ICINCO-2012), pages 543-547

ISBN: 978-989-8565-22-8

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

deviates from the constant power regime can be

much higher than of a motor, operating at constant

power throughout its speed range in a vehicle. In its

normal operation mode, the electric motor, can

provide constant rated torque up to its base or rated

speed. At this speed, the motor reaches its rated

power limit. The operation beyond the base speed,

up to the maximum speed, is limited to this constant-

power region. The range of this constant-power

operation depends primarily on the particular motor

type and its control strategy. An electric machine

should be able to perform a long constant-power

operation in order to be suitable for HEV

2 HYBRID ELECTRIC VEHICLE

SIZING AND CONTROL

STRATEGY IN ADVISOR

An effective HEV design requires optimal sizing of

its key mechanical and electrical components. In the

design process of an HEV, there are a range of

design variable choices, including HEV

configuration, key mechanical, electrical

components sizes, and control parameters. On the

other hand, the HEV design problem is focused at

several simultaneous objectives such as the

minimization of fuel consumption (FC) and exhaust

emissions (e.g., HC, CO, and NOx) while

maintaining driving performance. However, these

aspects are often in conflict with each other. The

minimum FC does not necessarily result in the

minimum emissions. Several approaches and

methods have been reported to optimize HEV

component sizes and control strategy parameters,

with the aim of simultaneously reducing FC and

exhaust emissions (Banvait, 2009). A parallel hybrid

powertrain is used in this paper, where two

mechanical powers are added together in a

mechanical coupler. The control strategy of a

parallel HEV is responsible for distributing the

driver’s required torque between the IC engine and

electric motor while sustaining a charge in the

batteries. The IC engine is the primary power plant,

and the batteries and the electric motor drive

constitute the energy bumper. Both IC engine and

electric motor may deliver power to the vehicle

wheels. In addition, the electric motor may also be

used as a generator to charge the battery by either

regenerative braking or absorbing the excess power

from the engine when its output is greater than the

output required to drive the wheels. For simulations,

it was used ADVISOR 2003 and Matlab 2011. In

order to reduce the fuel consumption, less required

torque from the ICE was calculated in the control

strategy, and more required torque from electric

motor. In ADVISOR the cumulative fuel use (CFU),

expressed in Laplace is calculated like below:

CFU = 1/s*x*3.785*231/r*61.02

(1)

where r is the fuel density (749), and x is the fuel

use (FU), measured in L/s. 1/s means that the

function is integrated.

FU=y*(0.1*pow((m-n)/(m-20), 0.65)+1)

(2)

where y is the hot fuel use (HFU), m is the

engine coolant thermostat set temperature(96 Celsius

degrees), and n is the coolant temperature. HFU is

obtained from a 2-D lookup table with the inputs

arguments: fc_map_spd (speed map), and

fc_map_trq (torque map). The torque available (T)

from the ICE is calculated as below:

T=max(min[(Tr+Ei), maxt], Tcl)-Ei

(3)

where Tr is the required torque, Ei is the engine

inertia, maxt is the maximum torque required, and

Tcl, is the torque when the throttle is closed. In the

torque coupler block in Advisor, the needed power

from the driver is divided between the requested

power from the ICE and the requested power from

the motor. The inputs in the torque coupler bloc are:

torque and speed required, torque and speed

available from the ICE, and torque and speed

available from the electric motor (EM).

Figure 1: Torque Coupler in Advisor.

The outputs are torque and speed available at

torque coupler, torque and speed required from ICE,

and the required torque and speed from EM. Torque

available at torque coupler (Ta) is the sum of the

torque available from the ICE and EM, minus the

losses in this bloc, because of the friction force.

Ta=Ti+Te*tc_mc-L

(4)

ICINCO 2012 - 9th International Conference on Informatics in Control, Automation and Robotics

544

where Ti is the torque available from ICE, Te is

the available torque from EM , tc_mc is the constant

ratio of speed at motor torque input to speed at

engine torque input, and L is the parameter

according to the losses due to the friction force.

Speed available at the torque coupler (Sa) is the

minimum of the speed available of the ICE and the

EM:

Sa=min(Sf, Se/tc_mc)

(5)

where Sf is the speed available from the ice, Se

is the speed available from the EM. First parameters

that we used are: fuel converter with maximum

power of 41 Kw, 25 modules off lead batteries with

maximum power 25 Kw, and nominal voltage of 308

Volts, and a 75 Kw electric motor. Because the

maximum power of the electric motor is almost

double than the ICE, in the control strategy

proposed, the electric machine is used as the primary

source of power, and the mechanical machine is

used to recharge batteries and to sustain the request

of torque and speed as much as possible.

The control strategy that it was used is illustrated

in the figure 2 below.

Figure 2: Control Strategy in Torque Coupler.

When the driver presses the acceleration pedal, a

torque and speed will be requested from the power

sources. At the requested power from the electric

motor it was added the electric power for the

accessory loads. If the required torque and speed is

less than maximum torque and speed available from

the electric motor, and the necessary power from the

batteries is less than actual power, and SOC is

greater than 0.64, or the requested torque is negative,

then only the electric machine is used. In these

conditions, the internal combustion engine is shut

down. As long as the controller is using only the

electric motor (EM), the system is a zero emission

vehicle. If SOC is below the low limit, and the

required power is greater than the power available,

then both ICE and EM are running together to

overcome the need of torque and speed. A logic

scheme of the control strategy is presented below.

Figure 3: Control strategy logic scheme.

In the above scheme, a Rule Based Energy

Management Control strategy is presented, which

was used by the ADVISOR model in the drive cycle

tests. The engine’s ON/OFF condition is dependent

on the SOC of Battery, power requested and vehicle

speed (Banvait, 2009). Trq&Spd is the torque and

speed required, Preq is the power required, Pa, is the

available power, Tchg is the torque necessary to

recharge the batteries, TaICE is the available torque

from ICE, Trq&Spd a EM, is the available torque

and speed from EM. When the required torque is

below 0, meaning that the vehicle is moving and the

driver is no longer pressing the acceleration pedal,

the ESS is charging. 0.64 is the lower limit of SOC,

for keeping a long life of the batteries. When the

current SOC is higher than its low limit LSOC and if

the required speed is less than a certain value, the

engine will turn off. This specific speed is called the

electric launch speed Ve. Furthermore, if the required

torque is less than a cutoff torque Foff × Tmax, the

engine will also turn off. When the battery SOC is

lower than its low limit, an additional torque Tchg is

Figure 4: Charge torque required.

A Control Strategy for Reducing Fuel Consumption in a Hybrid Electric Vehicle

545

required from the engine to charge the batteries like

in the figure 4 below:

Tchg = cs_chg-trq/(50*(1-

SOC)*(cs_hi_soc-cs_lo_soc))

(6)

where cs_chg_trq is 15.2, cs_hi_soc is 0.7, and

cs_lo_soc is 0.65.

Several approaches and methods have been

reported to optimize HEV component sizes and

control strategy parameters, with the aim of

simultaneously reducing FC and exhaust emissions

(Gao and Mi, 2007; Montazeri et al., 2006;

Poursamad and Montazeri, 2008). However, in most

of the recent studies found in the open literature, the

conflicted optimization targets such as FC and

exhaust emissions are aggregated into a multi

objective function (Desai and Williamson, 2009)-

(Hu et al., 2004).

3 TESTS AND RESULTS

The advanced vehicle simulator (ADVISOR), which

is one of the most popular HEV simulators

worldwide, is used as the modeling and simulation

tool in this paper. ADVISOR employs a combined

forward/backward-facing approach for vehicle

performance simulation. In the following

simulations some fixed parameters are used in the

parallel HEV (Desai and Williamson, 2009), (Fan et

al., 2009):

• rolling resistance coefficient: 0.009;

• aerodynamic drag coefficient: 0.335;

• vehicle front area: 2.0 m2;

• wheel radius: 0.282 m;

• cargo mass: 136 kg;

• gear ratio: 2.48, 3.77, 5.01, 5.57, and 13.45;

• efficiency of the gearbox: 95%;

• gearbox: five-speed manual gearbox;

• gear ratio: 2.48, 3.77, 5.01, 5.57, and 13.45;

• efficiency of the gearbox: 95%;

As the ICE, a Geo Metro 1.0 L SI engine with a

maximum power output of 41 kW and a peak

efficiency of 0.34 is used. In addition, as the electric

motor, a Westinghouse ac induction motor with a

maximum power output of 75 kW and a peak

efficiency of 0.92 is used. In this paper, according to

the charge and discharge resistance curve of the

lead-acid battery the SOC target value is set to 0.65.

Driving cycles are defined as test cycles that are

used to standardize the evaluation of vehicle fuel

economy and emissions. Driving cycles are speed–

time sequences that represent the traffic conditions

and driving behavior in a specific area. In this paper,

three cycles of NYCC, WVUINTER, and UDDS

were used to evaluate the FC and exhaust emissions.

These cycles are the currently used cycles in the

U.S. and European communities. First test was made

under the NYCC drive cycle conditions.

Figure 5: Drive Cycle CYC_NYCC.

During this drive cycle, the vehicle stopped 18

times, had the maximum speed 44.58Km/h, and an

average speed of 11.4km/h. The results were as

follows: fuel consumption is 4.4 L/100Km and a

remaining SOC of 0.6495 at the end of the drive

cycle. Using the standard control strategy under the

same conditions, it was obtained a fuel consumption

of 11.7L/100Km and a remaining SOC of 0.66. The

difference in fuel consumption between the two

control strategies is substantial.

4 CONCLUSIONS

Hybrid electric vehicles are the most viable solution

for the world fuel economy, and emissions. A lot of

control strategies are develop every day, to improve

in a continuous way the dynamic performances of

the vehicles, and to reduce, or to maintain as much

as possible the lowest consumption (Morteza and

Poursamad, 2006),(Fan et al., 2009). In this paper

the electric motor was used most in the control

strategy, with the restriction of maintaining a

reasonable state of charge in the batteries. As the

tests showed, the biggest difference in matter of fuel

consumption was obtained in the NYCC drive cycle.

There the vehicle had a lot of stops and goes, and it

matches perfectly with the real urban traffic in the

hardest conditions. The fuel consumption was more

than satisfactory, and the remaining SOC also. In the

future it is very likely that the full electric vehicles

to run on the streets bun until then, the hybrids are in

the trend, and the control strategies are and will be

improved (Ehsani et al., 2010), (Chan et al., 2010).

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