Abstract— This study presents a fast and efficient method for modeling of Hybrid Electric Vehicles (HEV). The method is about to modeling the components of the HEV as energy sources, energy consumers and energy storages, and investigating the energy flow between these components. The management logic of the energy is also defined according to various driving modes. The overall model is built on Matlab/Simulink environment and run on some driving cycles, and results are presented.

I. INTRODUCTION hybrid electrical vehicle (HEV) may consist of an internal combustion engine (ICE), electric motor (EM),

electric generator (EG), power electronic circuits, advanced electronic control units (ECU), a complex mechanical transmission and a battery bank. In an all-electric vehicle (EV) there is no ICE, but all other components exist including batteries with excessive power. EVs and HEVs are studied by numerous authors in the past, one comprehensive study is that of Chan [1]. First full-scale hybrid vehicle work in Turkey is Doblo/Tofas example realized at Marmara Research Center [2]. There have been university theses and an industry project constitutes the basics of this paper [3-7]. One of the main contribution is that of Gokce [4].

While modeling a hybrid electric vehicle, all of the components need to be covered, which are nonlinear in nature. In this study, energy conservation and energy balance method is adopted. With this method, it is possible to model the performance of a hybrid electric vehicle by defining its components as energy producing, energy consuming and energy storing elements. As the subject of this work is a series-parallel hybrid electric vehicle, the energy producing component will be the ICE. The battery bank stores electrical energy. Mechanical energy will be stored in vehicle’s mass. Additionally there are friction losses on the vehicle, like air friction and wheel friction. Some of the losses occur in hydraulic braking, which turns

R.N.Tuncay and C.Gokce are with MEKATRO R&D Company,

TUBITAK Marmara Research Center, Technology Free Zone, 41470 Gebze, Turkey. Phone : 90 262 6431099, Fax : 0262 6430962, ntuncay@mekatro.com, cgokce@mekatro.com

O. Ustun and M.Yilmaz are with the Department of Electrical

Engineering, Istanbul technical University, Maslak, Istanbul, Turkey. Phone 90 0212 2856742 and 90 212 2856775, Fax : 90 212 2856700 ustun@elk.itu.edu.tr, mryilmaz@elk.itu.edu.tr

vehicle’s kinetic energy into heat. Energy storing devices are considered together with

energy producing, transmitting and consuming components. Every component’s efficiency is taken into account while analyzing the energy flows suitable to predefined operation modes. In some modes, ICE produces mechanical energy and this energy is used to rotate the wheels, in other modes battery powered electric motor supports the ICE. In other case, battery is flat and some of the energy produced by ICE is converted to electrical energy by the generator to supply the battery and the electric motor. Several driving scenarios are defined taking every possible operation modes into account. These modes cover acceleration, cruising, braking, uphill and downhill operations.

In this respect, the series parallel hybrid electric vehicle model is formed consisting of six parts, namely; ICE, Generator Machine, Driving Electrical Machine, Batteries, Transmission and Load Models. Also, control logic is defined to proper work of these components. Once the model was built, the performance is simulated in some driving cycles and results are discussed.

II. MODELING THE COMPONENTS

Fig. 1 – Series Parallel Configuration of a HEV

The schematic view of the series parallel hybrid electrical vehicle can be seen in Figure 1. The components are modeled and connected as seen in the figure.

Energy Flow Modeling Method for Simulation of a Series-Parallel Hybrid Electrical Vehicle

R.N.Tuncay, O.Ustun, C.Gokce, M.Yilmaz

A

A. Internal Combustion Engine The engine is modeled as a torque source that has a

variable output due to the engine speed. Engine speed is defined with the help of a Matlab function. The function defines the maximum and minimum desired engine speeds. The minimum speed values are defined for the situation that the vehicle is decelerating whereas the maximum values are calculated for acceleration. The maximum and minimum speed values of the generator and the ratios of the planetary gear are the parameters for calculating the engine speed. The other part of the planetary gear is the driving shaft, which is proportional to the vehicle’s actual speed so it is taken as a definitive value for engine speed. So, the engine speed is defined as seen in Table 1. A lookup table containing the ICE’s torque – speed characteristics is built. The calculated speed value is used as the input, thus the output is the torque of the engine in that speed. All the proposed working conditions for ICE are defined in the control logic block [4,5].

TABLE I INTERNAL COMBUSTION ENGINE SPEEDS

Vehicle Speed Max. ICE Vehicle Speed Min. ICE in Acc. (km/h) speed (rpm) in Dec. (km/h) speed (rpm)

0-12 1600 0-89 1250 12-31 2000 89-100 1550 31-50 2500 100-110 1810 50-69 3000 110-120 2100 69-87 3500 120-130 2350 87-150 4000 130-140 2610

In this work, the engine performance is considered thus a comprehensive engine model including emissions and fuel consumption is not covered. The acceleration signal is defined by comparing reference speed by the vehicle speed. If reference is bigger than real, output of this block, which is the gas position, is “1”. Simulink model for ICE, including the block for gas pedal position, can be seen in Figure 2.

Fig. 2 – Internal Combustion Engine

B. Driving Electrical Machine

Driving electrical machine is modeled as a power and torque source. Torque – speed and power – speed characteristics are used in lookup tables for modeling. The speed input of these blocks is derived from vehicle’s actual speed.

The driving electrical machine is actually used for two purposes: Electrical driving motor and regenerative braking generator. The calculated speed and torque values are given to output after control block which checks the conditions of control logic and decides whether the vehicle is accelerating or regenerative braking. Torque output is given to transmission whereas the power output is sent to battery pack after it is added to or subtracted from generator machine’s power [4,5]. The Simulink model can be seen in Figure 3.

Fig. 3 – The Driving Electrical Machine

C. Generator Machine

Generator machine is used for starting up the engine, generating power from engine’s excess power and also adjusting the gear ratio. It is modeled as a power and torque source. Torque – speed and power – speed characteristics are used in lookup tables. Speed input of this block set is calculated in the planetary gear block set. Output is connected to driving electrical machine’s power input then the share of the battery is calculated. The starter action of this machine is also modeled [4]. The model can be seen in Figure 4.

Fig. 4 – Electric Generator

D. Planetary Gear

The planetary gear is used for splitting the engine’s output power between driving shaft and generator machine and also used as an electronically controlled continuous variable transmission. The engine shaft is connected to planetary carrier, generator is connected to sun gear and the driving shaft is connected to ring gear [4,7]. This power split device

is a common component in Series-Parallel HEV’s and the one used in Toyota’s Prius can be seen in Figure 5.

Fig. 5 – Planetary Gear as Power Split Device for SP-HEV (by Toyota Corporation)

The ratio between three components of the planetary gear is adjusted as seen in Equation 1. By using this equation, Equation 2 and 3 is derived to calculate the torque coefficients for defining the torque affected on wheels by engine (CT_ICE) and generator (CT_MG1) [4,7].

( )2.63.6

generator drivingengine

n nn

+ ⋅= (1)

( )

( )( )_1 2

3.60.722 2.6

iceT ICE

MG MG

nC

n n⋅

=⋅ + ⋅

(2)

( ) ( )

( )2

_ 11

9.36 6.763.6

ice MGT MG

ice MG

n nC

n n⋅ − ⋅

=⋅ −

(3)

The reduction gear ratio between driving shaft and wheels

is also taken into account as k = 3.905. The overall Simulink model for planetary gear can be seen in Figure 6.

Fig. 6 – Planetary Gear Simulink Model

E. Battery

Battery, in this work, modeled as a voltage source and a resistance, which changes with the power flow and load. This voltage level of the batteries is derived from a lookup table, which gives the voltage level from the state of charge data, which is derived from the battery experiments or taken from the supplier datasheets.

As power of a battery is the multiplication of the voltage level and the current form the battery, energy demanded from the battery to the system, or, energy stored to the battery from the system can be easily calculated. Calculated current inwards or outwards to the battery has the positive and negative sign respectively, and the integration of this value gives the Ampere-Hour capacity used or stored [2-7]. The resulting Simulink model of the battery can be seen in Figure 7.

Fig. 7 – Simulink Model of the Battery Pack

F. Load and Transmission The load of the vehicle is affected from air friction, wheel

friction and the slope of the road. The frictional and gravitational forces that affect the motion of the vehicle are calculated. Simply, the relation between these forces can be added to the energy flow model with integrating the power value calculated in Equation 4 [1-6]. Also, it is possible to approximately calculate the torque value that should be applied to the wheels to keep the speed constant as in Equation 5 [4,5]. For acceleration, F = m.a should be added to Fload [4,7].

load load vehicleP F υ= × (4)

m load mT P ω= ⋅ (5) The transmission of the vehicle is the place where this

energy flow changes into kinetic energy of the vehicle. Calculated torque is multiplied by the reduction gear ratios and the radius of the wheel to find the velocity of the vehicle

[4]. The transmission model can be seen in Figure 8.

Fig. 8 – Transmission Simulink Model

III. DRIVING MODES AND CONTROL LOGIC

Fig. 9 – Overall Series Parallel HEV Simulink Model

The components of the vehicle are connected as seen in Figure 9 to realize the energy flow vehicle model. To be able to run the model as a series parallel HEV, control logic containing the driving modes is needed to be developed. This logic defines imaginary valves to manage the energy flow on the each component. When the logic on a driving mode is “1”, the relevant action on the component is taken.

The driving modes are defined as follows [4]:

Vehicle start up and low speeds: As the internal combustion engine is inefficient in this range, acceleration with electrical machine is suitable.

Normal working: To avoid the battery flat-outs and excessive performance losses in this range, vehicle is driven by both internal combustion engine and electrical machine.

Sudden acceleration: In this mode, full throttle acceleration of the vehicle is considered. With the help of the extra energy from the generator, electrical machine runs in its full performance. So, internal combustion engine and electric motor together produce the maximum available power.

Regenerative braking: During deceleration, vehicle generates energy from its kinetic energy by running the electric machine in generator mode.

Battery recharge at rest: When the state of charge is below certain levels, it is possible to run the internal combustion engine in its efficient ranges and recharge the batteries with the help of the generator.

Control logic is developed to realize good driving performance and good battery energy management in these driving modes. Thus, control logic tries to keep the SOC in a narrow band around 50% to avoid overcharge or undercharge. Thus, SOC is divided into 5 levels and in each level, proper energy management algorithms are utilized [4]. Between 40% and 60%, the SOC is considered to be in adequate level. In this level, vehicle runs in silent mode below 40km/h, electric assist on full throttle is available, engine is run in its optimum point and excess power is used by the generator machine to charge up the batteries. When the SOC increase above 60%, up to 90%, electric consuming modes continue to operate as it was in the adequate level but energy is only saved by regenerative braking. This means that engine excess power is used by the generator only to power the driving electrical machine. When SOC increase above 90% critical overcharge level is reached. After this level, no regenerative braking or any energy generation occurs. The vehicle uses energy as usual. When SOC is below 40% but more than 10%, silent mode is cancelled. As the ICE is not efficient in low speeds, it is run in its efficient points but the excess power is obtained and used in generator to charge up the batteries. This occurs both in low speeds and at rest. Electric assist and regenerative braking occur as usual. When the SOC is below 10%, critical undercharge level is reached. In this level energy management system cancels all the electric assist and concentrate on regaining the proper SOC levels.

IV. SIMULATION OF THE ENERGY FLOW OF SERIES PARALLEL HEV

The series parallel hybrid electrical vehicle model has been built on Matlab R13 using Simulink 5. Initial SOC has been taken 60%. Model has been run on UDDC and US06 driving cycles. The cycles can be seen in Figure 10.

Fig. 10 – a. UDDC Driving Cycle b. US06 Driving Cycle

During the UDDC, which is 12 km, the propulsion system

has conducted 39.53 kWh of mechanical energy to the vehicle. From this energy, 3.77kWh of electrical energy has been recovered from regenerative braking and electric machine has conducted 2.87kWh of mechanical energy to the system. The SOC has reduced from 60% to 47% because vehicle has run in silent mode in low speeds, consuming battery energy. SOC pattern during UDDC can be seen in Figure 11.

US06 cycle has been run twice and 26km is driven. During this cycle, propulsion system has conducted 54.06 kWh of mechanical energy to the system, 4.36kWh of this energy is recovered electrically through regenerative braking and electric machine has conducted 2.43kWh of energy to the system. The SOC has dropped only 2.5% since vehicle generally used electrical energy in full throttle accelerations by using the driving electrical machine as an assist motor. The SOC pattern can be seen in Figure 11.

Developed model has saved 9.50% of the total mechanical energy used for driving the vehicle in UDDC by regenerative braking. In US06, recovered amount is 8.07% of the total mechanical energy given. These values are calculated by taking the amount of regenerative electrical energy that has been stored in the batteries. Actual saved energy could be calculated by subtracting battery losses from this amount, which has negligible effect on overall energy calculations.

Fig. 11 – a. SOC in UDDC Driving Cycle b. SOC in US06 Driving Cycle

V. CONCLUSION The HEV is a popular research and development subject

recently [1-7]. Many configurations of HEV are researched and performances of these vehicles are needed to be simulated on computers. The energy flow concept defined in this paper is a fast and relatively easy method for testing the performance of the vehicle in respect to the components used and also the driving modes developed. Several driving modes for several driving cycles can easily be tested in this model. The concept is also effective as the researcher can be aware of the power or energy and efficiency of each component before the integration of them into the vehicle. Finally it has been showed that approximately 9% energy saving is possible with this type of hybrid vehicles.

REFERENCES [1] C. C. CHAN, “The State of Art of Electric and Hybrid Vehicles”,

Proceedings of IEEE, vol. 90, no. 2, Feb. 2002. [2] ELIT 1 – Seri Hibrit Elektrikli Araç, TUBITAK MRC Energy

Institute, Research Project and Technical Report, 2002. [3] O. TUR, “Hibrit Elektrikli Araç Güç Sisteminin Simülasyonu”, Master

Thesis, Institute of Science and Technology, Istanbul Technical University, Istanbul 2004.

[4] C. GOKCE, “Modeling and Simulation of a Series Parallel Hybrid Electrical Vehicle”, Master Thesis, Institute of Science and Technology, Istanbul Technical University, Istanbul 2005.

[5] Paralel Hibrit Elektrikli Aracın Modellenmesi ve Simülasyonu, Mekatro Resarch and Development Comp., Technical Report, 2004.

[6] ELIT 2 – Paralel Hibrit Elektrikli Araç, TUBITAK MRC Energy Institute, Technical Report, 2004.

[7] C. GOKCE, O. USTUN, M. YILMAZ, R. N. TUNCAY, “ Modeling and Simulation of Series Parallel Hybrid Electrical Vehicle”, ELECO’05, Int. Conference on Electrical and Computer Engineering, Bursa, 2005.