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FUZZY CONTROLLED EVAPORATIVE BATTERY THERMAL MANAGEMENT SYSTEM FOR EV/HEV: Part 1

Ataur Rahman*, Nur Farhana, Ahmed Helmi, MNA Hawlader Department of Mechanical Engineering Faculty of Engineering International Islamic University Malaysia 50728 KL, Malaysia *Corresponding Author: Email: arat@iium.edu.my; ataur7237@gmail.com

ABSTRACT

Electrical vehicle needs to draw power from battery pack for acceleration. It needs to draw high current for speeding up to 130 km/h on 0% gradient and moderate speed 40 km/h on 5-10% gradient. Battery generates powerful electrical currents to meet the power demands of the EV, causing significant warming of the Li-ion cells due to internal resistance. The battery operating temperature of 40ºC and above, the battery life span is reduced. The rationale of this study is to develop a fuzzy controlled evaporative battery cooling thermal management system (EC-BThMS) to control the battery temperature in the range of 20ºC - 40ºC. The proposed thermal management system has been developed by developing evaporative system with estimating the total cooling loads and thermal behavior of the battery cells. The main objective of the proposed system is to control the battery temperature in the range of 20ºC – 40ºC both in charging/discharging process and make the system more energy efficient. A fuzzy controlling system has been introduced with the EC-BThMS to control the electro-compressor and the expansion valve based on the response of battery temperature sensors.The battery temperature profile has been studied in IIUM campus operating an EV with traveling speed of 60km/h on 0% gradient and 40 km/h on 5% gradient. While, experiment has been conducted on Sepang F1, Malaysia International Formula 1 (F1) circuit traveling speed of 130km/h on 420m of 0% gradient staright track. The maximum battery temperature was recorded 360C for the car travelling on 5% gradient and 390C on Sepang F1 circuit. Comparison has been made on the performance of EC-BThMS with air cooling battery thermal management system (AC-BThMS) by using same car. Result shows that EC-BThMS can save 17.69% more energy than AC-BThM 1 and 23% than with AC-BThMS 2. Keywords: EV/HEV; Battery Thermal Management System; Fuzzy Control; Cell of battery.

1. INTRODUCTION One of the biggest obstacles faced by the EVs development is to maintain battery temperature at an

appropriate level. This is important not only to maintain the performance and capacity of the battery, but also to ensure the battery safety and life span. Electrical car needs to be equipped with high power battery pack for traction and high speed. The momentary peak load periods generate powerful electrical currents, causing significant warming of the Li-ion cells due to internal resistance. Lithium-ion battery should be operated in the desired range of 25-350C which could lead to balance utilization of the active material and potentially to improve the performance and span the battery life. Charging and discharging processes of a battery will produce heat. An enormous amount of heat is generated especially during the high rate discharges. If this heat cannot be dissipated immediately, the battery temperature rises dramatically within a short period of time (Li and Wang, 2010). Heat behavior of Li-ion battery during rapid charge and discharge cycles had been studied by Onda et al. (2006). They reported that much heat generation happen during rapid charge and discharge cycles which make the battery

 

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------------------------------------------------- a)Author to whom correspondence should be addressed. Electronic mail: arat@iium.edu.my temperature rise significantly. Heat control and management is one of the most important issues in the lithium-ion batteries at high temperature or high charge/discharge rate will lower the battery life or even cause fire, as reported by [Xiangzhe and Hongbin (2005), Benjer et al. (2009), Wang et al. (2009), Chen and Evans (1994)].

Dominko et al. (2005), Al-Hallaj et al. (2000), Wong et al. (2010), Duan and Naterer (2010), and Heckenberger (2009) were studied on electrochemistry and structures of LiFePO4 material. They have reported that the Li-ion battery capacity drops seriously under low temperature. It drops about 17-22% when the battery cells temperature is -20ºC. The anode material starts decomposing and reacting with the electrolyte under a higher temperature of about 172ºC. According to Nikulata and Veje (2012), Albright (2012) and Smith-Root (n.d.), for LiFePO4 battery application, maximum recommended battery temperature is 40ºC. Battery temperature above 40ºC will reduce battery life slightly, and significantly reduce if the battery temperature goes beyond 60ºC. Furthermore, the findings of Kuper et al. (2009) reported that Li-ion battery performs better at the preferred temperature range of 20ºC - 40ºC, which providing a close to maximum power capability and providing acceptable thermal ageing rates. Pesaran (2001) asserts that the battery life cycle would get shorter at temperature more than 60ºC. Battery pack imbalances can reduce performance and can also damage the battery and/or reduce life (Zolot et al. 2002). In order to optimize performance of a battery pack, the goal of a thermal management system is to deliver an optimum average temperature with even temperature distribution and uniformity with small temperature variations within a battery module (Pesaran et al. 1997, and Pesaran, 2001). Hence, significant importance has been placed on thermal management of Li-ion battery cells to keep the batteries operating at a desirable temperature range, to prevent the batteries from exceeding a high temperature and to achieve the desired life span (Heckenberger, 2009). According to Zolot et al. (2002), thermal management of a battery pack is typically accomplished with the combination of two approaches; (i) a cooling system is designed to absorb heat from the battery pack and (ii) the battery controller adjusts the vehicle’s use of the battery pack based on the conditions in the batteries. Sabbah et al.(2008) and Kizilel et al. (2009) were studied on air-cooling battery thermal management system. They reported that air cooling system is not a proper thermal management system to keep the temperature of the cell in the desirable operating range without expending significant fan power. Pesaran et al. (1999) has conducted study on battery thermal management system of EV and they recommended liquid cooling system.However, the main weakness of the liquid cooling system is the possibility of liquid leaking which causes electric short-circuit and in severe case eventually could cause fire the car. A study of integrating between the air conditioning system’s refrigerant with the battery cooling has been conducted by Heckenberger (2009) for Mercedez-Benz S400 Blue HYBRID and Rahman et al.,(2014)for Proton Saga 1.3FXL Saga Electric car. They reported thatevaporative cooling battery thermal management system is more energy intensive than the system operated by air-cooling and liquid cooling. Furthermore, more energy is needed by cooling process via a refrigerant circuit, as compared to an operation by a coolant circuit. Parker Hannifin Corporation (2012) has investigated and concluded that evaporative cooling system is safer than liquid (water-ethylene glycol) cooling system due to the coolant’s inherent non-conductive property, while it is also delivers two times greater thermal performance with only 25% of the flow rate required by liquid cooling.

II. MATHEMATICAL MODELLING Electric current flows in and out from the battery during charging and discharging respectively causes heat

which due to the entropy change from electrochemical reaction (Pesaran, et al.(1999), Krüger et al.(2009) and Bergveld (2001), Joule’s effect (Ohmic heating) caused by internal electric resistance inside the cell and polarization resistance as energy is required for the diffusion and movement of atoms in the battery reaction

 

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process. Total heat rate generated inside a battery cell due to electrochemical reactions, polarization resistance and Joule’s effects can be expressed as follows;

outgeninenergy QQQQ (1)

where,energyQ is the rate of energy increment stored within the battery cell, inQ is the rate of energy transfer into

the cell, genQ is the rate of energy generation and outQ is the rate of energy transfer out of the cell. It is assumed

that there is no heat transfer energy transfer from cell to cell, inQ = 0, therefore the overall energy balance on a

battery module can be expressed as;

outgenenergy QQQ

Considering outQ equals to convQ , Equation (1) can be expressed by;

TTAhRII

Fn

STIQ ibattsmi dbidbi

i

iibattdbenergy )(

2)( (2)

The change in energy storage due to temperature change over time t when the vehicle is operated, or current is drawn from the battery can be expressed as;

TVcdt

dQ penergy (3)

where, andpc are the mass density and the specific heat capacity, respectively of the battery module material

and V is the volume of the module.

The above overall energy equation used to calculate the average module temperature battT over time can then

be written as;

TTAhRII

Fn

STITVc

dt

dibattsmi dbidbi

i

iibattdbp )(

2)(

(4)

Therefore, by replacing the battery module mass m into the above equation, the overall energy equation used to

calculate the average module temperature battT can be expressed as follows:

TTAhRII

Fn

STI

mcTT ibattsmi dbidbi

i

iibatt

dbp

ibattfbatt )(

2)()()(

1 (5)

The discharge current (Ib(d)) from the battery can be estimated by incorporating the traction equation of

Rahman et al.(2011):

mvolm

fDa

pt

db V

Vg

mVA

C

g

mVPdt

dvm

I

)

22

sin210009.001.01005.0

(6)

 

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where m is the mass of the vehicle in kg, g is the gravitational acceleration constant, m/s2, r the adhesion

coefficient of the road, Pt(p) is the tire pneumatic pressure of tyre in kN/m2, the slope angle of the road in deg,

a is the air density in kg/m3, DC is the coefficient of aerodynamic resistance, fA is the frontal area of the

vehicle in m2 and V is the travelling speed of the vehicle in m/s and Vm(vol) is the motor rating voltage in volts. For the simulation: the adhesion coefficient value r of 0.02, highest gradient sin =G equals to 3.67%, air

density a equals to 1.164 kg/m3 and maximum travelling speed Vmax of 120 km/h has been considered. Vm(vol)

is the motor rating voltage in this study has been considered as 100 volts. The maximum power needs for the vehicle to mobile on 3.67% gradient with maximum speed of 120 km/h has been presented in Table 1.

One of the factors determining the cooling capacity inside the cooling ducts is refrigerant mass flow rate. The mass flow rate of refrigerant can be estimated as,

41 hh

Qm evap

ref (7)

where, refm

is the refrigerant mass flow rate, 1h is the enthalpy at point 1 and 4h is the enthalpy at point 4.

41 hh is called the refrigerant effect during evaporation process. It can be represented as

outinrefp TTchhh 41 ,where refpc is the heat capacity of refrigerant, inT is the inlet refrigerant at the

inlet port of the evaporator while outT is the temperature of the refrigerant at the outlet port of evaporator.

Assuming that the heat received by the refrigerant is equal to the heat generation from the battery, genevap QQ .

Hence, the refrigerant mass flow rate can be expressed as,

41

2

hh

RIIFn

ST

mi ii

i

ibatt

ref

(9)

Power consumption by the compressor can be defined as,

12)(

1hhmP ref

ccmcin

(10)

where, ɳcm and ɳc are the efficiency of the compressor and compressor respectively, Pin(c) is the power by compressor motor in kW.

During vaporization process, the refrigerant exists as part liquid and part vapor. That is a mixture of saturated liquid and saturated vapor. The proportion of the liquid and vapor phases is defined by a property called, quality, x. Quality is the ratio of the mass of vapor to the total mass of mixture which can be defined as,

tot

g

m

mx (11)

where, fgtot mmm with mg is mass of saturated vapor and mf is mass of saturated liquid. With x

equals to 1 indicates a 100 percent vapor in the mixture. With known mass and volume, the specific heat v, entropy s and enthalpy h can be calculated based on the refrigeration R143a Table with the reference

 

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properties of saturated vapor and saturated liquid. A general equation can be modeled to estimate the the specific heat v, entropy s and enthalpy,

fgfavg xyyy (12)

The eq. (12) is a general equation with subscript avg is referred to value of v, s and h properties

of the mixture individually. While subscript f referred to saturated liquid properties and fg is referred to difference between value of saturated vapor and saturated vapor fg yy .

Mass ow rate of refrigerant, refm in the expansion valve is comprised of mass flow rate of the

mixture with quality, x. Thus, each mass flow rate of each composition saturated liquid, mf and saturated vapor mg can be calculated as,

fgref mxmxm 1 (13)

with g

ecdevevg v

PPUkm

* and

f

ecdevevf v

PPUkm

*

The equation (13) can be rewritten as,

fg

ecdevev

fgf

ecdevevref

vx

v

xPPUk

vvx

vPPUkm

11

111

*

*

                                                                                (14)

where *evk and Uev are constant and opening restriction of expansion valve. ecd PP is pressure

difference of inlet (Pcd) and outlet (Pe) of expansion valve. Thus, Pe is controlled to be above 414:89kPA. Then, value of Pe is the reference for determining the specific heat of vapor, vg and specific heat of liquid vf according to Table 2 . The opening restriction of the expansion valve has been associated with electromagnetic force. The electromagnetic force has been developed in the expansion valve electromagnetic coil with the output voltage of the battery pack’s thermocouple.

Controlling properties of refrigerant such as pressure, Pe is fundamental in attaining perfect cooling system for the battery modules. As mentioned earlier that pressure is to be controlled at the outlet of expansion valve by using magnetic effect on the expansion valve based on the output voltage of the battery pack’s thermocouple.Using Bernoulli's principle of Shan K Wang (2000), mass flow rate of refrigerant can be controlled with the proper restriction of expansion valve.

22221

211 2

1

2

1ghvPghvP (14)

Given P1 as Pcd and P2 as Pe, with similar tube opening, the pressure will be similar. With potential

energy neglected, 22

2

1cdeecd vvPP where difference speed, v is acquired when the cross

sectional area, A is changing between two points..

 

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1212

12 v

A

Avorv

A

Av

e

cd (14.1)

The A1 is referred to cross sectional at inlet of expansion valve as Acd and A2 is referred to cross sectional at outlet of expansion valve as Ae. Thus, by manipulating the Ae at inlet of evaporator, the pressure and mass ow rate of refrigerant can be controlled. If Ae is smaller the higher pressure will be droped in the expansion valve. Therefore, the percentage of refrigerant quality (i.e x%) will be higher which due to the higher mass of refrigerant vapor which will cause the higher cooling rate of battery cells inside the module. While, if Ae higher suggested low pressure drop and x% will be low. Therefore, cooling rate of the battery cells will be low. The contribution of vaporize vapor on the cooling of evaporator of battery pack can be represented by equating the equation (7) and (14) as follows:

fgf

ecdevev

evap

vvx

vPPUk

hh

Q 111*

41

vvx

vPPhhUkQ

gf

ecdevevevap

11141

* (15)

gf

ecdevevevapv

xx

vPPhhUkQ 1

141

*

Eq.(15) indicates that the vaporize refrigerant has contribute largely to dissipitate the heat from the evaporator as the specific heat of vapor (vg) is much more less than the specific heat of saturated liquid (vf).

III. STUDY THE PARAMETERS INFLUENCING THE BATTERY PERFORMANCE

A. Battery Discharging Current

Battery discharge current in this study has been conducted by teoretically and experimentally both in

laboratory and field. Battery of electrochemistry LiFePO4 maximum voltage of 100 V and capacity of 86 Ah has been designed for a 1300kg Proton Saga EV. The battery is consists of 13 modules each of nominal voltage 7.5V and capacity of 86 Ah. An AC induction motor of rating voltage100V with a high performance controller has been used to control the battery discharge currents. Figure 1 shows the discharge current which motor needs to draw from battery to meet the vehicle traction. Result shows that the amount of current discharge with increasing the vehicle. For the vehicle maximum travelling speed of 130 km/h, the motor needs to draw 250A on 0% gradient while the motor needs to draw 190A to maintain the travelling speed of the vehicle 50 km/h.

The laboratory experiment has been conducted and the result has been shown in Figure 2. Result shows that the controller has drawn the maximum current of 550A for developing the motor torque of 120 N.m at 3000 rpm to meet the acceleration load demands of the vehicle. According to the characteristics of the battery, battery can sustain only for few minutes with discharging current of 550A (LG battery assembler) due to the excessive heat energy development in to the cells of the module which could degrade the battery performance and reduce battery life and even causes fire. Therefore, the battery cooling system needs to introduce for the EVs/HEVs to

 

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maintain the battery temperature in the range of 25 to 400C by dissipating the excessive heat. The field experimental result on battery discharge current has been discussed in section 3.2 with Figure 9 and 10(a). B. Cooling Systems

The battery temperature increases significantly with increasing the discharge current and the operation time without cooling system as shown in Figure 3. For example, when the vehicle is driven at speed of 100 km/h, motor needs to draw 160 A to meet the power demand causes the temperature of the battery pack rises about 700C. While, if the vehicle drivens speed of 60 km/h, motor needs to draw 100 A cause the battery temperature rises 45ºC.

The battery temperature has been studied by three different cooling systems to find the optimum cooling system to control the battery temperature in the range of 20ºC – 40ºC. Figure 4 shows the simulation study on three different battery cooling system such as air cooling, liquid cooling and evaporative cooling for a vehicle of 130 km/h and discharge current of 200A with considering the heat transfer coefficient of 40 W/m2K for air cooling system, 200 W/m2K for liquid cooling and 500 W/m2K for evaporative cooling based on (Soldo et al.,2006) .

The air cooling system only could be able to control the battery desired temperature if the current drawn from the battery is less than 150A due to its low heat transfer coefficient. Therefore, air cooling battery thermal management system may cause the battery damage or fire for vehicle higher speed and climbing on higher slope with higher payload. Furthermore, the power consumption will be high as the number of fans needs to run all the way of vehicle operation to cool down the battery. For example, 20 fans each of power consumption 20W has been considered to dissipate heat about 500 watt for the 8.6kWh battery. Total power 400W needs to deliver continuously to operate fans at 7000 rpm during discharging current. The liquid cooling system is very much potential to control the battery desired temperature even for battery current drawn 400A due to its higher heat transfer coefficient and high rate of heat removal. The liquid cooling system is able to maintain the battery temperature below 40ºC if the 1.0 kW electric pump keeps run throughout the battery discharging current by maintaining the battery temperature below 400C.Total approximate 1 – 2 kW needs to operate the pump to keep the 8.6 kWh battery temperature below 40ºC.

The evaporative cooling system has a very high heat transfer coefficient which has higher amount of heat dissipation than the heat generation into the battery for any discharge current rate. The evaporative cooling system is able to keep the battery temperature in the range of 20ºC - 40ºC if 2.6 kW electro compressor keep run to compress the low pressure refrigerant R134a with mass flow rate of 16.5 g/s to the evaporator of the 8.6 kWh battery.

Based on the power consumption of the three cooling systems it is concluded that the liquid cooling system is much potential than the evaporative and air cooling system. However, the liquid cooling system is very much risk for the battery cooling system which could make short circuit if there is any leaking. The evaporative battery cooling system make more potential by controlling with fuzzy logic controller which makes the system effective with the reponse of the thermistor that has been equipped with battery module. Furthermore, evaporative colling system is very much safe as the temperature of this system has made in the rnage of 25-400C which has avoided any sweating affect inside the battery

 

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module. Power consumption of fuzzy control evaporative cooling system has shown in Figure 5. Porwer consumption comparison has been simulated and shows in Figure 6. C. Cooling System Development         In this study, we have developed an evaporative cooling battery thermal management system (EC-BThMS) for the battery pack of Proton Saga EV. This cooling system has been developed based on the principle of ‘Refrigerant Cycle’. The EC-BThMS has been built with independent cooling ducts, compressor, condenser, expansion valve and set of theromouples. Total of 14 cooling ducts are used as the evaporator for the EC-BThMS. The assumptionen has been made for the development of the cooling ducts: (i) flow is one-dimensional steady state, homogeneous and two-phase (saturated liquid and vapor), (ii) the duct cross-sectional area is constant, and similar to all ducts; and (iii) the pressure of each inlet and outlet are the same. The principle of the EC-BThMS is that heat generated inside the battery modules will be absorbed by the refrigerant and this heat will then be dissipated to surrounding air. The concept of the system is shown in Figure 7. Copper was chosen for the material for its good thermal conductivity of 386 W/m0K. Each of the

cooling duct were placed very closely (for thermal contact) to the battery modules, and sandwiched side by side in between the modules. Furthermore, each of the evaporative duct has inlet port which allows to flow the low pressure refrigerant (80% of saturated liquid and 20% of vaporized)to the ducts and leave as low pressure refrigerant (about vaporized) through outlet pipes from the ducts. The developed EC-BThMS is shown in Figure 8. The R-134a refrigerant has been used as the medium

of cooling. The EC-BThMS is an energy intensive system as the system has need 1.6 kW power to operate the electro compressor continuously for mainting the battery temperature in the range of 25-400C. An intelligent fuzzy system has been incorporated in EC-BThMS with a sets of thermocouple to make the EC-BThMS more potential and energy efficient. A conductive materials has been used between the evaporative duct and the battery surface to avoid the sweating effect of the refrigerant although the refrigerant is not develop any sweating effect in theprature 250C. Electro compressor activates automatically with the sets of thermocouple. Each of the thermocouples has been placed in each of the battery module. Electro compressor is only activates if the temperature of the battery modules goes to 350C and disactivates once the temperature goes down to 300C. D. Vehicle Speed and Battery Discharging Current The study on battery discharging currrents has been conducted based on vehicle speeds and road gradiaents of International Islamic University Malaysia’s campus. Total distance of road surrounding the IIUM campus is 2.3 kM and the maximum road gradient is 3%. The study was conducted without loading and loading refrigerant in the EC-BThMS. The vehicle was driven from stand still up to speed 60 km/h. The maximum discharge current was recorded 100 A for the traveling speed of the vehicle over 3% gradient at speed of 45km/h.

Figure 9 shows that the vehicle speed and corresponding current at different driving time. The maximum discharge current 130A has been recorded by using a digital clip-on multimeter at vehicle speed in the rage of 0-35 km/h in 10 sec. The fluctuation of the discharge current has been found due to the road profile where the vehicle has needed lower traction force.

Figure 10 shows the battery temperature and discharge current at different operating time. Figure 10(a)

 

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shows that the discharge current is 130A and the corresponding battery temperature was recorded as 500C which is less than the succeeding discharge current and operational time. This is maily due to the internal cooling of the cells at the beginning of the vehicle operation. The maximum battery temperature was recorded as 630C. The battery temperature has been raised to 600C and the increasing trends was recorded although the vehicle has many cases needs lower traction. Figure 10(b) shows the battery temperature was recorded in the range of 27 – 380C even for the higher discharge current of battery which is mainly due to the heat absorbed by refrigerant. It is interesting that the EC-BThMS was not activated in overall vehicle operational time 14 minutes.

Figure 11 shows the battery vs battery state –of-charge (SOC). The temperature difference of the battery was recorded for 85% SOC shown in Figure 11(a) and for 40% SOC shown in Figure. This is could be due to the higher aquoustic electrochemical reaction to develop the higher flow rate of electron which ofcourse causes the current flow. IV. INTELLIGENT SYSTEM TO CONTROL THE REFRIGERANT FLOW

Intelligent system has been modelled for the mass flow rate of refrigerant to the evaporator by controlling the electro-compressor with a fuzzy logic controller and the electromagnetic throttle valve (expansion ) has modelled for maintaining the quality of refrigerant (x). The compressor speed and the throttle valve are controlled based on the response of thermistor that has been mounted on each of the battery module. The main objective of the controlling both of the compressor and throttling valve is to make the EC-BThMS for energy efficient. The EC-BThMS Fuzzy Intelligent Control System (FICS) has been developed based on the concepts of M.Gopal (2010) and Rahman et al. (2012). The FICS activates with the response of a set of thermo couples. Each of set thermocouples is used to monitor the temperature of battery individual module. Fuzzy logic controller (FLC) is used to control the power delivered from the battery pack to the DC motor of the DC motor built-in compressor. Hence, power management of EC-BThMSis established in order to minimize power consumption. The battery power adjusting mechanism controls the variable power input (P) to the motor of compressor to develop the torque (Tp) with FLC. It is noted that the sensing inputs of the battery module temperature which depends on the vehicle speed and battery state of discharge (SoD). The shematic diagram of fuzzy logic controller and throttle valve has been shown in Figure 12.. A. Implementation of Fuzzy Logic Controller

In the control system of the EC-BThMS, temperature of the battery (Tbat) is selected as the controlled

variable, and mass flow rate of refrigerant (refm ) to the battery evaporator as the regulated variable through the

operation of the compressor with 24V DC motor.Based on the difference between measured value (Tbat(m)) and reference value (Tbat(r)), the regulation variable, i.e., mass flow rate of

refm has been regulated. Hence, the

resultant deviation, i.e. temperature error (TE) e and the rate of temperature error (RTE) e are continuously measured in operation. From the dynamic of the system, if the measured temperature is less than the desired temperature, it is necessary to increase the flow rate of refrigerant by regulating current flow to the motor. This knowledge of this system behavior allows formulating a set of general rules that are described. The units of the used factors are: TE (C), TPE (C/s), and MFR (kg/s). For the two inputs and one output, a fuzzy associated memory or decision (also called decision rule) is formed as regulation rules. A total of 25 rules were formed. Parts of the developed fuzzy rules are shown in Table 1. For fuzzification, the linguistic variables large negative error

 

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(LNTE), small negative error (SNTE), zero error (ZTE), small positive error (SPE), and large positive error (LPTE) are used for the temperature error (TE) ; the large negative rate of error (LNRTE), small negative rate of error (SNRTE), zero rate of error (ZRTE), small rate of error (SPRTE), and large positive rate of error (LPRTE) are used for the rate of temperature error (RPE). Similarly, the linguistic variables very little mass flow rate of refrigerant (VL

refm ), little mass flow of refrigerant (Lirefm ), medium mass flow rate-of-refrigerant (M

refm ),

large mass flow rate-of-refrigerant (Lrefm ) are used for the flow rate-of-refrigerant (

refm ) as output parameter.

Once the inputs are fuzzified, the FIS refers to a set of user defined if-then rules to decide on a fuzzy output. Schematic representation of fuzzy logic expert system for flow rate modeling is shown in Figure 2. V. PERFORMANCE COMPRISM OF EC-BThMS OVER AC-BThMS The energy efficient of the developed EC-BThMS has been compared with two other EVs using air cooling battery thermal management system (AC-BThMS 1 and AC-BThMS 2). AC-BThMS 1 was an air cooling battery thermal management system equipped with 8 fans at the battery pack, while AC-BThMS was equipped with 12 fans. For both of the systems, cold air is drawn from surrounding air and blown to the battery modules by fans. For these AC-BThMS, the all fans for air blowing were kept running during operation of the vehicles. The result of the cooling systems has been shown in Tables 3. Table 2 indicates that the EV with EC-BThMS can save 17.69% more energy than the EV with AC-BThM 1 and 23% than the EV with AC-BThMS 2. This is because the potentiality of the EC-BThMS which is able to maintain the temperature of the battery pack in the range of 20ºC – 40ºC. VI. CONCLUSION

The Fuzzy and partially electromagnetic controlled EC-BThMS is highly potential to control the battery temperature in the range of 25-400C with minimum energy consumption ad high safety. VII. FURTHER STUDY

The fuzzy logic and partial electromagnetic controlled EC-BThMS has been developed already. The experimental work on the Fuzzy Logic and electromagnetic controlled expansion valve has needed extensive experimental study which is in under process. The next paper will be submitted to Journal of Renewable and Sustainable Energy as part 2 if this paper gets eligibility to publish in this journal. ACKNOWLEDGEMENT The author is grateful to the PROTON SDN BHD and Research Management Centre (RMC), International Islamic University Malaysia for funding this project. The author would also like to express his thanks to the RMC for patent filling this project and the Laboratory assistant for her help and support in this study. REFERENCES

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Vehicle Battery Modules.  International  Journal of Heat  and Mass  Transfer,  53(23−24.), pp.  5176−5182 

(2010) 

Heckenberger, T.  Li‐ion Battery Cooling: More Than Just Another Cooling Task. Technical Press Day 2009, pp. 

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13  

LIST OF FIGURES 

 (a) G=0%

 (b) G=5%

Figure 1: Battery discharge current for vehicle different traction (a) gradient (G) =0%, (b) G = 5%

Figure 2: Battery discharging current.

 

 

Figure 4: Ba

Figure 3: B

attery temper

Battery temp

rature contro

14 

perature profi

olling capacit

ile for discha

ty of air, liqu

arge current.

uid and evapoorative coolin

ng

 

15  

Figure 5: Fuzzy controlled evaporative battery cooling system performance

Figure 6: Power consumption of the cooling systems.

 

 

16  

Figure 7: Concept of EC-BThMS

Figure 8: Developed evaporative battery cooling system

(a)

(b)

Figure 9 :Discharge current for different speed (a) after 12 kM; (b) after 24 kM

 

high pressure side; liquid

cool air

low pressure side; liquid

low pressure side; vapor

high pressure side; vapor

Batterymodules

Evaporator ducts

Expansion valve

Condenser

Light‐weight electric compressorrefrigerant flow

UP

RHS

FR

 

17  

(a) without loading refrigerant

(b) with loading refrigerant

Figure 10: Battery temperature and corresponding discharge current for (a) without loading refrigerant (b) with

loading refrigerant

(a) SOC ( 85-40%)

(b) SOC ( 40-25%)

Figure 11: Battery temperature for disfferent SOC

 

 

 

0

20

40

60

80

100

120

140

0 2 4 6 8 10 12 14

Battery temperature, 0C /

Discharge curren

t, A

Operational time, min

Discharge

Battery

0

20

40

60

80

100

120

140

0 2 4 6 8 10 12 14

Battery tem

perature, 0C /

Discharge current, A

Operating time, min

Discharge

Battery

 

18  

 

Figure 12: Shematic diagram of fuzzy logic controller and throttle valve.  

 

Figure 13: Development of fuzzy rule for controlling the battery temperature 

 

19  

LIST OF TABLES 

 

Table 1: Vehicle traction parameters and corresponding heat generation

Vehicle Operation Mode Traction

(Nm) Power Required

(kW)

Discharge Current

(A)

Heat Generation (Watt)

For speed: 60km/h

0% grade 85 5 49 100

3.67% grade 190 12 110 210 For Speed: 120 km/h

0% grade 160 21 210 900

3.67% grade 275 33 345 2450

Table 2: Partial of R-143a saturated liquid refrigerant

Source: Yunus et al. (2001) 

Table 3: performance of the EC-BThMS

Type of BCThMS

Performance Measurement Mode

Quarter mile acceleration

[sec]

Shortest time in two 11.5

kM [min]

Max. velocity (within shortest

time in 11.5 kM) [km/h]

Travelling distance for

Vcut

[km]

Comparison of energy saving with

EC-BThMS [%]

EV with EC-BThMS 22 17:15 119 68.62 -

EV with AC-BThMS 1

51 26:51 119 59.94 17.69

EV with AC-BThMS 2

25 26:19 91 57.75 23

Note: “Vcut” represents battery cut off voltage  

 

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