An Intelligent Energy Management and Control System for Electric ...

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charger is designed for an electric scooter inter faced with a control unit. The system has power factor correction mechanism implemented in a digital signal ...
2014 IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT)

An Intelligent Energy Management and Control System for Electric Vehicle l 2 c. Chellaswamy , R. Ramesh

2 i Research Scholar, Professor i Department of ECE, St.Peters's University, St.Peter's Institute of Higher Education and Research, Avadi, Chennai, India 2 Department of ECE,Saveetha Engineering College, Chennai, India I 2 [email protected], [email protected]

Abstract-This paper presents an intelligent energy management

ion, and nickel metal hydride are tested under urban driving

for electric vehicles

condition.

(EVs)

which contains automatic charging

From

the

simulation

result

SAFT

technology

mechanism. Nowadays EVs charging the battery pack by using

batteries has higher test cycle for daily operation of EVs [4].

road side stations, park stations etc. are increase the travel time.

An optimal battery charging for EV is designed based on life . cycle cost. The battery model is developed and studies the

To overcome this problem we have proposed an automatic charging with an intelligent energy management system using

(

) capable for estimating and controlling

Kalman filtering IEMK

the battery packs. Energy and battery management in EV is difficult and important under driving condition.

This paper

mainly focuses to estimate different parameters such as available power, state of charge, and thermal management. The algorithm has been implemented for the battery packs to maintain charging by both the solar and wind energy systems. IEMK provides

difficulties of both the swap and rapid recharging stations. For optimizing the calculation a modified differential algorithm is used and the result shows that swap stations are better than rapid charging stations [6].

system has been developed. The system also minimizes the gas emission [7].

optimal mean for estimating the parameters of battery pack and accurate under running condition. Simulation results show that the proposed method is stable, state of charge with 1 % error, and robust compared with other systems.

I.

connectivity and accessibility for both the passengers and commodities. The vehicle which is used crude oil is producing more pollutants that are pumped into atmosphere. Fuel vehicles the

biggest

contributors

of

Carbon

Monoxide

(CO)

Nitrogen Dioxide (N02)' High levels of N02 may lead to Lung damage or Respiratory Disease. It has also been increased hospital admissions for asthma, respiratory problems, and mortality.

When

inhaled

scheduling method is used for both the EVs and home

Carbon

Simulation

results

shows

that

this

scheme

manage the power consumption, shifting the load according to the demand [8]. Car parking infrastructure is used to charge the EVs and smart charging algorithm has been developed. Fuzzy logic based power flow controller provides the priority based

INTRODUCTION

Road transport gives higher level of spatial coverage, better

are

Microgrids are used to energize the EVs as a result reduction of greenhouse emissions. An optimal centralized consumption.

Keywords-extended Kalman filtering (EKF), state of charge, electric vehicle, energy management

For less fuel consumption and

increase the efficiency of ship an optimal energy management

Monoxide

enters

the

bloodstream and disrupts the supply of oxygen to the body's

charging index [9]. Convex optimization has been used for energy

management

for

hybrid

EVs.

The

computational

complexity is less compared with dynamic programming. The engine ON/OFF control has been implemented through convex



optimization and two case studies have been proposed. In bo h the cases convex optimization performs faster than dynamic

0

programming [10]. The mobility behavior of vehicle �s g . stochastic process, battery discharge cost, the tariff and dnvmg behavior has been studied. The agent based simulation permits to solve complex problems in a customized approach [11].

tissues. Electric vehicles typically have less noise pollution

Velocity estimation is done by using 2-D interpolation and

than an internal combustion engine or the vehicle which is used

road grade information related to velocity has been studied in

crude oil.

[12]. The system has been studied using both trip and tour

The wind turbine and photovoltaic produces fluctuating power because the input energy is not a constant. The fluctuating power can be stored in a 6.6KW NiMH battery pack. A cascade PWM (pulse width modulation) is used to control the SOC balancing of more number of battery packs and avoid the requirement of additional filtering [1]. A battery charger is designed for an electric scooter inter faced with a control

unit.

The

system

has

power

factor

correction

mechanism implemented in a digital signal processor. The charging mode can be selected by the battery management system interfaced with the battery pack. Battery charger model is tested experimentally and the result is discussed in [3]. The cycle life of two different battery technologies such as lithium

ISBN No. 978-1-4799-3914-5/14/$31.00 ©2014 IEEE

based strategies and compared with the conventional method of energy management system. Electric vehicle need freq��nt . charging based on the capacity of storage system. SatIsfiablhty modular theory based navigation system is used to find the route and the location of charging stations [13]. The price based load balancing navigation is also introduced to achieve load balancing. Distribution power system is used to recharge the electric and hybrid vehicles. If we increase the number of vehicles, at the same time the grid will be affected. An intelligent system is designed to avoid undesirable situations such as low voltage, feeder over load etc. Load management and

artificial

intelligent

system

is

introduced

for

load

management and reducing power loss. The remainder of this

180

2014 IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT) paper is organized as follows: introduction about the proposed

IEMK does not require any additional setup for



system is presented in section 2; estimation of different

measuring the status of battery pack.

parameters are given n section 3; simulation results are given

The proposed algorithm easily converges with the



in section 4; finally conclusion is present in section 5. II.

current status if any abnormal error occurs. management and control for electric vehicles.

Electric vehicles become popular and the goal is to reduce greenhouse emission. The major disadvantage is frequent charging is needed for it. IEMK overcomes this problem and maintain SOC. The microcontroller based power electronic system interfaced with different sensors, battery management system,

and

renewable

power

The algorithm can be used in real-time battery



SYSTEM DESCRIPTION

generation

systems

The

batteries

connected

to

are

completely

battery

sealed

management

and

terminals

through

CAN

are bus

communication. The controller always senses the SOC of each pack and set maintain 25

(a)

Charging limit at the input of battery pack:

Optimal voltage Vm and current 1m corresponding to maximum output power of the solarcell is given by vm

C.

=

VOC

_

kT

q

In

(1

-

q VOC kT

)

VOC

'

( )

TIn � T,

kT =

q

Battery Thermal Management

(b)

Battery thermal management (BTM) is important to extend the life of batteries present in the electric vehicle. This paper

Figure 5. Adaptation Process for estimating SOC (a) filter coefficients (b) mean square error.

focuses the cooling strategies of both the air and liquid cooling system. The air cooling system has lower temperature transfer coefficient and difficult to maintain the temperature uniformly between

battery

module.

Let

the

temperature

transfer

coefficient (Tc ), and the air coolant channel diameter (Da), pressure loss in the coolant channel (/lL), temperature change in the coolant from inlet to outlet is (/lT I), and the temperature variation between coolant and battery cell surface (/lT2). Now the maximum battery cell surface temperature /lTmaF /lTH /lT2 can be used to control the temperature of the cell. Different properties of

coolant like density,

heat

transfer,

A

! ..

g�� (f)

.

----

+-----------

i

---- �

---------- i

�-----

;

---------- +

;--------- ;----------1

----------,

thermal

conductivity, and viscosity is given in table III. Figure 6. Status of SOC under driving condition for a 45Ah Li-ion battery

Table ITI. Coolant and its properties used for battery cooling

Fig. 6 shows that the SOC estimation with and without

Coolant

using

Properties Air

Water

Mineral Oil

Density

1.23

1069

924

Thennal Conductivity

0.03

0.39

0.13

Kinetic Viscosity

1.5e's

2.6e'·

5.6e's

Specific Heat

1006

3323

1900

IV. A.

EKF

calculated

under

driving

manually

from

condition. the

The

measured

SOC OCV

can and

be the

measured current. We can fmd the reference SOC from the measured OCV. Compare SOC of both the reference and SOC using EKF has error within 2%. Further from Fig. 6 we can understand that the controller disconnect the battery pack from the load if SOC :S 30%.

SIMULATION RESULT

Parameter Estimation/or EKF A new method is proposed to estimate the charging state of

the battery. This method is combination of Kalman filter with adaptive filter using RLS method. The battery terminal voltage

time (sec) -->

is used as an input of the FIR filter model, the discharge state is the output, and it is simulated using MATLAB 2012. The number of adjustable parameter values is computed through recursive

computation

and

it

was

set

at

50.

The

filter

coefficients and mean square error of adaptive filter using RLS

Figure 7. Discharging and charging auxiliary battery pack under driving condition

Fig. 7

shows

that

the

SOC

estimation

of

both

the

discharging and charging state of Li-ion battery with and

183

2014 IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT) without using EKF under driving condition. The controller

V.

always watches the status of all the packs present in the electric vehicle. If 30 :s SOC :s 40 the controller immediately disconnect and recharge the battery packs. In Fig. 7 around

50th minutes the battery reaching the low threshold level and

after 30 minutes the controller automatically recharging the battery again.

In

order

to

achieve

CONCLUSION

high

performance

of

battery

management system for electric vehicle, a new method is proposed. This method is combination of extended Kalman and adaptive filtering with control algorithm. The EKF is used to estimate the SOC under running condition of electric vehicle. We have observed that EKF with RLS method is more

accurate

than

normal

measurement

method.

The

temperature greatly affects the life and performance of the battery pack present in the vehicle. Generally, water has a high thermal conductivity than oil. The thermal resistance such as jacket wall, air gap can be added between battery cell outer surface and coolant to reduce the temperature coefficient at the cell surface. From the simulation we can conclude that mineral oil cooling system can be preferred for limiting the maximum Figure 8. Power estimation

peak temperature of the battery.

The charging and discharging power of both the measurement is shown in Fig. 8. In both the measurements EKF based estimation produces more accurate than HPPC method and

REFERENCES [I]

Laxman Maharjan, Shigenori Inoue, Hirofumi Akagi, and Jun Asakura, "State-of-Charge (SOC)-Balancing Control of a Battery Energy Storage System Based on a Cascade PWM Converter," IEEE Transactions On Power Electronics,Vol. 24,No. 6,June 2009.

[2]

U. S department of energy, Idaho National Engineering and Environmental Laboratory, PNOV Battery test manual, Revision 3, DOE/TD-I0597,2001.

[3]

Gianmario Pellegrino, Eric Armando, and Paolo Guglielmi, "An Integral Battery Charger With Power Factor Correction for Electric Scooter, "IEEE Transactions on Power Electronics, Vol. 25, No. 3, March 2010.

[4]

Lorc Gaillac, " Accelerated Testing of Advanced Battery Technologies in PHEV Applications," The World Electric Vehicle Journal, Vol 2, Issue 2,2008.

[5]

J. Chiasson and B.Vairamohan, "Estimating the State of Charge of a Battery," IEEE Transactions on Control Systems Technology, pp. 465470,2004.

[6]

Yu Zheng, Zhao YangDong, YanXu, Ke Meng, JunHua Zhao, and Jing Qiu, "Electric Vehicle Battery Charging/Swap Stations in Distribution Systems: Comparison Study and Optimal Planning," IEEE Transactions On Power Systems,Vol. 29, No. I,January 2014.

[7]

F. D. Kanello, "Optimal Power Management With GHG Emissions Limitation in All-Electric Ship Power Systems Comprising Energy Storage Systems," IEEE Transactions On Power Systems, Vol. 29, No. I,January 2014.

[8]

Mosaddek Hossain Kamal Tushar, Chadi Assi, Martin Maier, and Mohammad Faisal Uddin, "Smart Microgrids: Optimal Joint Scheduling for Electric Vehicles and Home Appliances," IEEE Transactions on Smart Grid,Vol. 5,No. I,January 2014.

[9]

Tan Ma, Osama Mohammed, "Optimal Charging of Plug-in Electric Vehicles for a Car Park Infrastructure," IEEE Transactions on Industry Applications,DOl 10.11 091TIA.20 13.2296620.

also EKF estimation not allowed outside the range.

.

time (mlnutes)-:>

Figure 9. Temperature transfer rate of coolants

The temperature transfer rate from the battery pack to different coolant is compared in Fig. 9. A greater amount of temperature can be transferred from battery pack to coolant with lesser temperature increases of the cell. Temperature transfer coefficient of coolant oil is higher than that of water so that temperature can be removed easily from the battery.

time (minutes)

-.:>

Figure 10. Mean temperature of coolant and battery

The mean temperature variation of both the air and mineral oil cooling system is shown in Fig. 10. The variation of mean temperature of coolant and mean temperature of battery core are compared. The peak temperature is lower in mineral cooling system and it is higher in the case of air cooling system. In the same way time taken for mineral oil cooling is lower than that of air cooling system.

[10] Philipp Elbert, Tobias N"uesch, Andreas Ritter, Nikolce Murgovski and Lino Guzzella, "Engine On/Off Control for the Energy Management of a Serial Hybrid Electric Bus via Convex Optimization," IEEE Transactions on Vehicular Technology,2013. [II] David Dallinger, Jochen Link, and Markus Buttner, "Smart Grid Agent: Plug-in Electric Vehicle," IEEE Transactions on Sustainable Energy, Doi: IO.II09ITste.2014.2298887. [12] Guoyuan Wu,Kanok Boriboonsomsin,Matthew J. Barth,"Development and Evaluation of an Intelligent Energy-Management Strategy for Plug­ in Hybrid Electric Vehicles," IEEE Transactions on Intelligent Transportation Systems,DOl: 10. 1109/TTTS. 2013.2294342. [13] Mohammad Ashiqur Rahman, Qi Duan, and Ehab AI-Shaer "Energy Efficient Navigation Management for Hybrid Electric Vehicles on Highways," ICCPS 13,April. 2013,Philadelphia,PA,USA.

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