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.
184
