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Statistical Analysis of Lithium-Ion
Battery Data Collected On-Board
Electric Vehicles
Lin Peng
Master of Science Thesis EGI 2013:
Statistical Analysis of Lithium-Ion
Battery Data Collected On-Board
Electric Vehicles
Peng Lin
Approved
Examiner
Supervisor
Andrew Martin
Verena Klass
Commissioner
Contact person
Abstract
In order to replace diesel energy in the transportation sector as well as to reduce the emission of
green house gases (GHGs) and avoid air pollution for a sustainable future, electrification of
vehicles is one of the most popular topics today.
Plug-in hybrid electric vehicle (PHEV) technology is a promising technology for electrification of
automobiles. It uses both internal combustion engine and electric motor for propulsion. The
battery pack that propels the electric machine can be recharged from grid electricity and from
kinetic energy converted from regenerative braking.
In this thesis, field test data from a Volvo V70 prototype in a 2010 study by Volvo and Vattenfall
(ETC, Volvo, Vattenfall, 2010) was analyzed with Matlab to give a better understanding of the
usage of PHEVs and the performance of lithium-ion battery.
Several conclusions were obtained in this thesis from the analyzed data. It was found that average
and maximum driving speed in Diesel Mode is faster than that in Electric Mode. Different
drivers had different preference of driving speed. Driving distance vary in different months;
longer distance was running under Diesel Mode; A considerable number of 370 kg carbon
dioxide emission was saved by using electric energy instead of diesel energy for the studied car
during one year. Battery performance in cold temperature conditions needs to be considered and
the vehicle was switched to Diesel Mode from Electric Mode when SOC falls below 30%
Table of Contents
Abstract....................................................................................................................................... 1
1
Introduction ......................................................................................................................... 1
2
Objective .............................................................................................................................. 1
3
Literature review................................................................................................................... 2
3.1
Brief background .......................................................................................................... 2
3.2
Introduction of EV, HEV and PHEV .......................................................................... 3
3.2.1
Electric vehicle ...................................................................................................... 3
3.2.2
Hybrid electric vehicle ........................................................................................... 3
3.2.3
Plug-in hybrid electric vehicle ................................................................................ 3
3.3
Batteries........................................................................................................................ 5
3.3.1
3.4
Vehicle and battery characteristic .................................................................................. 8
3.4.1
Vehicle characteristic ............................................................................................. 8
3.4.2
Battery characteristics ............................................................................................ 8
3.5
4
Field testing of PHEVs............................................................................................... 10
Method............................................................................................................................... 11
4.1
Case study................................................................................................................... 11
4.1.1
5
Lithium-ion battery................................................................................................ 6
On-board collected data....................................................................................... 12
4.2
Data analysis with Matlab............................................................................................ 13
4.3
Define operating modes.............................................................................................. 13
4.4
Calculation .................................................................................................................. 14
Results................................................................................................................................ 16
5.1
Mode definition .......................................................................................................... 16
5.1.1
First division of vehicle operating modes ............................................................. 16
5.1.2
Final division of modes........................................................................................ 17
5.2
Driving distance .......................................................................................................... 17
5.2.1
Daily driving distance for each month around a year............................................ 17
5.2.2
Driving distance at different driving modes ......................................................... 19
5.3
Vehicle speed.............................................................................................................. 20
5.4
Engine power and battery power ................................................................................ 21
5.5
Energy ........................................................................................................................ 23
5.6
Carbon dioxide emission............................................................................................. 23
5.6.1
5.7
Battery temperature ............................................................................................. 23
State of charge ............................................................................................................ 24
6
7
Discussion.......................................................................................................................... 27
6.1
Driving mode.............................................................................................................. 27
6.2
Driving distance.......................................................................................................... 27
6.3
Vehicle speed.............................................................................................................. 28
6.4
Power and Energy and Carbone dioxide emission....................................................... 28
6.5
Battery temperature..................................................................................................... 29
6.6
SOC............................................................................................................................ 29
Conclusion ......................................................................................................................... 30
Acknowledgments ..................................................................................................................... 31
Reference................................................................................................................................... 32
Appendix A:M-script for days with 5th of Jan. 2010 as example ................................................ 34
Appendix B:M-script for months with Jan. 2010 as example ...................................................... 38
1 Introduction
Transportation is essential for economic and social development, which relies heavily on fossil
fuel-based vehicles in modern society (Mi et al. 2011). According to a report of the International
Energy Agency (IEA) published in year 2012, transportation is responsible for 61,5% of the
worldwide oil consumption. This value corresponds to 9660, 42 TWh of energy. In the year of
2010, 6733 Mton of carbon dioxide had been released whereas the electricity consumption in the
transportation sector was only 108 TWh, which corresponds to just 1% of the energy obtained
from the oil consumed in the same year (IEA 2012). In order to eliminate green house gases, air
pollution resulting from combustion of fossil fuels and to reduce our dependence on fossil fuel,
finding alternative forms of energy and improving vehicle efficiency are crucial topics.
Plug-in hybrid electric vehicle is one of the alternatives for replacing conventional diesel based
vehicle. It uses both internal combustion engine and battery for propulsion. With battery packs in
plug-in hybrid electric vehicles, the reduction of usage of diesel energy, green house gas emission
and air pollution are made possible. But until 2009, plug-in hybrid electric vehicle was still a
concept that has not been commercialized yet. There were not many field test made.
From 2009 to 2010, Vattenfall and Volvo were working on their joint project about Volvo plugin hybrid vehicle V70 prototype (ETC, Volvo, Vattenfall, 2010). The Volvo plug-in hybrid
vehicle V70 prototypes were converted by adding battery packs to normal Volvo V70 cars. Thirty
families were chosen by Vovlo and Vattenfall to drive the Volvo V70 prototype between 2009
and 2010 for field test. From CAN-bus (Controller Area Network) installed on the vehicle, more
than 38 different kinds of signals were collected by ETC AB during the field test.
2 Objective
The objective of this master thesis is to use Matlab to analyze the data collected from the field
tests by ETC AB on the Volvo V70 prototypes, in order to study the performance of the vehicle,
the driving pattern of the driver and the lithium-ion battery properties, so to give a contribution
to better understanding of plug-in hybrid electric vehicles. Conclusions about performance of
the vehicle, driving pattern of driver and lithium-ion battery properties will be drawn from the
Matlab results.
1
3 Literature review
3.1 Brief background
As mentioned before, though transportation is essential for economic and social development, it
relies heavily on fossil fuel-based vehicles in modern society (Mi et al. 2011). According to a
report released by Ward Auto, the number of vehicles operating in the world remarkably
surpassed the 1 billion mark in year 2010. As much as 1,015 billion cars were registered globally
in 2011 (Sousanis 2011). Moreover, vehicle production seems recovering from the slowdown
following the economic crisis around year 2008. In 2011 the overall production reached
79,989,155 units, showing a 3.1% increase compared to the vehicle production in 2010.
World Motor Vehicle Production
1999 to 2011
90.000.000
80.000.000
70.000.000
60.000.000
50.000.000
40.000.000
30.000.000
20.000.000
10.000.000
0
1999
2000
2001
2002
2003
Cars
2004
2005
2006
2007
2008
2009
2010
2011
commercial vehicle
Figure 3.1.1 World motor vehicle production 1999 to 2011 (Sousanis 2011)
As a result of such fast growth of the vehicle production and of the corresponding fuel demand,
the fuel production is encountering difficulties to satisfy the market needs. Moreover, the
environmental problems caused by the use of fossil fuels are of major concern today. In year
2010, 41.2% of the fuel consumption has been oil, and 61.5% of the oil has been consumed in
transportation sector in the world, which leads to 22.2% of carbon dioxide emission in 2010
(IEA 2012).In order to reduce air pollution and protect the planet from global warming, it is
urgent to cut fossil fuel consumption and reduce carbon emissions in transportation. Alternative
fuels like biodiesel, compressed natural gas, ethanol, hydrogen and commercia l propane and
commercial butane (LPG) systems and electric propulsion systems like electric battery powered
systems, hybrid systems are being studied and some are already available on the market. In this
thesis, plug-in hybrid vehicles were studied.
2
3.2 Introduction of EV, HEV and PHEV
3.2.1
Electric vehicle
EV is short for electric vehicle which is powered by a battery. EVs were invented 60 years earlier
than gasoline-powered cars in 1834 (Mi et al. 2011). It replaces internal combustion engine of
conventional vehicles with battery as electricity storage and use electric motor (EM) for
propulsion. The battery is the one and only energy source for the operation of vehicle. The
battery can be charged from grid electricity and from the regeneration of braking kinetic energy
(IEA 2009). With electricity as energy source, EVs have no local and very low total GHGs
emission and air pollutants compared with conventional ICE powered vehicles, and GHGs
emission and air pollutants are able to be assigned to zero if the electricity used to charge the
battery comes from renewable energy such as solar energy and wind energy. Compared with
conventional internal combustion powered vehicles, since EVs are powered by electric motor
with electric energy, it has much higher efficiency and lower motor cost. The main problem of
EVs is the battery. Batteries for EVs can only provide a limited all electric range since batteries
for EVs have very low energy and power density compared with liquid fuels under current
technology (IEA 2009). And since the battery technique is not in mass production yet, the price
for batteries is high as well.
3.2.2
Hybrid electric vehicle
HEV is short for hybrid electric vehicle. The first HEV was built in 1898 in Germany by Dr.
Ferdinand Porsche (Mi et al. 2011). It can be seen as a combination of conventional ICEpowered vehicle and an EV. It uses mainly ICE for propulsion, but also uses battery power when
needed. Electrical motor can be used to provide auxiliary energy to ICE and can allow ICE to
shut down at low vehicle speed. The battery can be recharged both by engine and by regeneration
of brake energy, but not by gird charge. Thus a typical battery nominal energy (Wh) around 1
KWh to 2 kWh is sufficient for HEVs (IEA 2009). Compared with conventional ICE powered
vehicles, in HEVs system efficiency, fuel economy and engine performance are increased by
electric motor, as it can recover kinetic energy from braking and adjust engine torque and speed
to optimize ICE operation (Mi et al. 2011). As a result HEV is more environmentally friendly as
compared to conventional vehicles. Of course, HEV has larger driving range but it is less green
than EV.
3.2.3
Plug-in hybrid electric vehicle
PHEV is short for plug-in hybrid electric vehicle. Reading from its name, it tells that PHEV is a
HEV with a plug-in cable which allows the battery to be recharged from an electricity grid. It is a
combination of EV and HEV (Mi et al. 2011). It can have at least 5 times the battery capacity
than HEVs nowadays, and it is also less dependent on charging infrastructure and has possibly a
larger driving range than EVs (IEA 2009). Thus PHEV technology is a promising technology in
the early years of electrical vehicle development and promotion, with its acceptable driving range,
green concept and good fuel economy. There are rarely any field test for PHEV cars, as PHEV
technology is a relative new technology, but also it is expensive and time consuming to perform a
field test.
3
According to the book of Chris Mi et. al. it may maintain the driving need of American
households solely by battery (Mi et al. 2011).
Just like HEVs, PHEVs can connect ICE and electric motor in series, in parallel or in series parallel in a power point of view as illustrated in the figures below.
Figure 3.2.1 The architecture of series connection with peak power unit (Bossche 2006)
Figure 3.2.2 The architecture of parallel connection with peak power unit (Bossche 2006)
4
Figure 3.2.3 The architecture of series-parallel connection with peak power unit (Bossche 2006)
There were some electric car companies selling EVs and HEVs in 1900s, but due to immature
battery technology, high manufacture cost, inconvenience of charging, low market acceptance,
lack of government support and the low gasoline price by that time, all companies failed soon
(Mi et al. 2011).
As the fossil fuel price and fuel demand is dramatically increasing, and more and more worldwide
environment concerns arise, electric cars start to be interesting again. In the year of 1997, the
appearance of the first modern hybrid electric car which is known as Toyota Prius in Japan
turned the history of world’s automotive history to a new chapter.
3.3 Batteries
Battery is a device made of electrochemical cells that convert stored chemical energy to electrical
energy and vice versa (Merriam-Webster 2009). Battery packs with high voltage are used for
PHEVs. A battery pack is a collection of battery modules arranged in a series or in a parallel
combination. And a battery module is assembled by connecting battery cells in series or in
parallel (Team 2008). A series connection increases the total voltage without changing the
current, while a parallel connection increases the total current without changing the voltage.
A battery energy storage system for PHEVs consists of battery pack, conditioning system based
on power electronics and control system. The battery pack in PHEVs can provide energy for the
traction motor and store energy from grid charging or regenerative braking energy. Power
electronics converters are used to provide interfaces between batteries and ICE power, and
between batteries and utility power for the transition of different forms of energy. And the
control system is used to manage power and energy based on the need (Mi et al. 2011).
Today, there are some different energy storage technologies like Li-ion battery, nickel metal
hybrid battery (NiMH), lead acid battery and ultra-capacitors available for electric cars, where
Lead Acid, NiMH and Li-Ion are the major ones in the market. Table 3.3.1 shown below gives a
5
comprehensive qualitative comparison of Lead Acid, NiMH and Li-Ion type of battery
technologies.
Table 3.3.1 Qualitative comparison of major automotive battery technologies (A.Pesaran 2011)
Attribute
Lead Acid
NiMH
Li-lon
Weight (kg)
Poor
Fair
Good
Volume (L)
Poor
Good
Good
Capacity/Energy (kWh)
Poor
Fair
Good
Discharge Power (kW)
Good
Fair
Good
Regen Power (kW)
Good
Good
Good
Cold-Temperature(kWh&kW)
Good
Fair
Poor
Shallow Cycle life (number)
Good
Good
Good
Deep Cycle life (number)
Poor
Fair
Good
Calendar Life (years)
Poor
Good
Fair
Cost ($/kW or $/kWh)
Good
Poor
Poor
Safety- Abuse Tolerance
Good
Good
Fair
Maturity- Technology
Good
Good
Fair
Maturity-Manufacturing
Good
Good
Fair
Comparing those three battery technologies, it is easy to see that the performance of Li -Ion is
really outstanding beyond the others except at cold temperatures. Since Li-Ion is a comparably
new technology, the technology itself and the manufacturing of Li-Ion are not mature yet, which
lead to a relative high battery cost. Also the safety of Li-Ion battery is still not really satisfactory at
the state of art currently achieved. But all these problems may be solved by developing Li-Ion
technology even further.
With good performance and small size, Li-ion battery is the most promising battery technology
for HEVs or PHEVs today.
3.3.1
Lithium-ion battery
A lithium-ion battery is a rechargeable battery. As any other batteries, a lithium battery cell
consists of a positive electrode (anode), a negative electrode (cathode), and a separator. Lithium
ions move from the anode to the cathode while discharging, and move in the opposite direction
while charging as shown in the following figure.
6
Figure 3.3.1 Lithium-ion battery rechargeable battery charging and discharging mechanism
(Brain 2006)
The overall chemical reaction equation of a Li-ion cell is (Mi et al. 2011) :
(3.3.1)
for instance, when using the chemical LiCoO 2 the reaction on the positive electrode is (Gold
Peak Industries 2003) :
(3.3.2)
while the reaction on the negative electrode is (Gold Peak Industries 2003):
(3.3.3)
The nominal voltage, power density and energy of lithium-ion cells vary among different
chemical types of lithium-ion batteries. Lithium-ion battery cell can normally provide from 2.5 V
to 4 V depending on the chemistry used (Andrea 2010).
Lithium-ion batteries have the highest power density and energy density among any available
commercial cells today. With its great performance it is becoming popular for vehicle’s traction
pack. It also has the advantage of no memory effect, low self-discharge rate and it is rather
environmentally safe.
Meanwhile, thermal runaway is one of the concerns of lithium-ion batteries, which may lead to
fire or explosion. But also other features like cell balancing, pressure relief and overcharging can
be of some concern to lithium-ion batteries. Thus battery management systems are needed to
control battery pack.
7
3.4 Vehicle and battery characteristic
In order to have a better understanding of the vehicle and battery properties discussed in the
following chapters, some relevant terms and concepts are first introduced in this chapter.
3.4.1
Vehicle characteristic
Vehicle performance is always the first priority for a car design. When considering the design of
electric cars, there are limitations caused by the introduction of a battery system. For instance, the
maximum speed and acceleration are limited due to the limited power a battery pack in the car
can supply. Thus, the design of power trains is essential for the performance of an electric car.
Initial acceleration, cruise speed, maximum speed, drive cycles, gradability, drive range, fuel
economy and so on are typical specifications when considering performance (Mi et al. 2011).
Initial acceleration determines the time needed to start-up a car from rest to a required speed and
it depends on the power supply form the car and on the road condition. Gradability is the ability
of the car to climb a grade with a given speed. Drive range is referring to the distance a car could
drive depending solely on fully charged battery before a second recharge is needed. Fuel economy
refers to the distance a vehicle can travel with a unit of fuel (Mi et al. 2011). Power, energy of the
battery, the strategy with which the battery is used and the way the battery module connected to
a battery pack can have effect on these performance specifications. By studying these
specifications, directions to improve the performance may be found.
3.4.2
Battery characteristics
In order to study battery performance, the terms for battery characteristics listed below referring
to MIT electric vehicle tram (Team 2008) and Mi. Chiris and others(Mi et al. 2011) can be
applied.

All electrical driving range: Distance that can be run with only electrical power.

Battery capacity (C): The battery capacity indicates the total number of electric charge
that the battery can release at a certain discharge current before it is fully discharged.
Though Coulomb is the SI unit of battery capacity, ampere-hour (Ah) is generally used.
One ampere-hour is equal to 3600 Coulombs. For instance a battery with 30 Ah of
capacity, means it can provide 30 A of current in 1 h, or 1 A of current in 30 h.

C-rate: C-rate is a measure of the discharging rate of a battery relative to its maximum
capacity. It is used to normalize against battery capacity. A battery with nC rate means it
will be discharged in 1/n hours. For a battery with 5 Ah capacity, the 5 C rate would be
25 A while C/5 would be 1 A.

Energy Stored (E): Refers to energy stored, and indicates the total magnitude of electric
energy that the battery can release at a certain discharge current before it is fully
discharged. It is dependent on battery voltage and battery capacity. Watt hour (Wh) is the
SI unit of energy stored.
8

Power (P): Amount of energy that battery can supply at an instant of moment of time in
W.

Energy density and power density: Amount of energy and power per unit volume of
battery.

State of Charge (SOC): The SOC is a measure of the remained capacity in the battery
compare with the maximum capacity of battery in percentage.
It is defined by the following equation:
(3.4.1)
Where,
is the charging current at time t, is the charging time from start and Q0 is
the maximum capacity of the battery. A typical range of SOC for PHEV is from 20 to
95%.

Depth of Discharge (DOD): On the contrary to SOC, DOD is a measure of capacity
discharged compare with battery maximum capacity in percentage. And DOD is given by
the following equation:
(3.4.2)
Where,
is the charging current at time t, is the charging time from start and Q0 is
the maximum capacity of the battery.
In order to prevent battery from being damaged, normally a battery cannot be totally
discharged. Cutoff voltage is defined as the minimum allowable voltage. And the point of
cutoff voltage is referred as 100% DOD. In this thesis, DOD data was converted to SOC
with the formula shown below:
SOC = 100 - DOD
(3.4.3)

Internal resistance (Ohm): The resistance of battery. It varies with charging and
discharging, SOC, and battery temperature. It represents the difficulty of charging or
discharging the battery. The higher the internal resistance, the more energy is converted
to wasted heat.

Cycle life: All batteries have a time limitation due to chemical side reactions occurring
inside the battery. A cycle life is defined as the number of charge and discharge cycles a
battery could perform before it fails the specific criteria of performance. The cycle life of
battery can be affected by charge and discharge strategy, battery temperature and other
conditions. It is a very important parameter for electric vehicle to reduce the operational
cost.
9

Calendar life: It is a measure of the capacity loss due to self-discharge, which is in turn
dependent on other factors such as electrolyte leakage, partial dissolution of the electrode
and so on (University of South Carolina 2013).

Abuse tolerance: Abuse tolerance means that the battery is under some external abuse
conditions, such as external heating, over charging, over discharging, high current
charging, nail penetration, crush or external short.
3.5 Field testing of PHEVs
Though PHEV is a promising technology to replace conventional diesel based vehicles for the
sustainable future, it is still a comparable new concept. Until 2009, there was no commercially
available PHEV in the market yet (Nicolas Dehlinger 2009). Most of available literatures were
using simulations, models to evaluate the potential and performance of PHEVs (E. 2007; EPRI
2007; Tomic J. 2007). Moreover, laboratory tests were also performed in some literatures to
standardize fuel consumption and emissions of PHEVs (Carlson R. B. 2007).
However, there are still some factors, such as climate, driver's habit, road conditions that are hard
to involve in these simulations, models or laboratories. And these real world factors can have a
big influence on the cost of PHEVs in real world. Thus, not only to study PHEV vehicle and
battery performance in the field, but also to evaluate cost and gain market proven of PHEV, field
test of PHEVs is obviously needed.
10
4 Method
4.1 Case study
Vattenfall has worked with electric vehicles ever since 1980's (ETC, Volvo, Vattenfall, 2010).
Within the E-mobility project, the Research and Development (R&D) side of Vattenfall has been
concerned about the electrification of transports. The project which data of this thesis was taken
from, was a joint project between Volvo and Vattenfall and financed by Swedish Energy Agency
since year 2007 (ETC, Volvo, Vattenfall, 2010).
Figure 4.1.1 Photo of Volvo V70 prototype (English 2009)
In this joint project between Vattenfall and Volvo, two Volvo V70 prototypes were converted
from the original Volvo V70s to diesel-electric plug-in hybrid prototype vehicles by Volvo. Both
of these Volvo V70 prototypes are equipped with 32 Ah Li-ion battery packs (ETC, Volvo,
Vattenfall, 2010).
Table 4.1.1 and 4.1.2 below show some vehicle and battery specifications of Volvo V70
prototype.
Table 4.1.1 Specification of Volvo V70 prototype vehicle properties (English 2009) (ETC, Volvo,
Vattenfall, 2010)
Diesel engine torque
449 Nm
Electrical motor torque
220 Nm
Electric rear wheel drive
74 kW
11
5-cylinder diesel front wheel drive
150 kW
Table 4.1.2 Specification of battery properties (ETC, Volvo, Vattenfall, 2010)
Type
PHEV Li-ion
Number of cells
192
Thermal management
Cabin air cooled
Weight
150 kg
Volume
150 L
Vathode
Nickel manganese cobalt oxide(NMC)
Anode
Hard carbon
Nominal voltage
350 V
Nominal capacity
32 Ah
Maximimun voltage
395 V
Minimum voltage
270 V
Maximum charge/discharge current
250 A
Total battery size
11 kWh
Usable battery size
7 kWh
These two prototype vehicles have been in field testing, from December 2009 to December 2010
in the Göteborg-area, the Stockholm-area and Varberg (Sweden). The Volvo V70 was tested by
30 different families which were selected by Vattenfall and Volvo. Most of the families have used
the car for one month. Besides recharging from electricity grid at home or work place, Vattenfall
also developed extra charging points for electricity supply (ETC, Volvo, Vattenfall, 2010).
4.1.1
On-board collected data
Data was collected from the CAN bus (Controller Area Network).When the car was moving or
recharging from the grid, the data was logged every half second. Both battery pack behavior and
operating conditions were collected during the tests. All CAN bus data was collected by ETC
Battery and FuelCells SwedenAB from January to December 2010 (ETC, Volvo, Vattenfall,
2010).
Data that was collected in 2010 for Volvo V70 PHEV WPA642 was used in this thesis. The data
was given in a form of Matlab data file. Each of these files contained all the data that has been
collected from a corresponding trip. These files were named in the form of
WPA642_year_month_date_starting time. Along with the number of trips a driver made in a
certain day, there may have 3 to 15 files of trips collected in a single day. Data such as vehicle
speed, engine speed, battery current, battery voltage, ambient temperature, battery temperature, in
car temperature, maximum battery temperature, minimum battery temperature, DOD, diesel
ERAD active, line current, line voltage, odometer master value were used in this thesis.
12
4.2 Data analysis with Matlab
As mentioned above, on-board collected data was given in the form of .mat file, which can be
worked with in the Matlab. Thus, the mathematical software Matlab was used as the tool to
analyze and calculate the data in this thesis. But also, Matlab itself is a well-known software that is
widely used in many fields such as engineering, finance and so on.
With Matlab, m-scripts were written to gather collected data for different time scale, trips into a
day, days into a month. In the m-scripts transfer files of trips to files of days, collected data such
as All time, All time absolute, ambient temperature, average battery pack temperature, battery
current, battery voltage, battery temperature maximum, battery temperature minimum, DOD,
Diesel_ERAD_active, ERAD motor speed, engine speed, line current, line voltage, motor
temperature, PEU statue, line current, line voltage and vehicle speed were logged from each trip
files to perform a statistic calculate. Formulas were used to calculate daily battery power, engine
power, battery energy, diesel energy, SOC and carbon dioxide emissions. Moreover, files of
months were further obtained by logging data form files of days. These m-scripts are shown in
the Appendix A and Appendix B.
4.3 Define operating modes
The Volvo V70 prototypes were built to work simply either in Electric Mode or Diesel Mode by
choice of the diver. Meanwhile, similarly to standard hybrids, Volvo V70 prototypes can
regenerate energy with its regenerative braking to the battery and recharge battery from its
engine. As shown in Table 4.3.1, three different modes were defined by the CAN bus when
collecting data from the field testing vehicle WPA642.
Table 4.3.1 Mode defined in CAN bus
Diesel_ERAD_active =1
Diesel_ERAD_active =2
Diesel_ERAD_active =0
Driving in Diesel Mode
Driving in Electric Mode
Grid charge or else( vehicle at
rest)
In order to eliminate false data due to CAN bus problem, but also to study vehicle, driving
patterns and battery behavior when regeneration happens, operating modes were further defined
manually in Matlab as Diesel Mode, Electric Mode, Regeneration Mode and Grid Charge Mode.
These defined modes acted as index for reading other values, like vehicle speed, engine speed
and others at the corresponding conditions as defined in the modes.
13
4.4 Calculation
The collected data was present in a mat file for each trip. For the ease of further working, the trip
files for the same days were calculated and combined first. And then those files of days were
further calculated and combined to files of months.
Statistic calculations such as maximum, average, minimum were done by build-in code in Matlab.
Other calculations are shown below with their corresponding formulas.
State of charge (SOC): As introduced before SOC is the reverse concept of DOD, which can
be calculated with equation 3.4.3.
Battery power: The same as electrical power, battery power P was calculated by multiplying
voltage U and current I. And the result was divided by 1000 to obtain unit of kW.
(4.4.2)
Here U is battery voltage in Volts, and I is current flow in the battery in A.
Discharge power, recharge power and regenerative power were calculated by putting all battery
power results with corresponding indexes such as Electric Mode, Grid Charge Mode and
regeneration mode.
When calculating average battery power and counting the power distributions, which is presented
in the results section, the values of the battery power equal to zero were excluded from the data.
Engine power: With given engine speed, the engine power was calculated with diesel engine
torque with the equation below.
(4.4.3)
Here, rpm is the engine speed in rpm ,
unit horse power (hp).
is the engine torque in Nm and 5252 is the factor to get
Energy(E): Energy is defined as the integral of power P over time of working.
(4.4.4)
Energy consumption of engine, energy discharged from battery, energy regenerated from
regenerative braking and energy recharged from grid were all calculated with their corresponding
power over the length of each power data in Matlab file and divided by 2.
When combining each trip together as one day, the time series logged were not continuous
anymore. Moreover, time gaps were always found between each trip, since the data was not
logged when the vehicle was under complete rest. If one uses the time to make the integration,
Matlab will connect the last point of the trip to the first point of next trip automatically to
calculate integrals, and this will lead to considerable mistakes in the results. As it is known from
Volvo, the data was logged each half second, it can be considered mathematically reasonable to
calculate distance by integrating the vehicle speed with respect to the data length of the logged
vehicle speed and divide by 2.
14
Distance(S): The total distance for each day was calculated by integrating all logged vehicle
speed and divided the result by 2 because of the same reason explained before.
(4.4.5)
Distance run under Electric Mode and Diesel Mode was calculated by integrating their
corresponding velocity under each mode.
Carbon dioxide emission: CO 2 emission was calculated with the following formulas:
(4.4.6)
Here, diesel engine efficiency was assumed as 30% (Melody 2007), energy density of diesel was
assumed as 38.6 MJ/L, CO 2 emission per liter of gasoline was assumed as 2.68 kg /L (A&S
2004).
(4.4.7)
Here , it is assumed that 43 kg CO 2 (A&S 2004) emit with 1 kWh of grid electricity generated.
The range and distribution of some resulted data was calculated as well to help to have a better
understanding about vehicle performance and battery usage.
15
5 Results
5.1 Mode definition
Mode definition is the basic for studying other results, which work as index when reading other
interesting values. Result of mode definition is introduced below at first
5.1.1
First division of vehicle operating modes
Mode Diesel Only
Mode Diesel Only was defined when Diesel_ERAD_active equal to 1 and battery current equal
to zero. Additional condition as Engine Speed larger or equal to zero and Vehicle speed larger or
equal to zero were included in mode diesel definition to exclude NaN values when reading other
interesting values with defined as index. Those NaN values may disturb reading and calculation
of read values.
There was an exception defined as Mode Diesel Only Special. In this Mode Diesel Only Special,
Diesel_ERAD_active equal to 2 and battery current equal to zero while engine speed larger than
zero. This exception case can be caused by fault collection of data by CAN bus.
In this thesis the conditions of Mode Diesel Only mentioned before and Mode Diesel Only
Special were both included to define the final Mode Diesel Only.
Mode Battery Only
Mode Battery Only was simply defined as Diesel_ERAD_active equal to 2 and engine speed
equal to zero, with additional battery current lager or equal to zero and vehicle speed larger or
equal to zero to exclude NaN values. There were no exceptions in battery only mode.
Mode Combine
Mode Combine was a peculiarity in the study. As mentioned before, Volvo V70 prototype was
only designed to run either in Electric Mode or Diesel Mode, while in field data there was a
considerable number of data showing combined occasions. For a better understanding of data,
those combined occasions were found out and discussed below.
Mode Combine 01 was defined as Diesel_ERAD_active==1, engine speed larger than zero and
battery current larger than zero.
Mode Combine 02 was defined as Diesel_ERAD_active==2, engine speed larger than zero and
battery current larger than zero.
Looking at the engine speed and battery current under these two modes, it was not hard to find
out battery current was always very low values, while engine speed was quite high .
Mode Regeneration
As mentioned before, PHEV can regenerate electricity from kinetic energy at braking with brake
regeneration technology. Mode Regeneration Diesel was defined as Diesel_ERAD_active equal
to 1, engine speed larger or equal to zero and battery current less than zero. There was an
exception again for Mode Regeneration Diesel, where Diesel_ERAD_active equal to 2, engine
speed larger than zero and battery current less than zero. Mode Regeneration Battery was defined
as battery current less than zero, engine speed equal to zero and Diesel_ERAD_active equal to 2.
All above mentioned mode were included in Mode Regeneration.
16
5.1.2
Final division of modes
Diesel Mode
As introduced before, this Volvo V70 prototype was only design to run at either in Diesel Mode
or Electric Mode, only explanation that may make sense to clarify the presence of Mode
Combine can only be fault of CAN bus when it was logging data . The values of engine speed
were quite big and values of battery current were actually small, Mode Combine was considered
as one part of Diesel Mode.
Mode diesel only together with Mode Combine and Mode Regeneration diesel were consider as a
total Diesel Mode.
Electric Mode
As explained before in Diesel Mode, battery current in Mode Combine were too small. It can
only considered as either battery fluctuation or fault reading of CAN BUS. Thus Electric Mode
only include in Mode Battery Only.
Battery Regeneration Mode
Battery Regeneration Mode was defined as the same as Mode Regeneration. There were be some
overlaps when applying both Battery Regeneration Mode and Diesel Mode as index, as Diesel
Mode shared Mode Regeneration Diesel part of Mode Regeneration. Further discussion was
presented in next chapters.
Grid Charge Mode
Grid Charge Mode was simply defined as battery less or equal to zero, grid line current larger or
equal to zero and Diesel_ERAD_active equal to 0.
5.2 Driving distance
Driving distance is a value that tells about how long a driver drives with the vehicle. It is one of
the important indications for vehicle design. It not only tells about driver's driving preference,
but also give a idea of how much battery capacity should the battery have to reach the expected
all electric driving range of drivers.
5.2.1
Daily driving distance for each month around a year
Table 5.2.1 Statistics of driving distance and days of driving for each month over a year
Distance(km) Jan
Total
Average/day
Max/day
No. of days
Feb Mar Apr May Jun
615 996
989
269
Jul
Aug
486 1511 1586 2027
Sep
Okt
Nov
Dec
1077 1584 1417 1125
28
39
49
21
26
58
72
88
47
72
54
51
139
88
118
77
78
220
237
556
81
176
228
163
22
25
20
13
19
26
22
23
23
22
26
22
17
Reading from statistic value shown in Table 5.2.1, driving distance varied from 269 to 2027 km in
different months. But also maximum driving distance and average daily driving distance varies
quite a lot in different months too. Calculated from the table, the average of daily distance over a
year was 50km.
Mean while as shown in table above, tested families were driving Volvo V70 prototype majority
days of each month, but not every day. The number of driving days varies at each month.
Daily Distance In Different
Months
70,00%
60,00%
50,00%
40,00%
30,00%
20,00%
10,00%
0,00%
Jan
Feb Mar Apr Maj
0-10km
Jun
10-40km
Jul
Aug Sep Okt Nov Dec
40-80km
over 80
Figure 5.2.1 Percentage of driving distance in certain ranges for different months
The distance range which daily driving distance located was pretty different as shown in Figure
5.2.1. In most months driving distance locate in the range 10 to 40 km and 40 to 80 km, while in
months like August and especially October driving distance located in over 80km range. The
reason that the range was defined as 0 to 10km, 10 to 40 km, 40 to 80 km and over 80 km, but
not an even way was to obtain a figure to tell better about distribution.
Comparing Figure 5.2.1 above and Figure 5.2.2 below, it was true and clear that in August and
October driving the distance was higher than that in other months. And in August there was an
especially long distance trip in one of the day. In July, though most driving distances were located
in the range 40 to 80 km, it still have high average daily driving distance with its high maximum
driving distance.
Figure 5.2.2 also shows details of driving distance at each day for each month. It tells that in
February, March, September and October driving distance for each day was rather evenly, while
in other months driving distance varies daily.
18
Figure 5.2.2 Driving distance at each day for all 12 months
5.2.2
Driving distance at different driving modes
Driving distance (Km)
Driving Distance Under Different
Driving Modes
1600
1400
1200
1000
800
600
400
200
0
4
Feb
Mar Apr
Electric Mode
Maj
Jun
Diesel Mode
Jul
Aug
Sep
Okt Nov Dec
Regeneration Mode
Figure 5.2.3 Driving distance under different driving mode for each month
Over the year, 5726 km or 57% of total distance was running under Diesel Mode, 4012 km or
30% of total distance was running under Electric Mode. 3130 km or 23% of distance was running
under Regeneration Mode, where 10% of it was overlapped with Diesel Mode due to the
definition of Diesel Mode and Regeneration Mode discussed in chapter 5.1. Figure 5.2.3 above,
showed that even though Volvo V70 prototype is a PHEV, most distance was running under
Diesel Mode by test drivers.
19
The number of distance under Regeneration Mode was remarkable, which demonstrated the
advantage of regenerative braking system of a PHEV.
5.3 Vehicle speed
Figure 5.2.3 and Figure 5.3.1 illustrated the fact that, not only more distance was running under
Diesel Mode than Electric Mode, but also vehicle speed was faster when vehicle was running
under Diesel Mode than under Electric Mode.
Average Vehicle Speed Under
Different Operating Modes
Vehicle Speed(km/h)
70
60
50
40
30
20
10
0
Jan
Feb
Mar
Apr
Maj
Jun
Electric mode
Jul
Aug
Sep
Okt Nov Dec
Diesel Mode
Figure 5.3.1 Average vehicle speed under different driving mode in each month over a year
Max Vehicle Speed Under Different
Operating Modes
Vehicle Speed (km/h)
140,0000
120,0000
100,0000
80,0000
60,0000
40,0000
20,0000
0,0000
Jan
Feb Mar Apr May Jun
Max vehicle speed at Electric Mode
July Aug Sep
Oct Nov Dec
Max vehicle speed at Diesel Mode
Figure 5.3.2 Max vehicle speed under different driving mode in each month over a year
As shown in Figure 5.3.2, both at Electric Mode and at Diesel Mode the maximum vehicle speed
were pretty high.
20
5.4 Engine power and battery power
Read and calculated from property table in chapter 1, the nominal power of battery is 87.5 kW .
The overall average Engine power was 89 kW and overall average battery power was 10 kW. The
maximum engine power was 316kW and maximum battery power was 89 kW over the year.
Average And Max Battery Power
Over Each Month Through A Year
100
power (kw)
80
60
40
20
0
Jan
Feb
Mar
Apr May
Jun
Max Battery Power
July
Aug
Sep
Oct Nov Dec
Average Battery Power
Figure 5.4.1 Max and average battery power in different months over a year
Average and Max Engine Power
Over Each Month Through a Year
350,0000
power (kw)
300,0000
250,0000
200,0000
150,0000
100,0000
50,0000
0,0000
Jan
Feb Mar Apr May Jun
Max Engine Power
July Aug Sep
Oct Nov Dec
Average Engine Power
Figure 5.4.2 Max and average Engine power in different months over a year
From the result above, one can say that the average engine power was almost equivalent to the
maximum battery power. And the average battery power was truly small in each month.
21
Histogram of Engine Power Within
Each Month
70,00%
60,00%
50,00%
40,00%
30,00%
20,00%
10,00%
0,00%
Jan
Feb
Mar
0-50kW
Apr
Maj
50-90kW
Jun
Jul
90-120kW
Aug
Sep
Okt Nov Dec
over 120kW
5.4.3 percentage of engine power locate in different range
From the table above, it is not hard to tell that most of engine power located in the range 90 to
120 kW, and followed by 50-90 kW range and 0-50kW. There were not many occasions that the
vehicle is driving with power more than 120 kW.
Histogram of Battery Power Within
Each Month
50,00%
40,00%
30,00%
20,00%
10,00%
0,00%
Jan
Feb
Mar
0-1kW
Apr
Maj
1-5kW
Jun
Jul
5-25kW
Aug
Sep
Okt Nov Dec
over 25kW
5.4.4 percentage of battery power locate in different range
On the other side, most battery power located in a range of 0 to 5 kW, in some occasions it
located more in the range of 5 to 25kw. There was not much battery power locate in the range
over 25 kW, except December.
22
5.5 Energy
Table 5.5.1 Energy consumption, regeneration and recharging in each month
kWh Jan
Feb
Mar
Apr
Maj
Jun
Jul
Aug
Sep
Okt
Nov
Dec Year
A
1682
1722
2079
420
1241
3480
3033
4550
1116
3160
2920
3288
28692
B
150
343
273
87
135
321
284
266
423
342
359
207
3189
C
12
27
24
8
15
37
38
34
44
45
51
22
357
D
221
387
284
106
161
334
328
289
425
351
373
234
3492
A: Energy consumption of diesel engine
B:Energy consumption of battery
C:Energy regenerated from regenerative braking
D:Energy from grid recharge
The table above shows larger energy consumption in Diesel Mode compared with Electric Mode.
It also illustrates that a fair amount of energy was regenerated from braking and engine. As the
table tells, there was more battery recharging than battery discharging.
5.6 Carbon dioxide emission
Table 5.6.1 Carbon dioxide emission in different months
CO2 (kg)
Jan Feb
Mar Apr May
Jun Jul
Aug Sep Oct Nov Dec Year
Diesel
Mode
195 200
241
49
144
404 352 528
129
367
339
381
3328
Electric
Mode
3
8
6
2
3
7
7
6
10
8
8
5
74
Saved
17
40
32
10
16
37
33
31
49
40
42
24
370
Saved: Reduced CO 2 emission by using electrical energy compared with diesel
energy
There was a huge amount of CO 2 emission in Diesel Mode, while CO 2 emission in battery mode
was really little.
The CO 2 emission of Electric Mode was calculated by convert battery discharge energy to
corresponding gasoline needed with equation (4.4.7). There was a considerable number of CO 2
emission saved by using electrical energy than fossil energy.
5.6.1
Battery temperature
Reading from collected data, maximum battery temperature was 43 ℃ in June and July and
minimum battery temperature was -6 ℃ in February.
23
Divided by season, the average battery temperature from spring (March to May) was 19 ℃, in
summer (June to August) was 27 ℃, in autumn (September to November) was 20 ℃ and in
winter (December to February) was 22 ℃.
Battery Temperature
Battery Temperature ℃
50
40
30
20
10
0
-10
Jan
Feb
Mar
Apr
Maj
Jun
Average battery temperature
Jul
Aug
Sep
Okt Nov Dec
Max battery temperature
Min battery temperature
Figure 5.6.1 Average, maximum and minimum battery temperature for each month
Histogram of Daily Average Battery
Temperature Within Each Month
100,00%
80,00%
60,00%
40,00%
20,00%
0,00%
Jan
Feb
Mar
Apr
below 5◦
Maj
Jun
5-15◦
Jul
15-25◦
Aug
Sep
Okt Nov Dec
over 25
Figure 5.6.2 Daily average battery temperature distribution in different months
5.7 State of charge
State of charge (SOC) was calculated by 1-DOD as illustrated in equation (3.4.3), where DOD is
the depth of discharging. It represents how much battery energy remains in the battery.
24
Histogram of SOC of Each Month
120
SOC(%)
100
80
60
40
20
0
Jan
Feb
Mar Apr
Maj
avg
Jun
Jul
max
min
Aug
Sep
Okt Nov Dec
Figure 5.7.1 Overall SOC of battery in each month
Figure 5.7.1, also shows that the average SOC stay in a interval of 60 to 80% in each month. The
minimum SOC lay around 15 to 20 % range in each month, except an exception where the
battery was almost running out in April.
For SOC split in different modes, the results are shown in Figure 5.7.2 and Figure 5.7.3.
SOC of Each Month in Battery
Mode
120
SOC(%)
100
80
60
40
20
0
Jan
Feb
Mar
Apr
Maj
avg
Jun
Jul
max
min
Aug
Sep
Okt Nov Dec
Figure 5.7.2 SOC of battery in Electric Mode in each month
The Figure 5.7.2 tell the fact that not only the max SOC was the same in Electric Mode as 98.4%,
but also the minimum SOC were the same as 29.6% for all the months, except September and
December which were 30%. All the average SOC of each month lie in the range of 65-80% in
Electric Mode.
25
Histogram of SOC of Each Month
in Diesel Mode
120
SOC(%)
100
80
60
40
20
0
Jan
Feb
Mar Apr
Maj
avg
Jun
Jul
max
min
Aug
Sep
Okt Nov Dec
Figure 5.7.3 SOC of battery in Diesel Mode in each month
Reading from Figure 5.7.2, the same as Electric Mode case, max SOC was the same in Diesel
Mode as 98.4%, while the minimum SOC varied under 25%. The smallest minimum SOC value
was in April which is 0,4%, and the second smallest minimum SOC value was in December as
6.8%. All the average SOC of each month lied in the range of 45-80% in Electric Mode.
26
6 Discussion
6.1 Driving mode
Driving mode division was the most difficult part when studying the data. The unexpected Mode
Combine, some exceptional occasions were all big troubles when dealing with the data with
Matlab. It was hard to get logical results when using distance as a reference to justify if mode
division was right or not.
It was clear that exceptions happened with Diesel_ERAD_active equal to 1 and
Diesel_ERAD_active equal to 2 with all known results. There were noticeable numbers of time
that engine speed larger than zero while Diesel_ERAD_active equaled to 2, which should not be
logical, since Diesel_ERAD_active equal to 2 means electric mode as defined by the company.
And the magnitude of engine speed was considerable. In this case, those exceptions cannot be
considered as wrongly recorded engine speed, but wrongly recorded operating mode can be an
explanation. However, the recording of operating mode should respond to manually driving
mode shifting by drivers. In this case, the fault of Diesel_ERAD_active mode can be caused by
drivers or by the CAN BUS system.
In the case of both engine speed and battery current was larger than zero, most of it happened
when Diesel_ERAD_active was equal to 1 and only sometimes it happened when
Diesel_ERAD_active equal to 2. But also the magnitude of battery current at
Diesel_ERAD_active equal to 2 was so small that it almost can be neglected. There were only a
few peaks of battery current up to 50 A that occurred at Diesel_ERAD_active equal to 1. In this
case, errors can result from the exclusion of current flow in Mode Combine from Electric Mode.
Talking about the Gird Charge Mode, even though the definition was simple, the situation was
not simple at all. At most of time when drivers charging the car at home, they keep the car
connected to grid until next time they use the car again. As a result, the car was still under
charging after they are fully charged. After the battery was fully charged, the battery will go
through a self-charge and self -discharge process to keep the balance of battery from
overcharging. With the definition of Grid Charge Mode in chapter 5.1.2, those positive battery
flows at self-charging will be counted to grid charge.
6.2 Driving distance
With results shown in chapter 5.2, driving distance varies in each month can be drawn as
conclusion. As mentioned before, according to a report from Vattenfall, the two Volvo V70
prototype was given to 30 different families chosen by Volvo and Vattenfall to drive, and
majority of those families hold the car for 1 month (Östervall 2011). As driving pattern differs
from different families and different family have different needs of driving a car, all those
variations can make sense. Families who used the car in July, August and October are driving
more than other families. But actually, drivers occupied the car for period of time between 1
week and 2 months, which may explain some big fluctuation in Figure 5.2.4 (Östervall 2011).
In the report mentioned before from Vattenfall, it also mentioned about a single trip between
Göteborg and Stockholm was done with a distance of 470km, which can explain the extremely
high travel distance (556 km) at a day in August.
27
6.3 Vehicle speed
In a report from Vattenfall, it is stated that selected drivers for Volvo V70 prototype had claimed
that they chose to drive at electric mode in city traffic and Diesel Mode in highways (Östervall
2011). It corresponds with the result that average vehicle speed at Diesel Mode is generally
higher than at Electric Mode as shown in Figure 5.3.1 in chapter 5.3. Average speed in April was
an exception, where vehicle speed at Electric Mode is slightly larger than vehicle speed at Diesel
Mode. It may be caused by different driving strategy of the driver who drives the car in April
compare with drivers in other months.
It may be easy to explain that maximum vehicle speed at Diesel Mode was larger than that at
Electric Mode, as Diesel Mode was running in highways and Electric Mode was running in city
traffic. Meanwhile diesel engines have a higher power than battery power reference to data shown
in chapter 1 is also a reason. Furthermore, it was claimed that the vehicle speed in Electric Mode
was limited to around 100km/h in the report mentioned above from Vattenfall . In January,
February, March, May, July and December, the vehicle speed are slightly above 100km/h. In
other months, maximum vehicle speeds were all below 100km/h.
6.4 Power and Energy and Carbone dioxide emission
Power reflects the traction force to the car, how fast the vehicle can be accelerated and how high
speed the vehicle can reach. The maximum vehicle speed in each month was quite stable in both
Electric Mode and Diesel Mode, whereas the average vehicle speed in each month varies a lot in
Electric Mode and especially in Diesel Mode. The average speed shows the driving pattern of
drivers in different months, while the maximum speed may speak about the limitation of speed.
Checking the result in chapter 5.4, both average and maximum battery power was quite stable in
each month, while engine power changes a lot with maximum engine power and stays stable with
average engine power in each month. At most of time, engine power lay in a higher than middle
range which is 50 to 120 kW, while batter power lay in a lower range which is 0 to 5kW or 5 to
25kW range. The results coincide with driver's driving preference mentioned before, that
electrical mode is preferred in city traffic while Diesel Mode is preferred on the highway.
As Diesel Mode was under driving for the most of time and Diesel Mode have a general higher
power than Electric Mode, it was clear that there is more energy consumption in Diesel Mode,
but also a large amount of corresponding carbon dioxide emission. The amount of carbon
dioxide emission in Electric Mode was really small. And a considerable amount of emission was
saved by using electrical energy instead of diesel. Moreover, the carbon dioxide emissions in
Electric Mode were the amount of carbon dioxide emitted from the power plant where grid
electricity is produced, the actual local emission of vehicle in Electric Mode was zero.
28
6.5 Battery temperature
According to the temperature distribution figures showing before, the battery temperature is
highly dependent on the season, where season can be translated to outside temperature. The
maximum temperature occurs in summer and minimum temperature occurs in winter.
As mentioned before, battery temperature is essential for battery safety, thermal runaway may
lead to leak, gas venting, fire or explosion of battery. The best temperature for the performance
of Li-ion battery is around 23 ℃ due to a trade-off between performance and safety. The
maximum temperature reached in the study stays in the safe area which is below 45 ℃. The credit
can be given to the cool climate of Sweden in summer and the cooling system installed on the
battery. On the other side, Sweden is cold in winter, while Li-Ion battery has poor performance
at cold temperatures. The minimum temperature in the study was -6 ℃ in February, while most
lithium-ion batteries cannot be charged safely below 0℃ (PowerStream 2013).
Moreover, there was some collected battery temperature that was unreasonably large, like 80 oC,
while other temperature collected before or after was all below 30 oC. Thus ranges that refer to
other temperature data such as AmbientTemp, Btemp_max, Btemp_min, InCar-temp were
applied to classifying results of temperature and eliminate those unreasonable ones .
6.6 SOC
The average SOC in the Electric Mode is generally higher than that in the Diesel Mode which can
be seen from Figure 5.7.2 and Figure 5.7.3. But also the minimum in the Electric Mode was at
29.6%, while the SOC at Diesel Mode are all smaller than 29.6% and varies in each month. It
may imply that the battery- only mode stops at 29.6% of SOC in the applied PHEV strategy. And
the driving after battery reach 29.6% of SOC, diesel engine will start to run with engine power.
This status may explain the Mode Combine that both current flow and engine speed were active
as mentioned before.
29
7 Conclusion
As the data was collected from a field test, wrongly collected data and logically unexplainable
situations occurred. The faultiness of collected data contributed to the difficulty and uncertainty
of mode defining. For this reason, the division into modes such as Diesel Mode, Electric Mode,
Battery Regenerative Mode and Grid Charge Mode might be inaccurate to some extent.
Driving distance, vehicle speed, power, energy, carbon dioxide emission, battery temperature and
SOC were examined in this thesis. From the result one can conclude that the average, maximum
and total driving range varied from month to month over a year probably due to the changing
drivers. But also, the daily driving range in each month was different. Moreover, longer distance
was travelled with Diesel Mode, and the average and maximum vehicle speed in Diesel Mode was
higher than that in Electric Mode. With respect to vehicle speed, the average speed varied a lot in
different months, while the maximum vehicle speed was similar. This fact can lead to the
conclusion that different testing drivers had different preferences of driving speed. The
conclusions drawn from the vehicle speed part can also be applied to the result of battery and
engine power. The engine power and battery power varies between different months. As to more
distance was driven in Diesel Mode and average engine power was larger than battery power,
much more diesel energy was consumed in the internal combustion engine than electrical energy
in battery. During the driving a considerable number of 357 kWh of energy was regenerated from
regenerative braking over the year. By using electric energy in Electric Mode other than diesel
energy, 370 kg of CO 2 was saved over the year. The battery temperature was highly dependent on
the temperature of environment. From the result the performance of battery at low temperature
is concerned. With results from SOC part, one can tell that the PHEV strategy was settled to
keep the SOC from nearly 100 to 30%, the vehicle was switched to Diesel Mode whenever SOC
was lower than 30%.
Future studies might focus on battery safety in the temperature region of the field test, the
necessity of a heating/cooling system and PHEV strategy with combined mode parallel hybrid.
30
Acknowledgments
This thesis is presented in part completion of the degree of Master in Sustainable Energy
Engineering in the department of Energy Technology at Royal Institute of Technology (KTH).
This master thesis performed with Plug-in Hybrid Electric Vehicle as topic with Verena Klass
work in Tillämpad Electrokemi as supervisor and Prof. Andrew Martin in the Department of
Energy Technology as examiner.
Thanks to Verena for helping me perform this thesis with patient and kindness. Andrew for
sparing his time for my presentation. And also thanks to those people who help me with the
work during the time I stayed in the department.
Lin Peng
31
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Penetration into the Transportation Sector', (IEEE Int. Symp. on Technology and Society (ISTAS)), 1-6.
English, Andrew (2009), 'Volvo V70 plug-in hybrid review',
<http://www.telegraph.co.uk/motoring/6438513/Volvo-V70-plug-in-hybrid-review.html>,
2013/04/15.
accessed
EPRI, NRDC (2007), '“Environmental Assessment of Plug-In Hybrid Electric Vehicles. Volume 1:
Nationwide Greenhouse Gas Emissions”,'.
ETC, Volvo, Vattenfall, (2010) ETC Battery and FuelCells Sweden AB, Vattenfall AB, Volvo
Technology AB, Volvo Personvagnar AB, Plug-in hybrid project PHEV financed by the Swedish
Energy Agency under project number 31097-1 until 2010-12-31 (2010).
IEA (2009), 'Technology Roadmap', Electric and plug-in hybrid electric vehicles(EV/PHEV)
(International Energy Agency),
http://www.iea.org/publications/freepublications/publication/EV_PHEV_Roadmap.pdf
IEA (2012), 'Key World Energy Statistics', (International Energy Agency),
http://www.iea.org/publications/freepublications/publication/kwes.pdf
Klass, V., Behm, M., and Lindbergh, G. (2012), 'Evaluating Real-Life Performance of Lithium-Ion
Battery Packs in Electric Vehicles', Journal of the Electrochemical Society, 159 (11), A1856-A60.
Melody, Baglione (2007), ' Development of System Analysis Methodologies and Tools for Modeling and
Optimizing Vehicle System Efficiency', (University of Michigan).
Merriam-Webster (2009), 'battery(def.4b)', in wiki (ed.), Merriam-Webster Online Dictionary.
Mi, Chris, Masrur, Abul, and Gao, David Wenzhong (2011), Hybrid electric vehicles principles and
applications with practical perspectives, eds Chris Mi, Abul Masrur, and David Wenzhong Gao
(Chichester, West Sussex, U.K. ; Hoboken, N.J.: Wiley,).
Nicolas Dehlinger, Micha?l Desjardins, Maxime Dubois, Jean Longchamps, Louis
Tremblay,Philippe Bélanger, Micha?l Bourdeau-Brien, James Eaves, Michel Gendron (2009),
'Plug-In Hybrid Electric Vehicle (PHEV) Québec Test Program: A Major Real-World Test Study on
Financial, Technological and Social Aspects of PHEVs', (Université Laval, Pavillon Palas is-Prince,
32
G1K7P4, Québec, QC, Canada: Laboratoire d’électrotechnique, d’électronique de Puissance et de
Commande Industrielle (LEEPCI),Département de finance et d’assurance,Université Laval).
Ö stervall, Sara-Linnéa (2011), 'Driving a plug-in hybrid:Results from user tests of Volvo V70 plug-in
hybrids in 2010', (Vattenfall Power Consultant AB).
PowerStream (2013), 'Lithium-ion battery and lithium iron phosphate battery charging basics', Lithium
ion battery charging, http://www.powerstream.com/li.htm, accessed 2013/06/13
Sousanis, John (2011), 'World Vehicle Population Tops 1 Billion Units',
http://wardsauto.com/ar/world_vehicle_population_110815, accessed 2013/05/10
Team, MIT Electric Vehicle (2008), 'A Guide to understanding battery specifications'.
<http://mit.edu/evt/summary_battery_specifications.pdf>, accessed 2013/04/15.
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sources 168, 459 – 68.
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< http://www.che.sc.edu/faculty/popov/drnp/WebSite/MSA-calendar.pdf>, accessed 2013/06/23
33
Appendix A:M-script for days with 5th of Jan. 2010
as example
clear all
clc
WPA642_20100105files=dir('WPA642_20100105*.mat'); %finds all files in this
folder that begin with WPA642
numWPA642_20100105files=length(WPA642_20100105files);%counting how many
files
%--------------------load all WPA642-files for one day------------------for k=1:numWPA642_20100105files %for-loop to go through all files in a
month
WPA642_20100105filename=WPA642_20100105files(k).name;
data(k)=load(WPA642_20100105filename,'Bat_Volt','VehicleSpeed','All__Time',
...
'All__Time_abs','ACCM_Active','AC_Curr','AmbientTemp','BTemp_av','Bat_Curr'
...
,'Btemp_max','Btemp_min','CV_Max_0','CV_Max_1','CV_Max_2','CV_Max_3',...
'CV_Max_4','CV_Max_5','CV_Max_6','CV_Max_7','CV_Min_0','CV_Min_1','CV_Min_2
',...
'CV_Min_3','CV_Min_4','CV_Min_5','CV_Min_6','CV_Min_7','DCDC_Current',...
'DCDC_Voltage','DOD','Diesel_ERAD_active','ERAD_Motor_Speed','EngineSpeed',
...
'GearLeverPosition','I_Line','InCarTemp','Line_connected','Max_CV','Min_CV'
,...
'Max_Temp_0','Max_Temp_1','Max_Temp_2','Max_Temp_3','Max_Temp_4',...
'Max_Temp_5','Max_Temp_6','Max_Temp_7','Min_Temp_0','Min_Temp_1',...
'Min_Temp_2','Min_Temp_3','Min_Temp_4','Min_Temp_5','Min_Temp_6',...
'Min_Temp_7','Motor_Temperature','OdometerMasterValue','PEU_Status',...
'PEU_Temperature','Start_date','Start_time','Stop_date','Stop_time',...
'V_Line');%variable names that are supposed to be loaded
end
%==============================load data==================================%
VehicleID='WPA642';
OdometerMasterValue=[data.OdometerMasterValue];
All__Time=[data.All__Time];
All__Time_abs=[data.All__Time_abs];
Start_date=[data.Start_date]; Start_time=[data.Start_time];
Stop_date=[data.Stop_date]; Stop_time=[data.Stop_time];
OdometerMasterValue=[data.OdometerMasterValue];%accumulated distance by
odometer on car
%total absolute time
Age_start=All__Time_abs(1);
Age_end=All__Time_abs(end);
%Time in
trip_filename(k)=cellstr(WPA642_20100105filename);%
%---Vechile-------%
VehicleSpeed=[data.VehicleSpeed];
ACCM_Active=[data.ACCM_Active];
AC_Curr=[data.AC_Curr];
Diesel_ERAD_active=[data.Diesel_ERAD_active];
ERAD_Motor_Speed=[data.ERAD_Motor_Speed];
EngineSpeed=[data.EngineSpeed];
GearLeverPosition=[data.GearLeverPosition];
PEU_Status=[data.PEU_Status];%petrolem energy useage
%----charging line-----%
I_Line=[data.I_Line];%current from charging point ,line current during
charge'
V_Line=[data.V_Line];%Voltage from charging point
34
%----Battery------%
Bat_Volt=[data.Bat_Volt];
Bat_Curr=[data.Bat_Curr];
DOD=[data.DOD];
Max_CV=[data.Max_CV];
Min_CV=[data.Min_CV];
CV_Max_0=[data.CV_Max_0]; CV_Max_1=[data.CV_Max_1];
CV_Max_2=[data.CV_Max_2]; CV_Max_3=[data.CV_Max_3];
CV_Max_4=[data.CV_Max_4]; CV_Max_5=[data.CV_Max_5];
CV_Max_7=[data.CV_Max_7];
CV_Min_0=[data.CV_Min_0]; CV_Min_2=[data.CV_Min_2];
CV_Min_3=[data.CV_Min_3]; CV_Min_4=[data.CV_Min_4];
CV_Min_5=[data.CV_Min_5]; CV_Min_6=[data.CV_Min_6];
CV_Min_7=[data.CV_Min_7];
%----Temperature----%
AmbientTemp=[data.AmbientTemp];
PEU_Temperature=[data.PEU_Temperature];
Motor_Temperature=[data.Motor_Temperature];
BTemp_av=[data.BTemp_av];
Btemp_max=[data.Btemp_max];
Btemp_min=[data.Btemp_min];
InCarTemp=[data.InCarTemp];
Max_Temp_0=[data.Max_Temp_0]; Max_Temp_1=[data.Max_Temp_1];
Max_Temp_2=[data.Max_Temp_2]; Max_Temp_3=[data.Max_Temp_3];
Max_Temp_4=[data.Max_Temp_4]; Max_Temp_5=[data.Max_Temp_5];
CV_Max_6=[data.CV_Max_6]; CV_Max_7=[data.CV_Max_7];
Min_Temp_0=[data.Min_Temp_0];
Min_Temp_1=[data.Min_Temp_1];Min_Temp_2=[data.Min_Temp_2];
Min_Temp_3=[data.Min_Temp_3]; Min_Temp_4=[data.Min_Temp_4];
Min_Temp_5=[data.Min_Temp_5]; Min_Temp_6=[data.Min_Temp_6];
Min_Temp_7=[data.Min_Temp_7];
%-----parameters may not be useful------%
DCDC_Current=[data.DCDC_Current];
DCDC_Voltage=[data.DCDC_Voltage];
Line_connected=[data.Line_connected]; % what is this?avaliable current?
%==========================================================================
%
%===================define different mode=================================%
%*************************Grid charge*************************************%
charge_grid=find(Bat_Curr<=0&I_Line>=0&Diesel_ERAD_active==0);
%****************************regen*****************************************
%
%All regen_whenv0=0, all regen at vehicle speed >0
%where does those enginespeed==0 happens need to be find out
mode_regen_diesel_010=find(Bat_Curr<0&EngineSpeed==0&Diesel_ERAD_active==1)
;%happens when slowing down
mode_regen_diesel_01=find(Bat_Curr<0&EngineSpeed>0&Diesel_ERAD_active==1);%
slowing down and accelerate
mode_regen_diesel_02=find(Bat_Curr<0&EngineSpeed>0&Diesel_ERAD_active==2);%
only day 13th jan have
mode_regen_diesel=union(mode_regen_diesel_01,mode_regen_diesel_02);
mode_regen_diesel_all=union(mode_regen_diesel,mode_regen_diesel_010);
mode_regen_bat=find(Bat_Curr<0&EngineSpeed==0&Diesel_ERAD_active==2);%regen
with engine off at battery mode, and combine only happens in mode 1
mode_regen=union(mode_regen_diesel_all,mode_regen_bat);
%regen4=find(Bat_Curr<0&EngineSpeed==0&Diesel_ERAD_active==0)=0
35
%*****************************combine**************************************
%
%diesel_mode_combien_run=Diesel_ERAD_active(EngineSpeed>0&Bat_Curr>0);
%mostly only when Diesel_ERAD_active=1, but only in file 13th also =2,
mode_combine_01_Run=find(EngineSpeed>0&Bat_Curr>0&VehicleSpeed>0&Diesel_ERA
D_active==1);
mode_combine_02_Run=find(EngineSpeed>0&Bat_Curr>0&VehicleSpeed>0&Diesel_ERA
D_active==2);%why does this happen need to find out
mode_combine_01=find(EngineSpeed>0&Bat_Curr>0&Diesel_ERAD_active==1);
mode_combine_02=find(EngineSpeed>0&Bat_Curr>0&Diesel_ERAD_active==2);
mode_combine_01_stay=setdiff(mode_combine_01,mode_combine_01_Run);
mode_combine_02_stay=setdiff(mode_combine_02,mode_combine_02_Run);
mode_combine=union(mode_combine_01,mode_combine_02);
clear mode_combine_01_Run mode_combine_02_Run mode_combine_01_stay
mode_combine_02_stay
%***************************Diesel only***********************************%
mode_dieselonly_run=find(EngineSpeed>=0&Diesel_ERAD_active==1&VehicleSpeed>
0&Bat_Curr==0);
mode_dieselonly_stay=find(EngineSpeed>=0&Diesel_ERAD_active==1&VehicleSpeed
==0&Bat_Curr==0);
mode_dieselonly=find(EngineSpeed>=0&Diesel_ERAD_active==1&Bat_Curr==0);
mode_dieselonly_special=find(Diesel_ERAD_active==2&Bat_Curr==0&EngineSpeed>
0);%check where
mode_dieselonly=union(mode_dieselonly,mode_dieselonly_special);
clear mode_dieselonly_run mode_dieselonly_stay
%***************************Battey only***********************************%
%disbat_only_run=find(Bat_Curr>0&EngineSpeed==0&VehicleSpeed>0);
mode_disbat_only_run=find(Bat_Curr>=0&EngineSpeed==0&VehicleSpeed>0&Diesel_
ERAD_active==2);
mode_disbat_only=find(Bat_Curr>=0&EngineSpeed==0&Diesel_ERAD_active==2);
clear mode_disbat_only_run
%**********************'****discharge*************************************%
mode_discharge=mode_disbat_only;
clear mode_disbat_only
%**********************'****disiel*************************************%
mode_diesel_01=union(mode_regen_diesel_all,mode_combine);
mode_diesel=union(mode_diesel_01,mode_dieselonly);
clear mode_diesel_01 mode_regen_diesel mode_regen_diesel_all
%======================Daliy calcuation===================================%
%************************distance*****************************************%
daliy_distance=cumtrapz(All__Time_abs,VehicleSpeed2)/2/3.6/1000;%km
%check if daliy distance was right
daliy_odometer=max(OdometerMasterValue(~isnan(OdometerMasterValue)))...
-min(OdometerMasterValue(~isnan(OdometerMasterValue)));%km
%***********************Energy********************************************%
%Battery power%
P_Bat=Bat_Curr.*Bat_Volt/1000;%kW
%Toque data from http://www.popularmechanics.com/cars/reviews/hybridelectric/4334201%
Toque_Engine=448.77574089723; %331lbf-ft convernt to448.77574089723NM
Toque_Motor=219.64250762946; %162lbf-ft convert to 219.64250762946NM
%Engine power%
P_En=EngineSpeed*Toque_Engine/5252;%Engine theoretical power,hp
%Motor power%
36
P_M=Toque_Motor*ERAD_Motor_Speed/5252;%Motor theoretical power,hp
%------------------------------------------------------------------------%
daliy_energy_bat_regen=trapz(P_Bat(mode_regen))/2/3600;%kWh
daliy_energy_bat_dischage=trapz(P_Bat(mode_discharge))/2/3600;%kWh
daliy_energy_en=trapz(P_En/0.746)/2/3600;%kWh 1hp=0.746kw
daliy_energy_GridCharge=trapz(P_Bat(charge_grid));
%-------------------------------------------------------------------------save('C:\Users\01\WPA642_20100105.mat');
37
Appendix B:M-script for months with Jan. 2010 as
example
clear all
close all
WPA642files=dir('WPA642*.mat'); %find all files in the directory----------numWPA642files=length(WPA642files);
for k=1:numWPA642files %for-loop to go through all files in a month
WPA642filename=WPA642files(k).name;
data(k)=load(WPA642filename,'Bat_Volt','VehicleSpeed','All__Time',...
'All__Time_abs','AmbientTemp','BTemp_av','Bat_Curr','Btemp_max','Btemp_min'
,...
'DOD','Diesel_ERAD_active','ERAD_Motor_Speed','EngineSpeed','I_Line',...
'InCarTemp','Line_connected','Max_CV','Min_CV','Motor_Temperature',...
'OdometerMasterValue','PEU_Temperature','Start_date','Start_time','Stop_dat
e','Stop_time',...
'V_Line','P_Bat','daliy_distance','daliy_Ah_batgrid','daliy_Ah_discharge','
daliy_Ah_regeneration',...
'daliy_energy_bat_regen','daliy_Curr_batdis','daliy_energy_en','daliy_energ
y_bat_dischage','daliy_energy_GridCharge');
end
VehicleID='WPA642';
All__Time_abs=[data.All__Time_abs];
Start_date=[data.Start_date]; Start_time=[data.Start_time];
Stop_date=[data.Stop_date]; Stop_time=[data.Stop_time];
OdometerMasterValue=[data.OdometerMasterValue];%accumulated distance by
odometer on car
%total absolute time
Age_start=All__Time_abs(1);
Age_end=All__Time_abs(end);
%---Vechile-------%
VehicleSpeed=[data.VehicleSpeed];
ERAD_Motor_Speed=[data.ERAD_Motor_Speed];
EngineSpeed=[data.EngineSpeed];
ERAD_Motor_Speed=[data.ERAD_Motor_Speed];
Diesel_ERAD_active=[data.Diesel_ERAD_active];
%----charging line-----%
I_Line=[data.I_Line];%current from charging point ,line current during
charge'
V_Line=[data.V_Line];%Voltage from charging point
%----Battery------%
Bat_Volt=[data.Bat_Volt];
Bat_Curr=[data.Bat_Curr];
DOD=[data.DOD];
Max_CV=[data.Max_CV];
Min_CV=[data.Min_CV];
BTemp_av=[data.BTemp_av];
Btemp_max=[data.Btemp_max];
Btemp_min=[data.Btemp_min];
AmbientTemp=[data.AmbientTemp];
Motor_Temperature=[data.Motor_Temperature];
InCarTemp=[data.InCarTemp];
%===================define different mode=================================%
%*************************Grid charge*************************************%
charge_grid=find(Bat_Curr<=0&I_Line>=0&Diesel_ERAD_active==0);
38
%****************************regen*****************************************
%
%All regen_whenv0=0, all regen at vehicle speed >0
%where does those enginespeed==0 happens need to be find out
mode_regen_diesel_010=find(Bat_Curr<0&EngineSpeed==0&Diesel_ERAD_active==1)
;%happens when slowing down
mode_regen_diesel_01=find(Bat_Curr<0&EngineSpeed>0&Diesel_ERAD_active==1);%
slowing down and accelerate
mode_regen_diesel_02=find(Bat_Curr<0&EngineSpeed>0&Diesel_ERAD_active==2);%
only day 13th jan have
mode_regen_diesel=union(mode_regen_diesel_01,mode_regen_diesel_02);
mode_regen_diesel_all=union(mode_regen_diesel,mode_regen_diesel_010);
mode_regen_bat=find(Bat_Curr<0&EngineSpeed==0&Diesel_ERAD_active==2);%regen
with engine off at battery mode, and combine only happens in mode 1
mode_regen=union(mode_regen_diesel_all,mode_regen_bat);
%regen4=find(Bat_Curr<0&EngineSpeed==0&Diesel_ERAD_active==0)=0
%*****************************combine**************************************
%
%diesel_mode_combien_run=Diesel_ERAD_active(EngineSpeed>0&Bat_Curr>0);
%mostly only when Diesel_ERAD_active=1, but only in file 13th also =2,
mode_combine_01_Run=find(EngineSpeed>0&Bat_Curr>0&VehicleSpeed>0&Diesel_ERA
D_active==1);
mode_combine_02_Run=find(EngineSpeed>0&Bat_Curr>0&VehicleSpeed>0&Diesel_ERA
D_active==2);%why does this happen need to find out
mode_combine_01=find(EngineSpeed>0&Bat_Curr>0&Diesel_ERAD_active==1);
mode_combine_02=find(EngineSpeed>0&Bat_Curr>0&Diesel_ERAD_active==2);
mode_combine_01_stay=setdiff(mode_combine_01,mode_combine_01_Run);
mode_combine_02_stay=setdiff(mode_combine_02,mode_combine_02_Run);
mode_combine=union(mode_combine_01,mode_combine_02);
clear mode_combine_01_Run mode_combine_02_Run mode_combine_01_stay
mode_combine_02_stay mode_combine_01 mode_combine_02
%***************************Diesel only***********************************%
mode_dieselonly_run=find(EngineSpeed>=0&Diesel_ERAD_active==1&VehicleSpeed>
0&Bat_Curr==0);
mode_dieselonly_stay=find(EngineSpeed>=0&Diesel_ERAD_active==1&VehicleSpeed
==0&Bat_Curr==0);
mode_dieselonly=find(EngineSpeed>=0&Diesel_ERAD_active==1&Bat_Curr==0);
mode_dieselonly_special=find(Diesel_ERAD_active==2&Bat_Curr==0&EngineSpeed>
0);%check where
mode_dieselonly=union(mode_dieselonly,mode_dieselonly_special);
clear mode_dieselonly_run mode_dieselonly_stay
%***************************Battey only***********************************%
%disbat_only_run=find(Bat_Curr>0&EngineSpeed==0&VehicleSpeed>0);
mode_disbat_only_run=find(Bat_Curr>=0&EngineSpeed==0&VehicleSpeed>0&Diesel_
ERAD_active==2);
mode_disbat_only=find(Bat_Curr>=0&EngineSpeed==0&Diesel_ERAD_active==2);
clear mode_disbat_only_run
%**********************'****discharge*************************************%
mode_discharge=mode_disbat_only;
clear mode_disbat_only
%**********************'****disiel*************************************%
mode_diesel_01=union(mode_regen_diesel,mode_combine);
mode_diesel=union(mode_diesel_01,mode_dieselonly);
clear mode_diesel_01 mode_regen_diesel mode_regen_diesel_all
mode_regen_diesel_010 mode_regen_diesel_01 mode_regen_diesel_02
39
%************************Vehicle
Speed*****************************************%
VehicleSpeed_p=VehicleSpeed(VehicleSpeed>0);
mounth_VehicleSpeed_av=nanmean(VehicleSpeed_p);
mounth_VehicleSpeed_max=nanmax(VehicleSpeed_p);
mounth_VehicleSpeed_std=nanstd(VehicleSpeed_p);
clear VehicleSpeed_p
%Battery power%
P_Bat=Bat_Curr.*Bat_Volt/1000;%kW
P_bat_p=P_Bat(mode_discharge);%since use p>0 will contain a lot balance
part, average will be lowered
P_bat_p=P_bat_p(P_bat_p>0);
mounth_P_Bat_av=nanmean(P_bat_p);%for Daliy average power distribution
mounth_P_Bat_max=nanmax(P_bat_p);%for Daliy max power distribution
mounth_P_Bat_std=nanstd(P_bat_p);
%for Daliy stander power distribution
%Toque data from http://www.popularmechanics.com/cars/reviews/hybridelectric/4334201%
Toque_Engine=448.77574089723; %331lbf-ft convernt to448.77574089723NM
Toque_Motor=219.64250762946; %162lbf-ft convert to 219.64250762946NM
%Engine power%
P_En=EngineSpeed*Toque_Engine/5252*0.745699872;%Engine theoretical power,kw
P_En_p=P_En(P_En>0);
mounth_P_En_av=nanmean(P_En_p);
mounth_P_En_max=nanmax(P_En_p);
mounth_P_En_std=nanstd(P_En_p);
%Motor power%
P_M=Toque_Motor*ERAD_Motor_Speed/5252*0.745699872;%Motor theoretical
power,kw
P_M_p=P_En(P_M>0);
mounth_P_M_av=nanmean(P_M_p);
mounth_P_M_max=nanmax(P_M_p);
mounth_P_M_std=nanstd(P_M_p);
clear P_M_p
% %----------------Temperature-------------------------------------------%
%---------battery temperature------%
mounth_T_bat_max=nanmax(Btemp_max(Btemp_max<=50));
mounth_T_bat_min=nanmin(Btemp_min(Btemp_min>=-20));
T_bat_av=BTemp_av(mounth_T_bat_min<=BTemp_av&BTemp_av<=mounth_T_bat_max);
mounth_T_bat_av_av=nanmean(T_bat_av);
mounth_T_bat_av_max=nanmax(T_bat_av);
mounth_T_bat_av_min=nanmin(T_bat_av);
mounth_T_bat_av_std=nanstd(T_bat_av);
% %Ambient Temperature%
T_am=AmbientTemp(AmbientTemp>=-20&AmbientTemp<=40);
mounth_T_am_av=nanmean(T_am);
mounth_T_am_max=nanmax(T_am);
mounth_T_am_min=nanmin(T_am);
mounth_T_am_std=nanstd(T_am);
% %Motor_Temperature%
T_M=Motor_Temperature(Motor_Temperature>=0&Motor_Temperature<=150);
mounth_T_M_av=nanmean(T_M);
mounth_T_M_max=nanmax(T_M);
mounth_T_M_min=nanmin(T_M);
mounth_T_M_std=nanstd(T_M);
% %InCarTemp
T_incar=InCarTemp(InCarTemp>=-20&Motor_Temperature<=40);
40
mounth_T_incar_av=nanmean(T_incar);
mounth_T_incar_max=nanmax(T_incar);
mounth_T_incar_min=nanmin(T_incar);
mounth_T_incar_std=nanstd(T_incar);
%---------------------------------SOC------------------------------------%
SOC=100-DOD(DOD<100);
mounth_SOC_max=nanmax(SOC);
mounth_SOC_av=nanmean(SOC(SOC<mounth_SOC_max));
mounth_SOC_min=nanmin(SOC);
mounth_SOC_std=nanstd(SOC);
%--------------------------C-rate in h-1----------------------------------%
curr_crate=Bat_Curr./32; %32 Ah pack capacity -> C-rate in h-1
mounth_curr_crate_av=nanmean(curr_crate);
mounth_curr_crate_min=nanmin(curr_crate);
mounth_curr_crate_std=nanstd(curr_crate);
%-------------------distance------------------------------------------%
VehicleSpeed2=VehicleSpeed(~isnan(VehicleSpeed));
mounth_distance=trapz(VehicleSpeed2)/2/3600;
VehicleSpeed_p=VehicleSpeed(VehicleSpeed>0);%remove influnce of
vechiclespeed=0%
VehicleSpeed_bat=VehicleSpee_p(mode_discharge);
VehicleSpeed_diesel=VehicleSpeed(mode_diesel);
VehicleSpeed_regen=VehicleSpeed(mode_regen);
%----average vehicle speed at different mode--------%
VehicleSpeed_bat_av=mean(VehicleSpeed_bat);
VehicleSpeed_diesel_av=mean(VehicleSpeed_diesel);
VehicleSpeed_regen_av=mean(VehicleSpeed_regen);
%----distance runned under different mode--------%
distance_bat=trapz(VehicleSpeed_bat)/2/3.6/1000;
distance_diesel=trapz(VehicleSpeed_diesel)/2/3.6/1000;
distance_regen=trapz(VehicleSpeed_regen)/2/3.6/1000;;
% %------------------Grid Charge-----%
P_GridCharge=P_Bat(charge_grid);
mounth_energy_GridCharge=trapz(P_GridCharge/3600);%kWh
%-------------------dischargre----------%
P_bat_dischage=P_Bat(mode_discharge);
mounth_energy_bat_dischage=trapz(P_bat_dischage/3600);%kWh
%------------------deisel----------------%
nan_P_En=P_En(~isnan(P_En));
mounth_energy_en=trapz(nan_P_En/3600);%kWh
%-----------------------regen-----------------%
P_bat_regen=P_Bat(mode_regen);
mounth_energy_bat_regen=trapz(P_bat_regen/3600);%kWh
%-------------------------Ampere hour-------------------------------------%
%Discharge%
Curr_batdis=Bat_Curr((mode_discharge));
mounth_Ah_discharge=trapz(Curr_batdis/3600);%Ah
%Battery grid%
Curr_batgrid=Bat_Curr(charge_grid);
mounth_Ah_batgrid=trapz(Curr_batgrid/3600);%Ah
%regeneration%
Curr_batregen=Bat_Curr(mode_regen);
mounth_Ah_regeneration=trapz(Curr_batregen/3600);%Ah
%-------------------------------------------------------------------------%
%=========================distance stastistics============================%
daliy_distance=[data.daliy_distance];
mounth_distance_av=nanmean(daliy_distance);
mounth_distance_max=nanmax(daliy_distance);
mounth_distance_min=nanmin((daliy_distance(daliy_distance>0)));
mounth_distance_std=nanstd(daliy_distance);
41
%----------------energy--------------------------------%
daliy_energy_en=[data.daliy_energy_en];
daliy_energy_bat_dischage=[data.daliy_energy_bat_dischage];
daliy_energy_GridCharge=[data.daliy_energy_GridCharge];
monthly_erergy_diesel=sum(daliy_energy_en);
monthly_erergy_disbat=sum(daliy_energy_bat_dischage);
monthly_erergy_GridCharge=sum(daliy_energy_GridCharge);
daliy_energy_bat_regen=[daliy_energy_bat_regen];
monthly_erergy_regen=P_Bat(mode_regen);
clear P_bat_regen
DOD_op=DOD(VehicleSpeed>=0);
SOC_op=100-(DOD_op(DOD_op<100));
SOC_op_av=nanmean(SOC_op);
SOC_op_max=nanmax(SOC_op);
SOC_op_min=nanmin(SOC_op);
SOC_op_std=nanstd(SOC_op);
DOD_bt=DOD(mode_discharge);
SOC_bt=100-(DOD_bt(DOD_bt<100));
SOC_bt_av=nanmean(SOC_bt);
SOC_bt_max=nanmax(SOC_bt);
SOC_bt_min=nanmin(SOC_bt);
SOC_bt_std=nanstd(SOC_bt);
DOD_desile=DOD(mode_diesel);
SOC_desile=100-(DOD_desile(DOD_desile<100));
SOC_desile_av=nanmean(SOC_desile);
SOC_desile_max=nanmax(SOC_desile);
SOC_desile_min=nanmin(SOC_desile);
SOC_desile_std=nanstd(SOC_desile);
%--------------distributions------------------------%
distance_L_1=find(daliy_distance <=10);
distance_L_2=find(daliy_distance >10& daliy_distance <=40);
distance_L_3=find(daliy_distance >40& daliy_distance 80);
distance_L_4=find(daliy_distance >80);
distance_L_ALL=length(distance_L_1)+length(distance_L_2)+length(distance
_L_3)+length(distance_L_4);
distance_L_1=length(distance_L_1)/( distance_L_ALL);
distance_L_2=length(distance_L_2)/( distance_L_ALL);
distance_L_3=length(distance_L_3)/( distance_L_ALL);
distance_L_4=length(distance_L_4)/( distance_L_ALL);
%-------Engine---------%
p_En_L_1=find(P_En_p<=50);
p_En_L_2=find(P_En_p>50&P_En_p<=90);
p_En_L_3=find(P_En_p>90&P_En_p<=120);
p_En_L_4=find(P_En_p>120);
p_En_L_ALL=length(p_En_L_1)+length(p_En_L_2)+length(p_En_L_3)+length(p_En_L
_4);
p_En_L_1=length(p_En_L_1)/(p_En_L_ALL);
p_En_L_2=length(p_En_L_2)/(p_En_L_ALL);
p_En_L_3=length(p_En_L_3)/(p_En_L_ALL);
p_En_L_4=length(p_En_L_4)/(p_En_L_ALL);
%-------Battery-------%
42
p_Bat_L_1=find(P_bat_p<=5);
p_Bat_L_2=find(P_bat_p>5&P_bat_p<25);
p_Bat_L_3=find(P_bat_p>25&P_bat_p<=45);
p_Bat_L_4=find(P_bat_p>45);
p_Bat_L_ALL=length(p_Bat_L_1)+length(p_Bat_L_2)+length(p_Bat_L_3)+length(p_
Bat_L_4);
p_Bat_L_1=length(p_Bat_L_1)/(p_Bat_L_ALL);
p_Bat_L_2=length(p_Bat_L_2)/(p_Bat_L_ALL);
p_Bat_L_3=length(p_Bat_L_3)/(p_Bat_L_ALL);
p_Bat_L_4=length(p_Bat_L_4)/(p_Bat_L_ALL);
%-----------temperature--------------%
T_bat_L_1=find(T_bat_av<=5);
T_bat_L_2=find(T_bat_av>5& T_bat_av<=15);
T_bat_L_3=find(T_bat_av>15& T_bat_av<25);
T_bat_L_4=find(T_bat_av>25);
T_bat_L_ALL=length(T_bat_L_1)+length(T_bat_L_2)+length(T_bat_L_3)+length(T_
bat_L_4);
T_bat_L_1=length(T_bat_L_1)/( T_bat_L_ALL);
T_bat_L_2=length(T_bat_L_2)/( T_bat_L_ALL);
T_bat_L_3=length(T_bat_L_3)/( T_bat_L_ALL);
T_bat_L_4=length(T_bat_L_4)/( T_bat_L_ALL);
%--------------max and mean velocity-----------------%
vehicleSpeed_diesel=VehicleSpeed(mode_diesel);
vehicleSpeed_bat=VehicleSpeed(mode_discharge);
vehicleSpeed_diesel_p=vehicleSpeed_diesel(vehicleSpeed_diesel>0);
vehicleSpeed_bat_p=vehicleSpeed_bat(vehicleSpeed_bat>0);
vehicleSpeed_diesel_av=nanmean(vehicleSpeed_diesel_p);
vehicleSpeed_diesel_max=nanmax(vehicleSpeed_diesel_p);
vehicleSpeed_bat_av=nanmean(vehicleSpeed_bat_p);
vehicleSpeed_bat_max=nanmax(vehicleSpeed_bat_p);
save('C:\Users\01.mat');
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44
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