International Journal of Engineering Trends and Technology (IJETT) – Volume 16 Number 1 – Oct 2014
Efficient Estimation of Fuel Consumption Using
Fuzzy Logic
Latha P S1, Menaga N2, Manjunath T K3
1,2
Asst. Prof.
Dept. Of Computer Science And Engineering
Mahendra College Of Engineering,Anna University,Tamil nadu ,India
Assoc. Prof.
3
Dept. Of Computer Science And Engineering
AIeMS,VTU,Bangalore,India.
ABSTRACT- Fuzzy logic is a rule based system which is
implemented using if then rules. For example two parameters say x
& y are varied in order to know which parameter effects the fuel
efficiency. This paper explores in particular the use of fuzzy logic in
the Efficient estimation of fuel consumption. We present an
intelligent Engine Control Unit (ECU) that controls a series of
actuators on an internal combustion engine to ensure optimal engine
performance. Here, PCA is used to reduce the data collected using
reduction algorithm where Principal Component Analysis (PCA) is
the best, in the mean-square error sense, linear dimension reduction
technique. It is a statistical procedure that uses orthogonal
transformation to convert a set of observations of possibly correlated
variables into a set of values of linearly uncorrelated variables
called principal components. Hence, The fuel efficiency of the
vehicle and the efficiency of the driver can be calculated using the
sensor data available in the vehicle.
Key words: ECU, CAN bus , PCA ,fuzzy logic.
I. INTRODUCTION
An Engine Control Unit (ECU), now called the
Power train Control Module (PCM), is a type of electronic
control unit that controls a series of actuators on an internal
combustion engine to ensure optimal engine performance. It
does this by reading values from a multitude of sensors within
the engine bay, interpreting the data using multidimensional
performance maps (called lookup tables), and adjusting the
engine actuators accordingly.
CAN bus (for controller area network) is a vehicle bus
standard designed to allow microcontrollers and devices to
communicate with each other within a vehicle without a host
computer. An on-board instrumentation capable to
communicate with the electronic system of the vehicle
(OBD/CAN) have been developed to collect all the sensor
data available and use them as input for power and
consumption models.
Here is the proposed reduction Optimization algorithms that
either require the data to be sphered, or they converge better
for sphere data. Sphering is a linear transformation that maps
x into a new variable v with unit covariance matrix:
Here we are using Fuzzy logic which is a form of manyvalued logic; it deals with reasoning that is approximate rather
than fixed and exact. Compared to traditional binary sets
ISSN: 2231-5381
(where variables may take on true or false values), fuzzy logic
variables may have a truth value that ranges in degree between
0 and 1. Fuzzy logic has been extended to handle the concept
of partial truth, where the truth value may range between
completely true and completely false. Furthermore,
when linguistic variables are used, these degrees may be
managed by specific functions. Irrationality can be described
in terms of what is known as the fuzzjective.
II. RELATED WORK
In the modern society, as the rapid development of
automotive industries, environment pollution gradually
becomes a challenged problem to which more and more
people pay much attention. The increased environment
awareness and requirement for drivability have raised the
interest and investment in the researches of complicated
automotive modeling and control methods. One of the
researches concerns on a high power output while still
maintaining a good fuel economy.
In order to meet the requirements of high fuel combustion
efficiency, balanced torque and power output, the performance
development of a 1.5L small gasoline engine has been carried
out. The technologies for high power and low fuel
consumption include optimized combustion system and
optimized gas exchange system. During the process of engine
development, advanced engine simulation tools were applied
to optimize design parameters in order to minimize the
experimental iterations. The performance experiment of the
engine has been verified. The test results show that the power
is 80kW and the maximum torque is136N.m. The brake
specific fuel consumption (BSFC) at part load condition of
2000r/min & 2bar is378g/kW/h. The engine performance
achieves the development target.
Alternative power trains for automotive applications aim at
improving emissions and fuel economy. Lack of experience
with these relatively new technologies makes them ideal
applications for computer-based modeling and simulation
studies. There is a variety of configurations, control strategies,
and design variable choices that can be made. If mathematical
models exist, rigorous optimization techniques can be used to
explore the design space. This provides an overview of a
design environment for alternative power trains that has these
characteristics: modularity, allowing a system to be built by
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International Journal of Engineering Trends and Technology (IJETT) – Volume 16 Number 1 – Oct 2014
combining components, flexibility, allowing different levels
of fidelity and different existing codes to be used; and, rigor,
since it is based on mathematical methods of decision making.
A simple application to a hybrid diesel-electric power train is
included.
Although the recent technological improvements in engine,
fuel and after-treatment devices, road transport is still
responsible for air pollution in urban area due to increasing
number of circulating vehicles and their relative travelled
distances. The actual European type approval procedure for
passenger cars and light-duty vehicles fixes standard limits for
exhaust pollutants to be respected during the execution of a
normalized driving cycle.
This kind of procedure is not representative of the real on-road
use of vehicles, characterized by a more dynamic speed
profile: a fixed driving cycle, equal for all the vehicles
penalizes low power weight ratio vehicles that see the driving
cycle more hard to execute than vehicles with higher ratios
and does not take account for the driving style; the influence
of driving style to the emissions in driving the same vehicle is
not negligible [1, 2].
In previous years a lot of on-board pollutant measurements at
the exhaust of vehicles was carried out in order to assess the
real emission and consumption behavior: the high costs of
portable emissions analyzers (PEMS), their continuous
maintenance and calibrations, the fragility of the components
and the weight and the encumbrance do not allows big
acquisition campaigns.
Microscopic models produce emissions and fuel consumption
estimates with higher temporal resolution than other scales of
models. Most emissions and fuel consumption models were
developed with data from dynamometer testing which are
sufficiently accurate for macroscopic level emissions
inventories. The primary goal of this project is to improve the
microscopic modeling of emission and fuel consumption by
integrating detailed vehicle data into the simulation. The
proposed approach combines a microscopic traffic simulation
model (VISSIM) with detailed emissions and fuel
consumption data that is either collected in the field or
obtained from an existing emission inventory dataset. The
project also examines the possibility of using the vehicle’s
On-Board Diagnostic Board (OBD) to record real-time engine
and emissions data at a high temporal resolution. The outcome
of this project provides transportation operators with a model
that is capable of reliably estimating the environmental impact
of various traffic management policies at the microscopic
modeling and would fill a gap that currently exists in traffic
modeling capabilities[8].
There are two basic approaches that may be taken to combine
the Environmental Protection Agency (EPA) detailed
emissions and fuel consumption data, used in the Motor
Vehicle Emission Simulator (MOVES) model, with
microscopic simulation tools, such as VISSIM[2].
ISSN: 2231-5381
One alternative is to use the microscopic simulation model
vehicle specific power trajectory data as source activity input
for a MOVES emissions model. This approach has the
potential to improve the quality of source activity input to
MOVES project scale analysis, as well as make the process of
generating activity input simpler for the user.
The second alternative is to use the emissions and vehicle data
contained in the MOVES default database to improve the
input to the microscopic simulation emissions module to assist
users in developing custom emissions profiles in the emission
module in order to more accurately represent the vehicle fleet
operating in the United States. Full description of the two
alternatives as well as a case study to illustrate how EPA
MOVES’ data can be integrated into microscopic simulation
[6].
In previous years in Europe NEDC(New European Driving
Cycle) was designed to know the fuel consumption of vehicles
but no consideration is given so far to driving conditions not
represented by this cycle. This is a significant limitation of the
approach, as the NEDC driving cycle covers only a small
portion of the vehicle engine operation in both load and speed.
Just to give an indication, while a typical passenger car sold
today can accelerate from idle to 100 km/h at a rate of 2.8
m/s2, the NEDC imposes an acceleration of only 0.74
m/s2.while using NEDC many costumers recognize that the
officially reported fuel consumption does not reflect the fuel
consumption they experience as referenced in [10].
The previous work’s collected the vehicle data in real time
and then the data is clustered into five groups. A scientific
approach is conducted for the clustering using k-means
algorithm and two driving features including average velocity
and variance of velocity [7].
The hybrid regression models that predict hot stabilized
vehicle fuel consumption and emission rates for light-duty
vehicles and light-duty trucks are presented below. Key input
variables to these models are instantaneous vehicle speed and
acceleration measurements. The energy and emission models
described in this work utilize data collected at the Oak Ridge
National Laboratory that included fuel consumption and
emission rate measurements (CO, HC, and NOx) for five
light-duty vehicles and three light-duty trucks as a function of
the vehicle’s instantaneous speed and acceleration levels.
The fuel consumption and emission models are found to be
highly accurate compared to the ORNL data with coefficients
of determination ranging from 0.92 to 0.99. Given that the
models utilize the vehicle's instantaneous speed and
acceleration levels as independent variables, these models are
capable of evaluating the environmental impacts of
operational-level projects including Intelligent Transportation
Systems (ITS). The models developed in this study have been
incorporated within the INTEGRATION microscopic traffic
simulation model to further demonstrate their application and
relevance to the transportation profession.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 16 Number 1 – Oct 2014
Furthermore, these models have been utilized in conjunction
with Global Positioning System (GPS) speed measurements to
evaluate the energy and environmental impacts of operationallevel projects in the field are referenced in [11].
III. PROPOSED WORK
The proposed system makes use of the sensors
present in the automobiles i.e., the different sensor values are
collected from the automobiles and the collected data is fed to
a reduction algorithm. The algorithm reduces the M*N data to
M*1 data. The reduced data is fed to the model prepared. The
model calculates the fuel consumption of the driver.
The model makes use of fuzzy logic to calculate the fuel
consumption. Fuzzy logic is a rule based system it is
implemented using if then rules. For example two parameters
say x & y are varied in order to know which parameter effects
the fuel efficiency.
Transform an N x d matrix X into an N x m matrix Calculate
the d x d co-variance matrix :






Eigen vectors

The scheme is composed of four phases.
Ci,j(diagonal) is the variance of variable i
Ci,j(off-diagonal) is the covariance between variables
i and j
Calculate the eigenvectors of the covariance matrix
(orthonormal)
Select m eigenvectors that correspond to the largest
m eigenvalues.

If A is a square matrix, a non-zero vector v is an
eigenvector of A if there is a scalar λ
(eigenvalue)such that =λv.
If we think of the squared matrix as a transformation
matrix, then multiply it with the eigenvector do not
change its direction.
Fig. 1 Four Phases
1. Phase -1 Data Extraction
3. Phase -3 Develop Logic
There are various sensors in the automobiles and they are
connected to different ECU’s these ECU’s are intern
connected to the OBD port via the CAN bus.
Develop logic is the program that will calculate the fuel
efficiency of the driver. The develop logic code is developed
based upon the fuzzy logic. Fuzzy logic is a rule based system
it is based upon the if then rules.
2. Phase -2 Feature Extraction
Feature extraction is a special form of dimensionality
reduction. When the input data to an algorithm is too large to
be processed and it is suspected to be notoriously redundant
(e.g. the same measurement in both feet and meters) then the
input data will be transformed into a reduced representation
set of features (also named features vector). Transforming the
input data into the set of features is called feature extraction. If
the features extracted are carefully chosen it is expected that
the features set will extract the relevant information from the
input data in order to perform the desired task using this
reduced representation instead of the full size input.
Principle Component Analysis
Principal Component Analysis (PCA) is a useful
statistical technique that has found application in fields such
as face recognition and image compression, and is a common
technique for finding patterns in data of high dimension. It is a
statistical procedure that uses orthogonal transformation to
convert a set of observations of possibly correlated variables
into a set of values of linearly uncorrelated variables
called principal components.
Steps Involved in PCA
The following are the steps involved in PCA:
ISSN: 2231-5381
Here two parameters say X and Y are varied to see the effect
of those parameters on the fuel efficiency of the car. Suppose
when we vary parameter X and find that the fuel efficiency is
affected then we say that the parameter X is responsible for
fuel consumption.
Fuzzy Logic
Fuzzy logic is a form of many-valued logic; it deals
with reasoning that is approximate rather than fixed and exact.
Compared to traditional binary sets (where variables may take
on true or false values), fuzzy logic variables may have a truth
value that ranges in degree between 0 and 1. Fuzzy logic has
been extended to handle the concept of partial truth, where the
truth value may range between completely true and
completely false. Furthermore, when linguistic variables are
used, these degrees may be managed by specific functions.
Irrationality can be described in terms of what is known as the
fuzzjective.
The term "fuzzy logic" was introduced with the 1965 proposal
of fuzzy set theory by Lotfi A. Zadeh. Fuzzy logic has been
applied to many fields, from control theory to artificial
intelligence. Fuzzy logics had, however, been studied since
the 1920s, as infinite-valued logics - notably
by Łukasiewicz and Tarski.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 16 Number 1 – Oct 2014
The described procedure to learn Fuzzy rules from training
data is a straight forward method to capture functional
interrelations between relevant parameters. It enables the user
to constrain the size of the generated rule base and the
resolution of the resulting approximation by the number of
input Fuzzy Sets. This resolution can be adjusted specifically
to the current problem by the form and position of the Fuzzy
sets. Additionally this procedure allows it to complete, extend
and/or modify the generated function approximate locally by
adding or changing certain rules.
Classical logic only permits propositions having a value of
truth or falsity. The notion of whether 1+1=2 is an absolute,
immutable, mathematical truth. However, there exist certain
propositions with variable answers, such as asking various
people to identify a color. The notion of truth doesn't fall by
the wayside, but rather a means of representing and reasoning
over partial knowledge is afforded, by aggregating all possible
outcomes into a dimensional spectrum.
Both degrees of truth and probabilities range between 0 and 1
and hence may seem similar at first. For example, let a 100 ml
glass contain 30 ml of water. Then we may consider two
concepts: empty and full. The meaning of each of them can be
represented by a certain fuzzy set. Then one might define the
glass as being 0.7 empty and 0.3 full. Note that the concept of
emptiness would be subjective and thus would depend on the
observer or designer. Another designer might equally
well design a set membership function where the glass would
be considered full for all values down to 50 ml. It is essential
to realize that fuzzy logic uses truth degrees as a mathematical
model of the vagueness phenomenon while probability is a
mathematical model of ignorance.
Classical logic only permits propositions having a value of
truth or falsity. The notion of whether 1+1=2 is an absolute,
immutable, mathematical truth. However, there exist certain
propositions with variable answers, such as asking various
people to identify a color. The notion of truth doesn't fall by
the wayside, but rather a means of representing and reasoning
over partial knowledge is afforded, by aggregating all possible
outcomes into a dimensional spectrum.
Both degrees of truth and probabilities range between 0 and 1
and hence may seem similar at first. For example, let a 100 ml
glass contain 30 ml of water. Then we may consider two
concepts: empty and full. The meaning of each of them can be
represented by a certain fuzzy set. Then one might define the
glass as being 0.7 empty and 0.3 full. Note that the concept of
emptiness would be subjective and thus would depend on the
observer or designer. Another designer might equally
well design a set membership function where the glass would
be considered full for all values down to 50 ml. It is essential
to realize that fuzzy logic uses truth degrees as a mathematical
model of the vagueness phenomenon while probability is a
mathematical model of ignorance.
ISSN: 2231-5381
4. Phase -4 Auto Tuning
If the efficiency of the driver is less than 50% then we indicate
the driver that the person driving is inefficient. Even after the
indication from the intelligent ECU if the driver doesn’t
change the driving style then the ECU can control the engine
inputs such that engine gives better performance.
CAN Interface
CAN is a multi-master broadcast serial bus standard for
connecting ECUs. Each node is able to send and receive
messages, but not simultaneously. A message consists
primarily of an ID (identifier), which represents the priority of
the message, and up to eight data bytes. The improved CAN
(CAN FD) extends the length of the data section to up to 64
bytes per frame. It is transmitted serially onto the bus. This
signal pattern is encoded in non-return-to-zero (NRZ) and is
sensed by all nodes.
The devices that are connected by a CAN network are
typically sensors, actuators, and other control devices. These
devices are not connected directly to the bus, but through a
host processor and a CAN controller.
Bit timing
Each node in a CAN network has its own clock, and no clock
is sent during data transmission. Synchronization is done by
dividing each bit of the frame into a number of segments:
synchronization, propagation, phase 1 and phase 2. The length
of each phase segment can be adjusted based on network and
node conditions. The sample point falls between phase buffer
segment 1 and phase buffer segment 2, which helps facilitate
continuous synchronization. Continuous synchronization in
turn enables the receiver to be able to properly read the
messages.
Fig. 2 Bit Timing of a CAN Message
Frame
A CAN network can be configured to work with two different
message (or "frame") formats: the standard or base frame
format (described in CAN 2.0 A and CAN 2.0 B), and the
extended frame format (only described by CAN 2.0 B). The
only difference between the two formats is that the "CAN base
frame" supports a length of 11 bits for the identifier, and the
"CAN extended frame" supports a length of 29 bits for the
identifier, made up of the 11-bit identifier ("base identifier")
and an 18-bit extension ("identifier extension"). The
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International Journal of Engineering Trends and Technology (IJETT) – Volume 16 Number 1 – Oct 2014
distinction between CAN base frame format and CAN
extended frame format is made by using the IDE bit, which is
transmitted as dominant in case of an 11-bit frame, and
transmitted as recessive in case of a 29-bit frame.
The sample data’s collected using the OBD2 tool from
different cars are as shown in the Fig.
CAN has four frame types:

Data frame: a frame containing node data for
transmission.
 Remote frame: a frame requesting the transmission of a
specific identifier.
 Error frame: a frame transmitted by any node detecting an
error.
 Overload frame: a frame to inject a delay between data
and/or remote frame.
To overcome the drawbacks of the existing system another
system is introduced here. The proposed system makes use of
the sensors present in the car i.e., the different sensor values
are collected from the car and the collected data is fed to a
reduction algorithm. The algorithm reduces the M*N data to
M*1 data. The reduced data is fed to the model prepared. The
model calculates the fuel consumption of the driver.
The model makes use of fuzzy logic to calculate the
fuel consumption. Fuzzy logic is a rule based system it is
implemented using if then rules. For example two parameters
say x & y are varied in order to know which parameter effects
the fuel efficiency.
Fig. 5 Unsorted Data
The values collected from a car driven by different drivers are
given in Fig. 4. These values vary from each other it shows
that fuel consumption of the same car differs from one driver
to another. The collected values are jumbled as shown Fig. 5.
we have to sort them. After sorting the data is as shown
below.
The block diagram of the proposed system is as shown below
Fig. 6 Sorted Data
After applying PCA the reduced data will be as shown below
Fig. 3 Proposed System
IV. EXPERIMENTAL RESULTS AND COMPARISONS
The data can be collected from the car using an OBD
tool which is connected to the OBD port as shown in the
Figure 3
Fig. 7 Reduced Data After Applying PCA
Fig. 4 Data Collection
ISSN: 2231-5381
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International Journal of Engineering Trends and Technology (IJETT) – Volume 16 Number 1 – Oct 2014
The snapshots below show the results of the driver’s fuel
consumptions for drivers driving for every second in liters per
second. The X axis shows the fuel consumption and the Y axis
shows the time in seconds. The car was driven for one minute
by each driver.
The data collected from the driver as shown:

Driver name: Rohith R

Time : 19:10

Distance : 400 mts

Place : Vijaynagar service road
Fig.10 Fuel consumption/sec v/s Time for Driver3
Below Fig. 10 the comparison between the fuel efficiency of
the drivers mentioned above. The graph also shows the
frequent changes in fuel economy of the different drivers
throughout the driving period of one min.
Fig. 8 Fuel consumption/sec v/s Time for Driver1

Driver name: Priyadarshan M.P

Time : 19:20

Distance : 400 mts

Place : Vijaynagar service road
Fig. 11 Comparison Of Fuel Consumption Of Different Drivers
IV.CONCLUSION
The fuel efficiency of the vehicle and the efficiency
of the driver can be calculated using the sensor data available
in the vehicle. In the present work the sensor data are
collected using the OBD tool and we are analyzing the
dimension reduction algorithms.
Fig. 9 Fuel consumption/sec v/s Time for Driver2

Driver name: Varun.V

Time : 19:30

Distance : 400 mts

Place : Vijaynagar service road
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The instantaneous power supplied by the engine and the
consumption can be calculated using the sensors that vehicles
themselves have installed onboard. In the present work, an
instrumentation able to communicate with the electronic
system of vehicles has been used to collect sensors data from
spark ignition and diesel vehicles, and models to calculate
power and consumption have been developed. To calculate the
consumption if there is not the specific parameter from OBD
that supplies directly that value, intake airflow and air/fuel
ratio are needed. If both values are not available models have
been developed to calculate them.
In future work even after the indication from intelligent ECU
if the driver does not stop pressing the accelerator
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International Journal of Engineering Trends and Technology (IJETT) – Volume 16 Number 1 – Oct 2014
unnecessarily the ECU may stop supplying the fuel or it may
calculate the amount of fuel to just keep the engine running.
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