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10.1109 ISAECT.2018.8618827-

Energy optimization method for connected vehicles
on a cloud database
Flah aymen
Photovoltaic, wind and geothermal unit
National school of engineering of
Gabès, Tunisia
University of Gabès, Tunisia
Photovoltaic, wind and geothermal unit
National School of Engineering of
University Of Gabès, Tunisia
Abstract— In this paper, we expose an energy optimization
method for an ensemble of electrical vehicles. The idea is based on
vehicles information sharing in a cloud database. Installed sensors
in a vehicle will allow us to know the needed vehicle information.
The regrouped data will be used then for building an optimal
energy experience which will be shared with the new vehicles. The
neural network technique is used here for the learning phases. The
proposed application for this work was validated for five
connected cars. Matlab Simulink was used for simulating the
results and given the present statistics.
Keywords—Energy, Hybrid electrical vehicles, power
management, Matlab, neural network, Cloud database, optimization.
Energy is the needed factor for any physical system for
continuing life in this word. Human or any physical system can't
proceed without this issue. We can't move or do anything
without energy. Our building physical systems can't also move
or work without energy. It's essential to find the best solution for
managing the existing energy in the specific situation. Try
moving from a point to another destination, without knowing
your energy resources and without estimating your energy
needs. It will be hardness and difficult. Also using a vehicle
based on any resource of energy can't be flexible if we can't
calculate or estimate our needs.
For a long time, engineers and researchers search to build a
multi-energy source system for helping users face the energy
sources dissipation. Photovoltaic and other renewable energy
were used in numerous applications, according to this objective.
We can see in [1] and in [2] that researchers search to build a
multi-source of energy for this application. Using a multi-source
of energy will be benefited if we can manage this quantity of
available power.
In this context, this paper exposes a problem and a solution
of the power management system inside an important electrical
field, which contains at least two sources of energy, the
combustion, and the electrical energy. Which are inside the
hybrid electric vehicle. The internal combustion engine will use
the combustion energy for doing its touch and the installed
Lassaad SBITA
Photovoltaic, wind and geothermal unit
National School of Engineering of
University of Gabès, TUNISIA
electrical motor will also use the electricity for running. Here,
we can find several version of the hybrid vehicle as the serial
and the parallel electrical vehicle. This is depending on the
placement of the engines. In our previous work cited in [3], we
have exposed those versions. Different forms of power
management algorithms and principle were exposed to various
cited work as [4], [5] and our last work related to this field is
exposed as [6].
Basing on the last statistics appeared in 2018 we can
conclude that it is possible that we have more than 22000
electrical vehicles on the road in the same city. We can see the
example of Canada in this link [7]. If each vehicle has its
conductor philosophy, its positions, its own internal parameters
and its specific condition. This can be a very important database
about this vehicle and we can benefit from this stored
The idea is based on the vehicle's information share. Each
vehicle can allocate its own experience for a specific condition
with another vehicle or with an ensemble of the vehicle. Using a
Cloud database, we can store all those information, we can build
a learning algorithm basing on an optimization technique for
helping other vehicles defining rapidly the needed decision and
the best performance for minimizing the energy losses due to an
unknowing information.
As we know that approximately, in all the latest versions of
cars, intelligent sensors were used and installed inside. Some
systems are related to the facing detection, where we can be used
for driver fatigue system. This system can detect the driver mode
if it is happy, angry or normal etc.…
Same sensors used in Lane Keeping Assist System (LKAS)
[8] to oblige driver to keep hands on the steering wheel, can
provide heartbeats rate and determine if the driver is nervous.
Traditional vehicle sensors as speed sensors, fuel sensors,
battery sensors, etc.… will be useful for knowing the energy
state of the car. GPS is also installed in all the new cars; this will
be a benefit for detecting the vehicle position. More details,
about this part, were exposed in our previous work cited in [8].
So in this paper, we will try to regroup all those sensors
information for building a vehicle database, which will be
utilized, in a second stage, in a neural-cloud database for
building an optimal energy experience for the corresponding
specific condition.
vehicle connected to the cloud database. Each vehicle will
their information as it is described in figure
So, the objective of this paper will be attempted after a
general introduction and after showing the essential components
of a hybrid vehicle model. Where we explained before, the
needed database architecture and its components, then we will
expose the communication protocol for the cars and the existed
database. The principle of neural network learning algorithm
inside the cloud database will be then explained and the obtained
simulated results, for a tangible prototype example, will be
discussed. We conclude this paper with a principal conclusion.
Electrical vehicles were developed on two models; Totally
electrical vehicle (TEV) and hybrid electric vehicle (HEV). At
least we will find an electrical motor, which will be used as the
main active motor or a reserve active motor. This is possible in
the hybrid version [9]. The system of battery and the electronic
power system is present in two versions of cars. The fuel tank
and the ICE are present only on the hybrid version. The position
of an electrical motor with the hybrid one gives the nomination
of series or parallel hybrid electrical vehicle [10].
Figure (1), indicates the relation between those blocs for a
hybrid and a pure electric vehicle model.
Fig. 2. Information on vehicle shared in the cloud database
Knowing the GPS position of the vehicle will allow us to know
the vehicle slope and then the possible needed. Using the face
detection system will help to see the conductor mode of the
vehicle. Also knowing the batteries and the fuel status will
permit us knowing if the hybrid mode must be launched or not.
The vehicle weight is equally important that knowing the applied
torque on the machine in the TEV architecture. So each
information will be useful for optimizing the power
Actually, we can't have a database if we don't have vehicles.
The database doesn't exist without a vehicle for shared
information. But, if the database exists, this is indicating that we
can find at least one vehicle connected to this base. However, it
is possible for some other vehicles to join this community. This
is must be checked by the driver from the beginning. Then each
new vehicle must give responses to numerous requests. More
description of this point is explained as follows:
Fig. 1. TEV and HEV architecture
The hybrid nomination will be valid if, at least, two energy
sources are used. It is possible to use more than two sources, we
can enlarge also with the renewable energy, as the solar energy.
More than work was presented on this subject as presented in [1]
and [11]. This is will help the system autonomy. Basing on the
conventional electric architecture we will develop in the coming
parts, the proposed energy management architecture.
As we have exposed in the introduction section, each vehicle,
needs an optimal energy experience, must share in the
corresponding cloud database some needed information. Those
data will be regrouped and classified from each connected
vehicle for building the energy experience database. Actually,
it's hard to build a perfect archive. Because we can't find in the
same zone, the similar vehicles models with the same installed
sensors and the same architecture. Therefore, for simplifying the
application we will suppose in this first version of this work, that
all the connected vehicles have the exact same characteristics.
So, for starting creating a database, we must have more than
If the original vehicle is fresh for this database, the system
will ask firstly the car model, if it is HEV or TEV, the vehicle
position, and the target trajectory. It will classify the car as the
corresponding category. Inside, the database, the neural network
program will compare the vehicle's information faces the other
existed info. Two scenarios can be founded, if the vehicle
condition has a similarity in the database, the algorithm will
choose, from the existed, the optimal one. Else if the sent
information is novel, the system will adjust the database,
according to this vehicle category, and a new learning phase will
be started. Then, a novel optimal energy condition will have
existed. This vehicle will preserve its energy experience until the
system gives a different optimal condition.
For the HEV model, the car will be asked to give its energy
experience as fuel and electricity consumption form and the
level of its stored energy. Also, the vehicle speed will be asked.
All of that information must be accessible to 300Km. Else if the
car is classified in the TEV model, the system will ask, basically,
the electrical power consumed, the vehicle speed and
acceleration during the last 100km. If one of that essential
information is absent the car does not have an order to join the
database. The presented figure (3), explains this protocol.
algorithm will start again. This is the step C. Else; step B will be
Vehicles who built the database
Electrical Power
Batterie Statuse
Electrical Power
Mechanical Power
Fuel consumed
Batterie Statuse
New vehicle
Step A
Database with neural
network Algorithm
Optimal experience
Fig. 3. TEV and HEV communication with the Neural network database
Step B
Learning from vehicles 1 TO 4
Sent the existed
optimal energie
experience for the
vehicle 5
Optimal energy
exepreince O.EX
As we have multi-input information in the database, from
multi-connected vehicles, the system will organize those
parameters into several vectors input. The number of input will
configure the neural network. For the neural network output, this
is linked to the car model, if it is TEV or HEV. In this work, we
will expose, only, the neural architecture of a TEV.
The number of neurons in the input layer depends on the number
of attached vectors. As we have 5 parameters from the TEV and
7 from the HEV, the number of neurons was selected as it is
exposed in the previous table. In the output layer, we have to fix
one neuron for the TEV, because we have an alone energy
source. However, two sources of energy exist in the HEV model.
Then the references of needed power, which is equal to the
optimal energy experience, will be injected into the vehicle.
Adding the Vehicle 5 data
to the database and adjust
the energy experinece
Step C
Fig. 4. Description for the algorithm function
In the rest of this part, we will expose some simulated data from
four total electrical vehicles. The results for the hybrid model
will be the object of a future work. The stored data will be
utilized in the learning algorithm for having the best energy
In figure (6), we expose the battery outputted power from four
different cars, according to the acceleration form given in figure
(5) and related to a distinctive exterior climate. We suppose that
the weight of the cars is the same and it is equivalent to 1200Kg,
the vehicle trajectory is also the same and it is fixed at 500
We will attempt to explain the running algorithm using five
connected cars, four of them who construct the database and the
fifth one will be a guest, which will ask an optimal energy
experience. We suppose that all the cars are placed on the same
trajectory. According to the figure (4), if the car number five is
not available, the learning algorithm will use the data of the four
existed cars. This is the step A. The algorithm will learn from
those cars and it will try to generate the optimal energy
experience (EX). When the fifth vehicle is present and requests
an optimal energy experience, better than the existing, two
possible happens. If existed energy experience is better, the
vehicle data will be added to the database and the learning
Fig. 5. Acceleration form for the four existing Cars
The neural network algorithm will take those data and it will try
to learn about this vehicle type comportment for a given
acceleration as it is shown in figure (5). The learning phase was
elaborated for 1000 iterations, which is corresponding to the
minimum possible learning error and a reasonable time
execution. The neural architecture is built on a back-propagation
learning algorithm.
circuit (SOCF). Also, we attach the positive acceleration
(P.acce) time for each car according to the maximum
acceleration slop (Acc.Slop) during this period.
By comparing the case of the first and the fifth car, we can see
that the results related to the fifth card are better. We have saved
0.2% of power antonym, in this case, face the case where the
vehicle five construct its own energy form.
Fig. 6. Battery power for the four existing Cars, (a) is related to the EX1,
(b) is related to EX2, (c) is related to EX3, (d) is related to EX4
After having the corresponding neural bloc, the overall
algorithm will be ready. It is easy now for any vehicle who asks
an optimal energy experience that has its need. In figure (7) and
(8), we expose the results related to the guest vehicle. Figure (7)
is linked to the fifth vehicle acceleration form. Figure (8) is
related to the power battery for this car.
70 sec
50 sec
100 sec
80 sec
68 sec
As it is outlined in this paper, a smart learning database basing
on neural network algorithm was used in a power management
problem related to the electric vehicle. The vehicles here are
related to a cloud database and classified into two categories.
The proposed method will use some essential information,
associated with the car and driver. For detecting the best and the
optimal energy experience, which will be used then by the guest
cars. This optimal energy experience will be a benefit for
reducing losses power related to electricity inside the electric
vehicle. The given results and figures, account for this principle
of this application and prove its efficiency.
Fig. 7. Acceleration form for the guest Cars
Fig. 8. Battery outputted power for the guest Car
In the table (2) we expose the related statistics for the five
cars depending on the consumed energy. The statistics were
simulated for only 500 meters. We expose for each vehicle the
battery SOC from the beginning (SOC I ) and at the end of the
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