GREECOPE: Green Computing with Piezoelectric Effect

Meghana Kshirsagar

1 a

, Rutuja Lahoti

, Tanishq More

and Conor Ryan

1 b

Biocomputing and Developmental Systems Group, Lero, University of Limerick, Ireland

Department of Information Technology, Government College of Engineering, Aurangabad, India

Keywords: Intelligent Transportation, Piezoelectric Effect, Smart Cities, Road Transport.

Abstract: The growing interest in the search and use of alternative resources for renewable energy can lead the future

towards substantially decreasing carbon footprint and reduce the effects of global warming. The proposed

research explores the possibility of harnessing piezoelectric energy from the environment of moving vehicles

on road. Although the technology is still immature, it has the advantage of having zero carbon footprints thus

making it ideal to investigate the potential for green energy generation. The main objective is to develop

regression models that can estimate energy generated from vehicular traffic. Energy is generated when force

is applied to piezoelectric transducers which depend on significant factors such as the number of piezoelectric

transducers and their arrangement, load applied and frequency. We design Support Vector Machine (SVM)

and Generalised Linear Model (GLM) for predicting energy. The best features for training the model were

selected by incorporating feature selection techniques such as Pearson’s correlation coefficient and Mutual

Information Statistics. The experimental setup makes use of simulated data which takes into account vehicle

count of different vehicles with and without load. The accuracy achieved from SVM and GLM are 99.6% and

99.7% respectively. The energy savings achieved by making use of generated piezoelectric energy is

discussed with a sample scenario of Motorway50 of Dublin, the Irish Capital city. Through this work, we

propose to investigate deeper into the feasibility towards cost-effectiveness by utilizing energy which is

wasted by human and vehicular locomotion.

1 INTRODUCTION

Basic human needs largely rely upon non-renewable

sources (Reeker, 2014) of energy which will become

exhausted in the future. Thus, there's a major

imperative to seek out some way through which we

are able to generate energy from the available

resources. This can cut back the use of non-renewable

energy sources to a good extent and may help to

create a better future. Solar power, windmills and

geothermal energy are some examples of renewable

energy (Davies, 2017). Petrol and diesel are non-

renewable resources and contribute significantly to

the effects of heat and climate impact over time as

travel has become one of the most important

requirements of our lifestyle, which is made possible

by the use of a variety of vehicles. Vehicles use fossil

fuels, which release large amounts of carbon and

greenhouse gas. Electric vehicles are becoming

increasingly more popular and their current limited

use presently is expected to scale up in the coming

https://orcid.org/0000-0002-8182-2465

https://orcid.org/0000-0002-7002-5815

decade. Hence with the anticipated growing trend for

electric vehicles the use of electricity as its fuel would

substantially increase. Fortunately, the piezoelectric

effect can utilize waste locomotion energy and

vehicle kinetic energy and convert it into electricity.

This can be achieved through the use of piezoelectric

sensors that can generate electrical energy when

pressure is applied to it. Therefore, energy can be

built up through the movement of road vehicles and

people (Borikar, 2017).

In this work a comprehensive model to predict the

piezoelectric energy output based on historical data is

discussed. We explore the use of Artificial

intelligence-based methods like Support Vector

Machine (SVM) (Kaur, 2017) and Generalized

Linear Model (GLM) (McCullagh and Nelder, 1989)

for the estimation of electricity generation through

piezoelectric effect from moving road vehicles is

discussed. Artificial intelligence-based methods have

the twofold advantage of being flexible and capable

of dealing with non-linearity.

164

Kshirsagar, M., Lahoti, R., More, T. and Ryan, C.

GREECOPE: Green Computing with Piezoelectric Effect.

DOI: 10.5220/0010445801640171

In Proceedings of the 10th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS 2021), pages 164-171

ISBN: 978-989-758-512-8

2 BACKGROUND

This section will discuss the characteristics of

piezoelectric material, their application and the

formulation for calculation of generation of energy.

2.1 Piezoelectric Materials,

Characteristics & Formula

Currently the most extensively used piezoelectric

material is PZT (Pb- Lead, Zr- Zirconium, Ti-

titanium). The working process of the piezoelectric

system when placed beneath the road is explained in

(Kour and Charif, 2016).

The proposed system is completely based on the

sound theoretical concepts of Piezoelectric effect,

which is the generation of electric charge when

subjected to pressure. Materials which exhibit

piezoelectric effect are known as piezoelectric

material.

Certain factors affect the amount of power

generated by incorporating piezoelectric plates

beneath the road surfaces, such as vehicle speed,

vehicle mass, traffic flow, dimension of road on

which piezoelectric plates are embedded and the

frequency settings depending upon locations. Speed

and weight of a vehicle is directly proportional to

energy generated.

Below formulas are useful while preparing the

dataset mentioned in 3.2 and regression model.

Let the total no of vehicles be T

and individual

vehicle count of car, bus, truck, motorcycle is T1, T2,

T3 and T4 respectively. Consider the average mass of

total no car, bus truck and motorcycle as M1, M2, M3

and M4 respectively.

Rolling resistance Fr of the wheels is calculated by

      

(1)

Where N

is the normal force acting on the surface

Cr - coefficient of rolling friction (0.03-0.15)

Mi = Mass of particular vehicle class, where

i (1,4)

g = Gravity

The rolling resistance of the tire can be overcome by

the power given by

   

(2)

Where v = speed of vehicle.

Let the time taken by a vehicle to pass over

piezoelectric roads be defined as:

  

(3)

= length of road laid with piezoelectric generator.

The intensity of energy generated due to mechanical

force of vehicles is calculated as:

  





(4)

Where U

= mechanical energy

  

(5)

λ = 0.078 and P represents the Power generated by

the respective vehicle class of mass M

and k =

Constant (to convert joules to kWh = 2.7778 × 10

-7

kWh). Thus, P is the final output that the regression

model must predict.

2.2 Applications and Estimation of

Energy Savings

Figure 1: Different Scenarios where piezoelectric materials

can be implemented.

Piezoelectricity can be generated by placing the

piezoelectric tiles at various locations. The locations

must have either large amounts of vehicles or human

movements to harness energy in a profitable way.

Figure 1 depicts various scenarios where

piezoelectric tiles/plates can be installed. For

instance, a Dance club can meet 60% of its energy

requirement (A., 2019), because large numbers of

people are contained in small areas. The frequency of

the movements is very high making them one of the

ideal locations to place the piezoelectric plates. From

future perspectives, airports will be an ideal location

to lay piezoelectric plates.

3 EXPERIMENTAL SETUP &

EMPIRICAL ANALYSIS

This section will discuss the architecture proposed by

the authors, dataset used and results obtained by

GREECOPE: Green Computing with Piezoelectric Effect

165

performing tests.

3.1 Proposed Architecture

The paper proposes an architecture which will

estimate the amount of electricity that can be

generated by harnessing piezoelectric effect from

moving road vehicles. The process flow consists of

two parts: the AI model for prediction of energy and

the distribution of generated energy. The AI model

takes the output of the GREECOCO (Kshirsagar et

al., 2021) system as its input.

The GREECOCO system consists of the vehicle

detection and classification algorithm which is the

authors' previous paper. Traffic videos are fetched

from the installed cameras and vehicle detection is

performed using three algorithms: YOLOv3, Faster

RCNN and Mask RCNN. After detecting the vehicle,

it is recognized into five classes which are car, bus,

truck, motorcycle and bicycle. The vehicle count is

stored in a database for further analysis. We access

this database to get a count which will be an input to

our next module which predicts the energy generated.

Figure 2 shows the overall architecture of a

system. The output of the GREECOCO system which

is the individual as well as total count of four vehicle

classes (Bus, Truck, Car and Motorcycle) is

combined with features such as vehicle mass and the

speed of moving vehicles. After performing feature

extraction, mechanical energy is calculated. This

mechanical energy is given as an input to the

Artificial Intelligence (AI) module which predicts the

amount of energy generated by each vehicle class

during various time durations. SVM and GLM

approaches are used for energy prediction. The

prediction results are stored in a database for future

reference and energy analysis. The generated energy

will be distributed to charging stations for electric

cars and streetlights.

3.2 Dataset Details

The training dataset (Table 1) of the energy prediction

model was created using the NumPy library for 6

years from 2014 to 2020. The dataset consists of a

count of individual vehicle classes, as well as the total

Figure 2: Proposed Architecture for GREE-COCO.

SMARTGREENS 2021 - 10th International Conference on Smart Cities and Green ICT Systems

166

Figure 3: Dataset details for the training of SVM and GLM

model.

count of vehicles from the GREECOCO system.

Also factors such as weight and speed of vehicle

are attributes of the dataset, along with force,

mechanical energy, and the expected energy

generated by each vehicle class. In the dataset, the

average mass of individual car, bus, truck and

motorcycle is 1000 kg, 7500 kg, 9500 kg and 170 kg

respectively. The data was used for the training of

SVM and GLM regression models, which predicts the

power generated for each vehicle class depending

upon the number of vehicles.

The testing dataset consisted of 10000 (416 days)

hours of data. Figure 3 demonstrates the cyclic nature

of each vehicle class’s count during an entire day.

This portrays the daily life situation of traffic

distribution throughout the day.

3.3 Data Pre-processing

Feature selection is a method of defining and

selecting a subset of input variables that the output

variable is most dependent. Our dataset consists of

40,000 sample, each with 15 input features, and we

employed Correlation Statistics (Sedgwick, 2012)

and Mutual Information Statistics (Beraha et al.,

2019) to perform selection.

The Correlation coefficient (r) is used to measure

the linear associativity between variables.

(6)

Here x

and y

are the values of sample dataset

and  and  are the mean values respectively. Here

the value r = 1 means a perfect positive correlation

and r = -1 means negative correlation.

Mutual information is a measure of dependence or

mutual dependence between two random variables.

The mutual information between two random

variables X and Y can be stated formally as follows:









 







 

(7)

Where I(X ; Y) is the mutual information for X and Y,

H(X) is the entropy for X and H(X | Y) is the

conditional entropy for X given Y.

Mutual information is always larger than or equal

to zero, where the larger the value, the greater the

relationship between the two variables. If the

calculated result is zero, then the variables are

independent.

Table 1: Dataset used for training SVM and GLM models.

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167

Figure 4: Feature Selection Results by Correlation Statistics.

Figure 5: Feature Selection Results by Mutual Information Statistics.

3.4 Regression Models for Estimating

Energy

To predict the generated energy by the vehicle, AI

models using SVM and GLM have been designed.

Support Vector Machine (SVM) algorithm, is a

decision boundary where the distance between the

closest member of different classes is maximum.

Many nonlinear decision boundaries, and kernels are

modelled by the SVM algorithm. The major benefit

of using SVM is the robustness, especially in high-

dimensional space, against data overfitting. Another

common training technique for a diverse set of

regression models is Generalized Linear Models

(GLM). GLM enables to express the relation between

covariates X and response y in a linear, additive

manner. As a result, GLMs is a perfect option for real

world datasets which are not linear and

heteroscedastic and where we cannot predict the

model’s error distribution.

Figure 6: The expected energy by different class vehicles.

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168

Figure 6 illustrates the prediction of Car,

Motorcycle, Bus and Truck for 50 hours

respectively. The figures highlight the SVM and

GLM predicted energy vs. actual energy generated

for a particular set of vehicles during a specific hour.

Throughout the table, Car generated more energy

than the other vehicle classes. The energy generated

by a vehicle depends upon the amount of load it

carries. If a vehicle is not carrying any load, then the

amount of energy generated differs in a measurable

amount than the vehicle carrying enormous load.

Figure 7 the comparison of vehicle’s generated

energy in 2 scenarios with and without load is

illustrated. We can see there is a significant amount

of difference in two scenarios output.

Figure 7: Comparison of Voltage Generation of different

vehicles with load and without load.

3.5 Performance Analysis of Regression

Models on Varying Traffic Count

The table 2 below shows five different scenarios with

varying vehicle counts and estimation of energy

generated from our AI models.

Scenario 1 depicts a total vehicle count of 100

where the categorical count for each vehicle class is

further distributed as 45 for the vehicle class car, 10

for the vehicle class bus, 5 for trucks and 40 for

motorbikes. In scenario 1, hence the total energy

generated by each class would be, for car 0.477 kWh,

bus 0.796kWh, truck 0.504 kWh and motorbikes

0.072 kWh. The combined energy amounts to 1.84

kWh, which can be used to light up 1 LED bulb

consuming 100w for a total duration of 18.4 hrs.

Similarly, scenarios 2-5 depict energy generation

according to different traffic counts assumptions.

In scenario 5, same number of vehicles are

considered irrespective of the vehicle class, though it

is noticeable that larger difference in the generated

energy.

Table 2: Analysis of Generated Energy from varying

vehicle count.

Vehicle Count

Energy

Generated(kWh)

Lighting Up of

LED Bulbs

Period (100 W)

100 (C:45, B:10,

T:5, M:40)

1.84 (CE:0.477,

BE:0.796, TE:

0.504, ME:0.072)

1 bulb for

18.4hrs

500 (C:250,

B:100, T:50,

M:100)

15.83 (CE:2.654,

BE:7.962, TE:

5.042, ME:0.180)

6 bulbs for

24hrs

1000 (C:20,

B:150, T:440,

M:210)

58.82 (CE:2.123,

BE:11.943,

TE:44.378,

ME:0.379)

24 bulbs for

24hrs

5000 (C:1200,

B:900, T:550,

M:2350)

144.11(CE:12.74,

BE:71.663,

TE:55.472,

ME:4.241)

60 bulbs for

24hrs

10000 (C:2500,

B:2500, T:2500,

M:2500)

482.26 (CE:26.541,

BE:199.064,

TE:252.147,

ME:4.512)

200 bulbs for

24hrs

4 DISCUSSION

This part will explore the returns of the investment

and the challenges that the system may face in the real

world.

4.1 Estimation of Return of Investment

We now illustrate the scenario of Motorway 50 (M50)

of Dublin, Ireland to demonstrate the returns obtained

by installing it with piezoelectric plates. The

construction cost of the entire motorway was €1

billion (Paschal Donohoe, 2020). Consider the

following requirements for infrastructure

development for the construction roads laid with

piezoelectric plates.

The estimated number of generators required for

a 1 km patch of a 2-way road are 13,333. Thus, the

entire 4-lane motorway will require 53,332

generators considering the cost of each individual

generator to be €25 (Garland, 2013). For a 4-lane road

covering a distance of 45.5 km we have the

approximate cost as 45.5×53332×25 which results in

€60,665,150 (approximately €60.6 million).

Moreover, labour and installation costs needed for

GREECOPE: Green Computing with Piezoelectric Effect

169

laying the piezoelectric tiles beneath the road will be

around € 5 million and €7 million respectively (Chew

et al., 2017), also an additional miscellaneous amount

of € 10 million is considered. Hence the entire budget

becomes €1 billion + €60.6 million + €5 million + €7

million + €10 million which amount to €1.082 billion.

The average power output obtained from the

piezoelectric generators can be estimated by

considering the following traffic scenario:

• Average traffic volume is 420,000 per day

(Baker., 2020)

• Average Speed of vehicles: 60kmph

• Power generated 175176.4 ×10

-7

kWh (per

vehicle) from formulas (1) - (5) discussed in

section 3.2

• Total power generated from the average traffic

volume = 7357.4 kWh

To calculate power generated from the entire road

patch of 45.5 kms we have,

Total power generated = 45.5 × 7,357.4 × 365

= 122,188,020.5 kWh

Currently, the Government of Ireland charges 17.67

cent/kWh for the electricity, thus revenue generated

from the generated electricity would be

= 122,188,020.5 kWh × 17.67

= €21,590,623.22

= €21.6 million (approx.)

Hence, the amount invested to lay piezoelectric

plates on M50 road will be recouped in approximately

5-6 years with the added labour costs and other

requirements. Knowing that the average life of

piezoelectric tiles to be around 30 years strongly

implies that the income generated by adopting this

technology can have roughly 24 years of profits.

Figure 8: Total energy generated and energy consumed.

Now let's consider the scenario, for instance, on a

200m patch of a road with 100,000 vehicular traffic

each day. Let this stretch have around 9 street lamps

(High-Pressure Sodium (HPS) Lamp) installed. Now

assuming that these 9 lamps each utilize 225 W per

hour, the energy required to light up the 9 street lamps

for 2 months, assuming they are used for 10hrs per

day, is 1215kWh. If we lay piezoelectric tiles on this

200m patch, then the energy generated is 12,740 kWh

(2 months), which further can be used to light up the

lamps. The comparison of energy generated and

energy consumed for lighting 9 HPS lamps is

highlighted in figure 8. The unused generated energy

(11525kWh) can be stored in Energy storage devices.

In addition to this, it can be used for electric vehicle

charging stations or can be distributed through the

electrical grid.

4.2 Challenges

The energy harnessing system using piezoelectric

material produces green, safe, and renewable energy

which can be generated at a high scale. In-spite of all

these positive sides, the system needs to resolve the

issue with respect to storing the generated energy.

The batteries' efficiency to convert the energy is not

constant and sometimes it is low. The available

technology of the storage system (for instance

Batteries) highly influences the efficiency of the

energy harnessing system. It must be understood that

the rechargeable batteries have a fixed lifespan to

consider the option of recharging the depleted

batteries. In some cases, due to limited storage,

batteries can seldom store only 50% of the generated

energy. However, dependency upon batteries can be

reduced or eliminated by choosing an independent

method for the energy harvesting system on the roads.

Thus, the direct use of generated electric energy is

highlighted by the above approach Si.e., energy must

be used as it is produced. In such a self-sustaining

way, energy loss can be reduced. The other aspect to

address is around maintenance costs of the

piezoelectric plates or sensors which are embedded

within the pavement layers. Therefore, each time the

plates need to be repaired or changed, the asphalt

layers of the road must be removed and reopened.

Such repairs will incur additional costs. This issue can

be resolved by improving robustness of transducers.

5 CONCLUSIONS

This paper discussed the possibility of harnessing

piezoelectric energy from vehicular traffic in busy

roads. The authors design SVM and GLM regression

models to estimate the electricity that can be

generated from traffic counts of different road

vehicles. The authors comprehensively discuss the

potential return on investment by adopting this

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170

architecture for a sample express highway case. The

authors strongly validate the potential advantages that

can be incurred by adoption of this technology

through depiction of vast amounts of energy that can

be generated and potentially redistributed in various

settings. Finally, the authors through a comparative

tabulation with other popular renewable energy

sources bring forth the low maintenance and natural

environment preservation that is possible through

adoption of this technology.

ACKNOWLEDGEMENTS

This work is supported under World Bank Technical

Education Quality Improvement Programme Seed

Grant (Government. College of Engineering,

Aurangabad) and partially supported by Science

Foundation Ireland grant #16/IA/4605.

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