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BIS-ASE 2019

Journal of Physics: Conference Series 1517 (2020) 012013

IOP Publishing

doi:10.1088/1742-6596/1517/1/012013

1

Fuel control system modeling for LPG fueled engine using

Adaptive Neuro-Fuzzy Inference Systems (ANFIS)

S Munahar1*, M Setiyo1, B C Purnomo1, and Y Rifangi2

1 Department of Automotive Engineering, Universitas Muhammadiyah Magelang,

Magelang, Indonesia. 2 Laboratorium of Automotive Engineering, Universitas Muhammadiyah Magelang,

Magelang, Indonesia.

*Email: suroto@ummgl.ac.id

Abstract. LPG is an alternative fuel for gasoline engines that have almost all basic properties,

such as energy content, octane number, automatic ignition temperature, flame speed, and

flammability limits. The CO, CO2, HC, and NOx emissions produced by LPG engines are lower

than current gasoline engines. However, LPG vehicles with first generation LPG kits (vaporizer

and mixer) generally waste fuel during deceleration. Therefore, this study develops fuel control

during deceleration with Adaptive Neuro-Fuzzy Inference Systems (ANFIS). Simulation results

show that the controller is able to provide a more realistic picture of the dynamics of AFR during

deceleration. In conclusion, ANFIS is very promising to be implemented as an AFR controller in LPG-fueled vehicles.

1. Introduction Fuel efficiency and reduction of exhaust emissions are the targets of international automotive

technology development [1]. This is achieved through the development of Electric Vehicle (EV) and

Fuel Cell Vehicles (FCV). However, there are several disadvantages associated with EV technology

such as long charging time which makes it less practical, lack of infrastructure supporting technology

applications due to the need of a charging station with a large power requirement above 2000 watts. In

addition, the product cost is very high. The FCV technology does not currently have infrastructure

defects asides from the fact that it lacks support from the government. In the near future, the development

of the mix technology between the internal combustion engine (ICE) and the electric motor (hybrid

technology) will be marketed. This technology has a good fuel efficiency, however, it is relatively

expensive, with poor engine response[2].

Another development of automotive technology is the creation of Air to Fuel Ratio (AFR) control

technology, which controls the mixing process of Air and Fuel with stoichiometry targets. AFR control

has experienced a very significant increase, including the application of using Fuzzy Login Controller

(FLC) [2], [3]. While the closed loop compensator system with variation of PID filter time delay is used

to increase fuel efficiency [4]. Oxygen sensors in AFR control are used to achieve stoichiometry [5],

with changes in engine torque to fuel usage [6]. It is also used in the development of direct injection

technology [7], by directly injecting fuel into the combustion chamber using high pressure. AFR control

is also used to control the brake control system which enters the engine when brakes are applied [8].

LPG is a very good alternative fuel, due to its high octane and low carbon content which makes it usable

BIS-ASE 2019

Journal of Physics: Conference Series 1517 (2020) 012013

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doi:10.1088/1742-6596/1517/1/012013

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on vehicles with high compression. In addition, this fuel produces better thermal efficiency, with the

production of lower exhaust gas using similar technology for gasoline engines. Research on LPG

development have been carried out using some variables, including the injection method or converter

kits. This study therefore makes improvements to the performance and volumetric air entering the engine

[9]. with changes on the engine and the ignition system tuning method [10]. This is carried out to adjust

the combustion characteristics toward the fire propagation capability possessed by LPG with an effect

on the thin mixture method on passenger vehicles. [11]. The emission characteristics produced in LPG

vehicles are compared to gasoline and diesel engines as well as on sulphur content concentration [12],

[13]. An experimental study of changes in octane number was conducted to determine the risk of harm

posed by using LPG, as well as its sustainable availability in the long run as transportation fuel. Its

development on control systems with external engine began to be promoted in achieving fuel efficiency

using cut off. Besides that, the user tools were in terms of economy and efficiency.

From previous studies, LPG control systems have not been conducted by considering the deceleration

system. Therefore, it is important to develop a method to improve fuel efficiency because vehicles tend

to consume more power while decelerating. Despite the development of the control system, the

simulation test is needed to lift the vehicle to an acceleration of 50 km/hour. Furthermore, the AFR and

LPG flowrate are measured with a gauge to create a modeling system based on the data generated

through the simulation process. When an error is generated the modeling iteration of the LPG control

system, tends to contain limited results. Therefore, it provides a mathematical picture which the control

system has tested through a simulation process, with a great success potential for the real environment.

The advantages of this system are able to provide decisions from the black box system and the learning

process.

2. Method

2.1. ANFIS (Adaptive Neuro Fuzzy Inference System)

ANFIS (Adaptive Neuro Fuzzy Inference System) is one of the intelligent control systems which

integrates Artificial Neural Network (ANN) and Fuzzy Logic Controller (FLC). ANN controller has the

ability to provide more optimal decisions from black box systems through a learning/training process

which is based on the working principle of the human brain, while FLC provides decisions from unclear

or vague systems. ANFIS works based on several inputs to produce a decision, with each consisting of

a part which functions as a membership value (f). The developed control system has 2 inputs, which

includes the position of the throttle valve (x) in units of percent and vehicle speed (y) in units of km/hour.

In addition, the control system produces two outputs, including the dynamics of AFR and LPG Flowrate.

The rules associated with ANN are as follows:

If y is B1 and x is A1 then f1 = r10 + q12 x2 + p11 x1

If y is B2 and x is A2 then f2 = r20 + q22 x2 + p21x1

(1)

Based on the rules in equation (1) the average weighted value produced by the network (f) with the

firing strength is denoted with w1 and w2:

𝑓 =w2f2±w1f1

𝑤1+𝑤2 =𝑤2. 𝑓2 + 𝑤1. 𝑓1

(2)

The network architecture in ANN controller has several input, hidden layer, and output layers. The

output of each layer produces a network that is processed in the next calculation. In addition, the ANFIS

developed on the control system comprises of 5 layers shown in Figure 1.

BIS-ASE 2019

Journal of Physics: Conference Series 1517 (2020) 012013

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doi:10.1088/1742-6596/1517/1/012013

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Figure 1. Network architecture in ANFIS [14].

The ANFIS network architecture shown in Figure 1 has the following layers:

Layer 1: This layer consists of neurons that are adapted to the activation function. The output (O) denotes

the degree of membership originating from the input namely: μA2 (x), μA1 (x), μB2 (y) and

μB1 (y). The notation on this layer is:

O1,i = μBi (y) fork i = 1,2

(3)

O1,i = μAi (x) for i = 1,2

(4)

If the first layer uses generalized bell, the equation used in the output becomes:

O1,i = μAi (x) = 1

1+[ 𝑎−𝑐

𝑎]2𝑏𝑖

(5)

The parameters a, b, c are capable of changing its values known as premise. The parameter b

in this equation uses the value 1.

Layer 2: This uses the logic of the multiplication system to calculate the fuzification value of the fuzzy

implication. The equation used the following rules:

O2,i =wi = μAi(x). μBi for i = 1,2

(6)

Layer 3: This layer has a node which is generated by calculating the ratio of the systematic degrees of

the first member. The results obtained are known as normalized firing strength, with the

formula denoted as follows:

O3,i =wi = 𝑤𝑖

𝑤1+𝑤2 I = 1, 2

(7)

Layer 4: This layer has several parameters which were consequently calculated in the previous layer.

The equation notation is written as follows:

O4,i = 𝑤𝑖. 𝑓𝑖 = 𝑤𝑖 (pix1 + qix2 + ri)

(8)

Layer 5: This produces output in the form of values, which are obtained from the calculation of the sum

of all input parameters.

O5,i =∑ 𝑤𝑖. 𝑓𝑖𝑖 = ∑ 𝑤𝑖.𝑓𝑖𝑖

∑ 𝑓𝑖𝑖

(9)

BIS-ASE 2019

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2.2. The LPG Control System

Figure 2. The LPG control system model has been developed

The LPG control system developed has a model in Figure 2. Dynamic drivers are systems which run

the vehicle according to the user's wishes. This system consists of a throttle valve as the opening angle

of the gas pedal with the position gear responsible for the transmission of tasks in the acceleration and

deceleration processes. Furthermore, the engine dynamic is a system of dynamics changes in vehicles

which consists of four components including intake manifold, speed, dynamic LPG and AFR. The intake

manifold describes the dynamics of the mixture of air and LPG, while its speed is controlled through

the throttle valve opening process. The LPG control system is seen in the controller chart, based on input

from various sensors. The engine system, intake manifold system, LPG dynamics and AFR dynamics

modelling refers to previous research[15].

3. Result and Discussion

3.1. Set Up Testing

To test the controller system made with a simulation, the vehicle is mounted and run on a car lift. This

method provides information on how the controller system works and eliminates uncertain road

conditions. However, it has not been able to accommodate the vehicle's actual kinetic style, with the

data retrieval set up is shown in Figure 3.

Figure 3. Setup devices.

AFR LPG data, flowmeter, engine speed, TPS, and vehicle speed are retrieved using an adjustable

gas analyzer digital flowmeter, tachometer, Labview software application and speedometer respectively.

After the test equipment is installed, driving simulation is carried out with data retrieval conducted using

transmission gear 2 with a maximum speed of 50 km/h because it was carried out on the car lift. This

BIS-ASE 2019

Journal of Physics: Conference Series 1517 (2020) 012013

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doi:10.1088/1742-6596/1517/1/012013

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was followed by the conduction of several tests to obtain appropriate data with the results shown in table

1. Data generated from the simulation process in car lift, is included in ANFIS to analyze the developed

control system which tends to conduct self-learning of the developed black box. Its prediction is used

as a reference on the success of the real test.

Table 1. Data of simulation results.

No.

Vehicle

Speed (km/hour)

Throttle Valve

Position (%)

AFR

LPG

No. Vehicle Speed

(km/hour)

Throttle Valve

Position (%)

AFR

LPG

1. 5 0 15.4 13. 52 0 14.8

2. 10 4 14.9 14. 45 0 16.3

3. 18 9 14.6 15. 40 0 16.3

4. 24 13 13.7 16. 35 0 16.7

5. 28 16 13.6 17. 30 0 16.6

6. 30 21 12.8 18. 25 0 16.6

7. 34 24 12.7 19. 22 0 15.4

8. 38 31 12.8 20. 20 0 14.9

9. 42 41 12.4 21. 15 0 15.5

10. 45 46 12.6 22. 10 0 15.5

11. 50 50 12.7 23. 10 0 14.7

12. 52 18 13.3 13. 52 0 14.8

3.2. Input Data to ANFIS

The data in table 1 is entered in ANFIS with the initial stage used as input is the vehicle speed data

and the accelerator position. The target output used as an output parameter is AFR LPG with the testing

process computed using ANFIS. The data in table 1 is entered in ANFIS with the second stage used to

enter input data in the form of vehicle speed and the accelerator position. After entering data into ANFIS,

the selection of membership is conducted in decision making, using membership function in the form

of a generalized bell. In addition, it consists of input, hidden and output layers as shown in equations 1

- 12. The ANFIS programming architecture created in MATLAB.

Test results with ANN learning are included in the LPG control system model. Next, the LPG control

system runs for 10 seconds. Period of 0-1 seconds, the throttle valve opens from 0% to 30%. The 2

second period of the throttle valve is opened reaching 60%. After that the throttle valve is closed, which

is seen in figure 4.

Figure 4. Simulation of throttle valve opening

Figure 5. Changes in engine speed (left) and vehicle speed (right)

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It is known, when the throttle valve opens, the engine will operate at 6000 rpm. The vehicle

drove up to 70 km/hour. In the next period, the engine speed and speed decrease, as seen in

figure 5 because the throttle valve is closed. The performance of the LPG controller by adding

vehicle dynamic and the ANFIS method is shown in Figure 6. When the vehicle is running at

high speed, the engine speed is high, but the throttle valve position is closed, the control system

will cut off the flow of LPG to the engine. AFR without ANFIS has a value of around 15.5,

while AFR with ANFIS has increased beyond 16.5. LPG flow with ANFIS has decreased

beyond the LPG flow without ANFIS while the LPG controller without ANFIS has a magnitude

of around 0.0006 gram/s. Increasing the AFR value and decreasing LPG Flow during

deceleration simulates fuel savings that are controlled by the developed control system.

Figure 6. Effects of LPG controllers with ANFIS on AFR (left) and LPG flow rates (right)

4. Conclusion

A series of simulation results on LPG controller modeling with ANFIS can be applied. Vehicle speed,

throttle valve position, engine speed, able to control LPG flow according to design. At the time of

deceleration, AFR has increased, LPG flow to the engine is reduced even though the vehicle dynamics

variable is added. The conclusion is that, the LPG controller system with a deceleration system promises

in real applications.

Acknowledgement

The author is grateful to Kemenristekdikti, Republic of Indonesia for funding the research in 2019, and

the laboratory of the Universitas Muhammadiyah Magelang for research facilities.

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