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Applying Fuzzy Logic Control on Warning Sound Control System for Electric Vehicles

I. SALLEHᵃ, M. Z. MD ZAINᵇ, R. I. RAJA HAMZAHᵇ, A. R. MUSAᵇ,

Department of Applied Mechanics and Design Faculty of Mechanical Engineering, Universiti Teknologi Malaysia

81310 UTM Skudai, Johor MALAYSIA

ᵃ[email protected], ᵇ[email protected]

Abstract: - Electric vehicle (EV) is well-known for its silence since the car uses asmooth electric motor instead of rough internal combustion engine (ICE) to propel it. Most people buy an EV because of this special feature. Unfortunately,this condition contributes to dangerous situations, especially for careless pedestrians and much worse, visually impaired people. The quiet character of the vehicle occurs during low speed movement, such as when the vehicle starts to move from static position, enters a junction, and reverses. For commonICE vehicles, the engine noise will act as a signal to notify pedestrians, especially when the car moves at low velocity. This paper discusses the usage of fuzzy logic control in managing and controlling the sound level emitted asa warning signal to alert pedestriansof oncoming EV based ontwo conditions; the pedestrian distance detected and the speed of the vehicle.These conditions play an important role in deciding the level of sound to be released, and this study hopes to optimize the release of warning signals and keep the unique soundless feature of the EV. The development of the system and control system validation will be shown and discussed. Key-Words: - Fuzzy Logic Control, Warning Sound System, Electric Vehicle 1 Introduction People depend on vehicles to travel from one place to another, hence causing the automotive industry to become one of the main industries in developing and rich countries. Car manufacturer research and development departments are always trying to bring new and fresh ideas to be implemented in their latest prototype model. Today, people in developing countries feel the burden of expenses because of the increase in petroleum price. Besides that, a lot of environmental non-government organizations (NGO’s) haveraisedconcerns on the effects of carbon monoxide gas released by petrol-based vehicles. According to Environmental Protection Agency (EPA) in United State (U.S), half of the air pollutants in U.S are from vehicles. EPA had stated that exposure to carbon monoxide can affect human health. In order to help people in saving their money and saving the earth, engineers havedevelopedthe EV, a vehicle that uses electric motor instead of engine to propel the car. As soon as th U.S started using EV, Japan and several European

countries expressed their concerns over the lack of noise produced.

Eventhough EV can be called a conducive vehicle by cutting petrol expenses and reducing air pollution, the car lacks of a sound to alert pedestrians. EV use quiet electrical motorsas compared toICE that produce engine noise. According to research done by National Highway Traffic Safety Administration (NHTSA) of U.S, from year 2000 to 2007, Hybrid Electric Vehicle (HEV) and EV had hit a total of 77 pedestrians and 48 bicyclists [1]. This shows that EV can be a threat to the safety of pedestrians, especially visually impared people who depend on vehicle sound. Without sufficient sound cues from EV, visual impaired people experience difficulty in detecting and predicting the vehicle presence and this could lead to accidents [2]. Further research by NHTSA, found out that most of the pedestrian accidents case involving EV happen when the vehicle travels at a low speed, such as entering a junction, and entering or leaving parking lot [1]. Alfred Zeitler stated based on his research, after the

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EV reaches a speed of 35km/h and above, the tyre and aerodynamic-wind friction can generate adequate noise for pedestrians to sense the car’s presence [3]. Hence, most EV manufacturers have to include a warning signal to their respective EV model to ensure pedestrian safety.

Most of the warning sounds installed in the respective EV focus in sound characters used to alert pedestrian. After the introduction of Nissan Leaf warning sound system to the public, National Federation of the Blind (NFB) in U.S reported that the sound used is unrecognized and still confusing, especially to those who are visually impaired[4]. Addition to the is issue, it was also reported that the sound used only increases the noise level of the environment and sometimes it can annoy people [5]. The warning sound that will be used in EV needs to be managed effectively by emitting it only in ‘needed’ conditions. Hence, this paper will be focusing on the development of a warning sound control system using fuzzy logic control operation. Fuzzy logic is one of intelligent control systems that is widely used nowadays. It can deliver an output that cannot be modelled mathematically, instead based on human experience.

2 Control System Hardware Setup The designated control system consists of four modules controlled by the main controller, Central Processing Controller (CPC). The modules are divided into two units, as per below.

• Control System Input Unit: o Pedestrian Detection Module o Speed Detection Module

• Control System Output Unit: o Database Storage Module o Audio Module

All these modules are electronic-based boards that can be connected and work properly with CPC.For this project, an Arduino ® UNO ATmega328 (datasheet) board is used as the CPC unit. The ATmega328 chip can store 32KB of memory with 0.5KB used for the boot-loader. Below is the summary table and figure for the Arduino UNO board.

Table 1: Arduino ® UNO ATmega328 board specification summary

Microcontroller ATmega328 Operating Voltage 5V Input Voltage (recommended)

7-12V

Input Voltage (limits) 6-20V

Digital I/O Pins 14 (of which 6 provide PWM output)

Analog Input Pins 6 DC Current per I/O Pin 40 mA DC Current for 3.3V Pin

50 mA

Flash Memory 32 KB (ATmega328)

Fig.1: (A) Arduino ® UNO ATmega328 board

(B) Board pin layout The modules involvedare connected to the CPC directly on 14 digital and 6 analog pins for input and output connection of the control system. 2.1 Control System Input Unit Pedestrian detection module and speed detection module are both in the control system input unit. Basically this module involves measuring instrument/sensor in order to identify the input or the condition stated before. For pedestrian detection module and speed detection module, ultrasonic ranging detector mod HC-SR04 distance sensor and hall-effect 3144 sensor are used. Figure 2 below shows both sensors respectively.


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Fig.2: (a) Ultrasonic ranging detector mod HC-SR04 distance sensor (b) Hall-effect 3144

sensor. Ultrasonic ranging detector mod HC-SR04 distance sensor is used to detect the object presence and calculate the distance. The sensor is a non-contact measurement detector that detects from 2 cm up until 500 cm with accuracy of 3 mm. The sensor module is connected via four pins-wire connection; 5V Supply (5V), Trigger Pulse Input (TRIG), Echo Pulse Output (ECHO) and Ground (GND) pin to the CPC board. Below is the summary table for the sensor.

Table 2: Ultrasonic ranging detector mod HC-SR04 distance sensor specifications

Specification Value

Power Supply +5 V DC Quiescent Current <2mA Effectual Angle <15° Ranging Distance 2cm – 500cm Resolution 0.3cm Weight 8.5 gram Board Dimension 45mm x 20mm x 15mm

As for vehicle speed measurement, Hall-effect 3144 sensor is used. The sensor is connected via four wires-pins; 5V voltage supply (Vcc), Digital Output (DO), Ground (GND) and Analog Output (AO) pin. The sensor will measure the speed based on the tachometer instrument concept. The sensor will detect the magnetic field present on rotating the tyre. The rotation speed of the tyre (in RPM) will be converted into vehicle speed. The rotational speed will be calculated based on the time taken for the magnetic field to be detected for one rotation. The speed will be converted based on equation (1) below.

𝑉𝑉 (𝑘𝑘𝑘𝑘/ℎ)

= �(𝑅𝑅𝑅𝑅𝑅𝑅 𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣) × �(𝑇𝑇𝑇𝑇𝑇𝑇𝑣𝑣 𝐷𝐷𝐷𝐷𝑣𝑣𝑘𝑘𝑣𝑣𝐷𝐷𝑣𝑣𝑇𝑇 𝐷𝐷𝑖𝑖 𝑐𝑐𝑘𝑘 × 𝜋𝜋)

100�� × 60

1000

… (1) The final equation to be used after inserting the constant variable, tyre diameter is assumed to be 15 inches or 38.1 centimetres as per below. 𝑉𝑉 (𝑘𝑘𝑘𝑘ℎ) = 0.071817 × 𝑅𝑅𝑅𝑅𝑅𝑅 𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣 … (2)

For this project, the sensor will capture the rotation speed of the computer fan instead of the tyre for comfort and ease as shown in Figure 3 below. From the figure, the position of magnet and the sensor should be perpendicular to each other with a 1.0 – 1.5 cm gap for better detection.

Fig.3: Measurement setup for Hall-effect 3144

sensor with computer fan

2.2 Control System Output Unit After the input has been processed and the selected output is chosen, the sound will be played through a speaker. The first module in the output unit is the data storage module. All the sound data is stored in the Transcend ® 2GB Secure Digital (SD) card. In order to read and extract the sound data from the card, an interface board is used. The SD card uses the Serial Peripheral Interface (SPI) connection to communicate with the CPC to release the sound. SPI is a synchronous serial data protocol used by microcontrollers in communicating with peripheral devices


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quickly over short distances. Communicating using SPI involves three common lines between microcontroller and the peripheral device. The three lines are as per below:

o MISO (Master In Slave Out) – Slave line sending data to Master

o MOSI (Master Out Slave In) – Master line sending data to the peripherals

o SCK (Serial Clock) – The clock pulse which synchronizes data transmission generated by Master.

The SD card interface board by LC ® Studio is used to extract the sound data for this project. The interface board contains SPI connection pins (MISO/MOSI/SCK); one selected card pin (CS), power supply pin (+3.3V/+5V) and ground (GND) pin. SPI interface pins on Arduino ® UNO board are digital pins - (11), (12) and (13) respectively.

Audio module is the second module in the output unit. The sound will be played using an 8 ohm speaker connected to an LM386 IC amplifier module. An amplifier is used because the audio format compatible for Arduino ® UNO ATmega 328 is a Waveform Audio File Format (WAV) file with 16000Hz and 8-bit mono quality. The standard audio quality used in the compact disk (CD) is usually 44000Hz with 16-bit stereo quality. So, because of the low quality of the audio file used, the sound needs to be amplified to deliver better quality signal for detection.The amplifier contains onboard LM386 IC chip, power indicator, speaker wiring block and 10-K ohm variable resistor volume adjust and the board is compatible to interact with Arduino ® UNO board. The connection between CPC and amplifier is through three wire pins; 5 volts of power supply (Vcc) pin, Ground (GND) pin and Arduino interacted pin – (10) - IN. Figure 4 (a) and (b) below show the series of pictures of the control system database storage module and audio module respectively.

Fig. 4: (a) SD Card Interface Board together with Transcend ® 2GB SD card (b) LM386

Amplifier with 8ohm speaker

The input and output unit are connected together to the Arduino ® UNO board. Figure 5 and Figure 6 show the schematic diagram for input and output unit, and Figure 7 shows the complete connection for both units.

Fig. 5: Schematic Diagram of connection for

Input Unit

Fig. 6: Schematic Diagram of connection for

Output Unit


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Fig.7: Complete connected warning sound

control system

3 Fuzzy Control Setup In order to control the sound level emitted by the system, fuzzy logic control is applied. As mentioned earlier, pedestrian detected distance and vehicle speed are the conditions that will be measured and processed in order to produce a compatible output. The condition is explained as per below:

(a) Speed detection setup – The control

system will activate when the vehicle starts to move until it reaches a speed of 65 km/h. After that, the system will be de-activated because the wind and tyre noise will emit enough sound to alert the pedestrian. The speed level will be shown later in the fuzzy membership function.

(b) Pedestrian detection setup – The sensor

will detect the obstacle present and its distance in order to classify the danger level of the situation for the system to emit the compatible sound. Various distances of detecting objects from 0 up to 5.0 meters will determine the compatible engine sound level to be emitted. The distance level will be shown later in the fuzzy membership function.

From inputs and output declaration, the

warning sound system that runs using FLC system consists of sensors for input measurement and audio module for output execution manifested in the form of fuzzy relation between the output and the measured input. The condition statement between inputs and output are the fundamental nature of the

fuzzy control algorithm of the process flow. Figure 8 shows the fuzzy block diagram for the warning sound control system.

Fig.8: Fuzzy logic operating block diagram for

warning sound system

3.1 Fuzzification of the Input Data Fuzzification is a process of changing the real scalar value into fuzzy value or fuzzy set.In fuzzy terms, the crisp input value measured is represented by a fuzzy set after fuzzification process is complete, and the degree of membership function will be determined from the functional graph. The membership function graph for the first and second input; detected distanceand vehicle speed are shown in Figure 9 and 10 based on linguistic variable tablesas per below.

Table 3: Input 1 (Distance detection level) linguistic variable

No. Crisp Input Range

(Distance in Centimetre,

cm)

Fuzzy Variable

Level

Membership Function

Graph

1 0 – 200 (- - 250)

HAZARD Trapezoidal (0-200-250)

2 200 – 300 AVERAGE Triangular (200-250-300)

3 (250 - -) 300 - 500

SAFE Trapezoidal (250-300-500)


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Table 4: Input 2 (Speed detection level)

linguistic variable

Fig. 9: Graph for membership function of

distance detection level

Fig. 10: Graph for membership function of

speed detection level 3.2 Output Membership Function Output for the warning sound control system is the engine sound. Different sound levels will be emitted by the system based on the input

reading measured earlier by both sensors. The output linguistic variables express linguistically to the fuzzy logic control actuators for sound selection. The output linguistic variables for the output are shown in the Table 5 and illustrated in Figure 11 below.

Table 5: Output linguistic variables No Fuzzy

Variable Name

Membership Function Value Range

Membership Function Graph

1 WARNING 0 – 30 Trapezoidal (0-0-20-30)

2 ALERT 20 - 60 Trapezoidal (20-30-50-60)

3 SAFETY 50 - 90 Trapezoidal (50-60-80-90)

4 SAFETY2 80 – 120 Triangular (80-100-120)

5 SAFETY3 110 - 150 Triangular (110-130-150)

6 SAFETY4 140 – 180 Triangular (140-160-180)

7 TURN_OFF 181 Above Trapezoidal (170-190-200-200)

Fig.11: Graphical representation of

membership function for output

Based onFigure 11 above, six different levels of engine sound are used for the control system in order to alert pedestrians. The six signals are WARNING, ALERT, SAFETY, SAFETY2, SAFETY3 and SAFETY4 sound. The last element in the table is TURN_OFF where the control system is completely shut down and no sound will be emitted.

No. Crisp Input Range (Speed

in Km/h)

Fuzzy Variable Level

Membership Function

Graph

1 0 – 40 (- -45)

IN_RANGE Trapezoidal (0 – 40 – 45)

2 40 – 60

INTERMEDIATE Triangular (40 – 50 – 60)

3 (55- -) 65 -

Above

OUT_RANGE Trapezoidal (55 – 65 –

300)


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3.3 Fuzzy Rules After the scalar values of the input variables have been fuzzified, FLC must decide on the action to be taken by selecting the compatible sound level based on the predetermined rule-based system. This phase is called ‘making decisions’ and the decision is made based on the rules and each rule should cover all the possible situations to happen to avoid any error. The ‘IF’ in the rule system indicates the situation or the input and ‘THEN’ describes the response or output for the fuzzy logic control system. Since the FLC processes two inputs at the same time, the minimum correlation inference technique is applied. The logic operation of ‘AND’ is used to connect between the first and second input and it will return the minimum of all inputs.

Table 6: Rule-based system for fuzzy logic controller

Ru

le N

o.

IF

(Sit

uat

ion

)Dis

tan

ce

AN

D

IF

(Sit

uat

ion

) S

pee

d

TH

EN

(R

esp

onse

) O

utp

ut

1 HAZARD IN_RANGE

WARNING 2 AVERAGE ALERT 3 SAFE SAFETY 4 HAZARD

INTERMEDIATE SAFETY2

5 AVERAGE SAFETY3 6 SAFE SAFETY4 7 ANY

ABOVE OUT_RANGE TURN_OFF

4 Sound Level Measurements After the output value is generated, the system will select the appropriate engine sound level to be emitted. Once the sound is released, it should be effective in alerting the pedestrian on the vehicle presence. So, in order to test the effectiveness of the engine sound, the signal will measure its sound level accordingly based on the standard procedure of SAE J2889-1: Measurement of Minimum Noise Emitted by Road Vehicle under topic 7.2, “Measurement of External Sound Generation”.It is measured using microphone/sound level meter at two locations; Front Centre (FC) and Front Parallel

(FP) with a two-meter distance gap. Figure 12 shows the illustrated measurement setup.

Fig.12: Measurement setup test view from top,

side and front

Four tests are conducted as per below. Figure 13 and 14 show the experimental setup of the measurement.

i. First Test – The test is done at FC position. The microphone/sound level meter together with the ‘object’ are placed at the front-centre with a fixed distance of 2 meters and tested at the speeds of 7.2, 14.4, 21.5, 28.7, 35.9 and 43.1km/h accordingly.

ii. Second Test - The second test is done at FC position. The microphone and the ‘object’ are placed at the front-centre with distances of 1.5, 2.0, 2.5, 2.7, 3.0 and 3.5meters and tested at speeds of 30km/h(IN_RANGE) and 55km/h (INTERMEDIATE)respectively.

iii. Third Test – The third test is done at FP position. The microphone along with the ‘object’ are placed at the front-side of the controller with a fixed distance of 2 meters. The system is tested at the speeds of 7.2, 14.4, 21.5, 28.7, 35.9 and 43.1km/h accordingly.

iv. Fourth Test – The last test is also done at FP position. The microphone and the ‘object’ are placed at the front-side with distances of 1.5, 2.0, 2.5, 2.7, 3.0 and 3.5meters and tested at speeds of 30km/h(IN_RANGE) and 55km/h (INTERMEDIATE) respectively.


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Fig.13: Sound level measurement setup at FC

Position

Fig.14: Sound level measurement at FP

position

5 Results and Discussion The measurement value is compared to the previously measured engine noise from the new Mitsubishi Colt 2010 Clear Tech 1.3 and eight year old Skoda Fabia Combi 1.4 Classic. Figure 15 shows the comparison graph between the engine sounds from the warning sound system with engine noise at FC position.

Fig.15: Comparison of engine sound level

from controller with engine noise level produced by ICE vehicles at FC position

Based on the plotted graph above, the

total noise level generated by ICE vehicles increases as the velocity/engine speed increases, whereas the artificial engine sound level emitted by the control system is nearly constant in the 65.0 - 66.0 dB range.The increase of noise level from ICE vehicles is greatly based on the engine noise. As for artificial engine sound, the signal is emitted at a nearly constant level and at a detectable range.This approach is applied in order for the system to deliver an EV artificial engine sound at optimum condition; to release a signal during unsafe situations and conditions, only to maintain the discretion of an EV and avoid noise emission. As for measurement at FP point, Figure 16 shows the comparison graph.

Fig.16: Comparison of engine sound level with ICE Total vehicle noise level and tyre

noise at FP position

As for this measurement, the tyre noise generated by the vehicle is also shown in the


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graph, since it plays an important role in alerting the pedestrian.From the graph, it shows that the tyre noise level exceeded engine sound level at the speed of approximately 22km/h. The consistency of the tyre-friction noise level produced as the vehicle moves, especially at higher speed, indicates that the tyre noise can be used to notify pedestrians on the presence of an EV and replace the artificial engine sound emitted by the control system as an alert signal.Tyre-friction noise is still not dominant in the beginning as it generates very low noise level. So, in this condition, the engine noise is needed to cover the ‘hole’. Thus, the engine sound produced by the control system can support the lack of noise produced by the tyre.

The FC and FP position indicates the pedestrian location and distance in an actual situation and condition. Figure 17 below shows a comparison histogram between the measurement value at FC and FP position for an artificial engine sound level produced at every signal level applied.

Fig.17: Engine sound level measurement

comparison From the figure, the artificial engine sound level keeps decreasing because as the vehicles speed-up, the noise generated from tyre and wind friction begins to dominate. So, the SAFETY4 signal doesn’t need a higher sound level to alert the pedestrian. The results show that almost all the measurements at FC generated higher sound levels as compared to the measurement done at FP.The sound levels

at both positions are still acceptable as they are still within detecting level range and below the harmful level.

6 Conclusion In conclusion, the development of a warning sound control system that runs using fuzzy logic system operation is to control the release of an artificial engine sound. In order to keep the ‘quiet’ character of an EV, fuzzy logic is applied to make sure the output is only emitted in certain conditions to ensure the pedestrian safety and maintain the environmental noise level. The control system has already been tested and its result has been compared respectively. Based on the test results, the system functions appropriately using fuzzy logic to alert the pedestrians.

Acknowledgement The authors wish to thank the Universiti Teknologi Malaysia (UTM) for providing the funding and facilities for conducting this research. References:

[1] U.S. Department of Transportation National Highway Traffic Safety Administration (September 2009), Incidence of pedestrian and bicyclist crashed by Hybrid Electric passenger Vehicle; Technical Report. DOT HS 811 204.

[2] Nyeste, P. and Wogalter, M. S. (2008). On Adding Sound to Quiet Vehicle. Human Factors and Ergonomics Society, Inc. Proceedings of the Human Factors and Ergonomics Society 52nd Annual Meeting. Call number 10.1518/107118108X352049.

[3] Zeitler, A. (2012). BMW Group, Psychoacoustic Requirements for Warning Sound of Quiet Cars, Publish on SAE International Journal, Call Number doi: 10.4271/2012-01-1522.

[4] Jim Motavalli (17 June 2010), Blind Advocates ‘Disappointed’ in Nissan E.V. Sounds for Pedestrians. Publish in New York Times, from


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http://wheels.blogs.nytimes.com/2010/06/17/blind-advocates-disappointed-in-nissan-e-v-sounds-for-pedestrians/, (Retrieved at 18/02/2014).

[5] Jim Motavalli, (21 June 2010), Anti-Noise Activists Oppose Sounds for Electric Cars. Publish in New York Times, from http://wheels.blogs.nytimes.com/2010/06/21/anti-noise-activists-oppose-sounds-for-electric-cars/?_r=0, (Retrieved at 18/02/2014).


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