Vehicles Using Data Acquisition And Fuzzy Logic Computer Science Essay

Automotive industries are working on bettering fuel efficiency, stressing to develop intercrossed vehicles that are better in public presentation every bit good as fuel efficient. Focus on renewable alternate beginnings of energy, is the cardinal characteristic. The research work in this paper is aimed at bettering the fuel efficiency in Hybrid vehicles by capturing the information from the ECU on RPM, Speed, Fuel Pressure, Load, Timing progress, etc, from the CAN port of the vehicles. Rigorous accent on the ECU information from the engine is the alone characteristic of this research.

The trials were conducted on different theoretical accounts from different makers like Toyota PRIUS Toyota HIGHLANDER, and Ford ESCAPE intercrossed vehicles. Dewetron Data analyser was used to capture the information, and processed for preciseness utilizing logical regulations with mathematical expression in Dewesoft, and processed in MATLAB where the fuzzy logic conditional regulations were implemented to better the fuel efficiency by on-line informations monitoring

Keywords: Online Data Analysis, Fuzzy logic, Matlab, Dewesoft, Offline information processing


The automotive makers are spread outing their intercrossed vehicle section intricately and extensively.

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Each maker has their ain specific functional intercrossed vehicle. For an illustration, The Toyota Prius and the Highlander have an wholly different manner of operation when compared to the Honda Insight and CRZ. The Toyota section of vehicles have a scheme that enables vehicles to run on battery till the vehicle achieves a 20mph and above velocity for the engine to take the burden and charge. This once more is dependent upon the rider burden in the vehicle.

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Unlike this the Honda section of vehicles have the engine programmed to get down first and take up the initial burden, and after a certain velocity have the motors assist the engine and later cut off until farther power is in demand. In either of these ways there is a scheme and computing machine plan behind to regulate the working. These complex computing machine plans can be simplified and made to work utilizing fuzzed logic regulations after the ECU information has been captured and analyzed.

The fuzzed logic regulations are based upon certain specifications that makers have set in the vehicle. Previous research in this subject does non acquire in to great inside informations of ECU informations acquisition. In this research work, more figure of input parametric quantities, which are Fuel force per unit area, intake air temperature, burden, RPM, Speed, Exhaust gas temperature, O detector reading, absolute throttle place, short and long term fuel trim, multiplex absolute force per unit area, and coolant temperature, and to give a precise determination of the commonly aimed end product parametric quantity called 'efficiency proctor ' based on IF THEN conditional regulations, is where the usage of fuzzed logic is legitimated, and emphasized. 'Milton Martinez [ 1 ] in his paper 'Fuzzy Logic Controller for Two manners Parallel Hybrid Electric Vehicle ' says that fuzzed logic is a manner of thought. As an sweetening to the work done in my old paper titled 'Use of Fuzzy Logic in Hybrid Vehicles for Bettering Fuel Efficiency [ 2 ] by Individual Component Control ' the engine information is besides captured analyzed and so command scheme is implemented.

Previous work was curtailed merely along with lesser input parametric quantities for supervising efficiency. Work in upbringing complex algorithms for supplying feedbacks to the driver was besides conducted. Analyzing the information from the ECU of the engine with specific functional parametric quantities which can further heighten the control scheme of the intercrossed vehicle has been the cardinal country of research work. The experimental apparatus and executing of work in stages, its demand and justification, and the result of the experiment, are described in item in the undermentioned subdivisions of this paper.

2. Experimental Apparatus

2.1 Calibration and Setup of DEWE 43V informations analyser

The DEWETRON DEWE43V informations analyser is an instrument which is capable of capturing informations from different detectors. The nucleus working system of this instrument is package called DEWESOFT. The package is capable of informations capturing, arranging and analysis by itself. Datas in the signifier of electrical signals from the detectors are acquired in to the DEWE 43V and stored in to the difficult disc of the computing machine. The experiment is conducted in 3 stages.

The first 1 is to look into the ego capableness of the DEWETRON and DEWESOFT. The 2nd stage is to look into the readability of the acquired informations in to MATLAB and connote fuzzed logic regulations in the offline manner. The 3rd stage is to make the 2nd stage of the experiment on the information which is coming out live from the ECU of the vehicle, which is called on-line information analysis.

In the first stage the information inputs that were available were stored in the signifier of channels. Each channel gives an end product of a certain electromotive force scope from each detector on the engine, and this is converted in to graphical informations for better visual image. In the first phase, the DEWE 43V was calibrated to the demands of this experiment. The key demands which were needed are that it should supply us with values of specific parametric quantities like Manifold Absolute force per unit area, Absolute Throttle Position, Intake air flow Temperature, Exhaust gas temperature, Coolant temperature, Timing progress, Short and long term Fuel Trim, Engine RPM, and Vehicle velocity.

The numerical values of these parametric quantities were obtained in the upper limit and minimal scopes. The CAN ( command country web port ) was connected to the OBD ( out bound informations ) port of the vehicle. After the channels were assigned their rubrics the intercrossed vehicle was turned on. The bend on manner is the point where the information from the ECU Starts flowing in through the CAN port.

2.2 Evaluation and Basic Formulation

Dewesoft has an option called offline math, which really helps us in puting up regulations. The logic regulations are assigned based on the recorded input values, and numerical restrictions are provided. The maximal bound to which the incurred value can promote is shown here. Based on this value we can delegate a control regulation which will give is a triggered signal when the detectors obtain a signal that is outside the bound of our boundary. The logic regulation assigned to regulate the vehicle velocity v/s the Engine RPM are

IF `` ENG RPM '' =0 AND '' VEHICLE SPEED '' & gt ; 0,1,0 ( 1 )

The regulation here triggers a additive map in the Graphic screen of dewesoft when the vehicle is in gesture and is being propelled in BATTERY entirely manner. The 0 and 1 are set trigger values which can be manually adjusted. Similarly the fuzzy regulation implemented to bespeak the point where the engine takes over is represented with

IF `` ENG RPM '' & gt ; 0AND `` VEHICLE SPEED '' & gt ; 20, 1,0 ( 2 )

This regulation triggers a additive map on the in writing screen of dewesoft that will demo the ENGINE pickings over manner. Here once more the trigger values are set between 0 and 1.

The Figure below shows the in writing show scene supervising the vehicle in the inactive and moving conditions. Rule1 is shoed when triggered by the solid Green trigger line and Rule 2 is shown by the solid orange line severally as indicated in Fig.1.

Fig.1 Dewesoft Graphic Display with Rule 1 and Rule 2 triggered

2.3 Advanced preparation & A ; Experimentation in Offline manner

Once the work foundation is set up to trip the basic manners of operation of the intercrossed vehicle, the advanced information is programmed in the Dewesoft for rating. Here the parametric quantities are set to obtain triggered maps which exceed the set values at different point of clip during vehicle runtime.

To accomplish this, the vehicle was operated in Driver entirely manner, driver and individual rider, driver and two riders, driver and three riders, and driver and four riders severally. This was done so as to obtain the assorted burden governed conditions for the engine take over clip. The regulations assigned for other conditions are

If TA & lt ; 1deg AND ENGINE RPM & lt ; 1500,0,1 ( 3 )

Engine Power ( IHP ) = ( 2pNT ) /60 Nm ( 4 ) [ 6 ]

Rule ( 3 ) and Formula ( 4 ) are assigned to cipher the flicker timing, as all vehicles Gasoline ( or ) loanblend, are equipped with a mechanism which will automatically progress the current excitement given by the distributer to the flicker stopper of the engine, so as to cut down the phenomenon of knock. All makers have their ain specific clocking progress and idiot mechanism harmonizing to the route and load conditions of the vehicle. Besides the driver behaviour is an of import facet.

Torque end product from the engine is available from the engine CAN informations, but so in general the expression for ciphering the torsional force is explained by Formula ( 4 ) , Where p = 3.14 which is a changeless, N= Engine RPM, T = torsion in NM and IHP = indicated Equus caballus power. The indicated Equus caballus power is a signifier of Brake Equus caballus power and the deductible frictional Equus caballus power. Brake Horse power is the Power available at the flywheel of the engine. Frictional Equus caballus power is ever a negative value. The peak values of assorted detector readings which are preset by the makers are shown in Table 1.

The minimal value and maximal values are indicated. This is a farther justification for the regulations assigned afterlife. Rules assigned are within the boundaries of upper limit and minimal values. Any numerical value which exceeds these regulation boundaries at any point of operation of the vehicle, due to any fortunes shall trip a additive graph on the show which is an error indicant or a warning.

Parameter With Units Minimum Value Maximum Value

Engine RPM 0 7500

Vehicle Speed MPH 0 150

Intake Air Temperature Deg C -40 115

Coolant Temperature Deg c -40 115

Manifold Air Flow gm/sec

Load value %

Fuel trim %

Clocking Advance Deg

Absolute Throttle Position %

Oxygen Sensor electromotive force V -50





-5 655.35






Table1. Parameters with minimal and maximal readings

Further the regulations including other parametric quantities are

IF RPM & gt ; 3000AND Speed & lt ; 40 and MAF & lt ; -50, 0,1 ( 5 )

IF Load & gt ; 100AND fuel trim & lt ; & gt ; -1 and ATP & gt ; 100, 0,1 ( 6 )

IF OSV & gt ; = -1 AND ATP & gt ; & lt ; 25 0,1 ( 7 )

Rule ( 5 ) justifies for defects happening at the clip of lower air intake or a defective air consumption system. Rule ( 6 ) is used to observe the point where the engine delays to get down up at maximal load status. This may ensue in over discharge of battery current and thereby this regulation helps in avoiding the same. Rule ( 7 ) maps the O detector electromotive force. This is needed for observing the fumes degrees from the Engine and besides helps in observing if the O detector has failed. At this clip the ECU shall non let the engine to get down and therefore the battery will hold to take the full burden and it may dispatch at a really short running period.

The restriction of Dewesoft is that on the application of logic regulations, it can merely trip a additive map which can do the operator understand that at this point the deformation or divergence from the usual running rhythm of the Internal burning Engine is taking topographic point, and further it can non straight pass on with the ECU of the engine to readapt parametric quantities at the point of defect. Taking this in to consideration, we now incorporate the same informations obtained from Dewesoft and import it in to MATLAB. The fuzzed logic tool chest is used to further heighten the efficiency of the research work, which is covered in the undermentioned subdivision.


In the 2nd stage of the research, as mentioned above, the informations acquired from Dewesoft was imported to Matlab. Using the fuzzed logic tool chest, and Mamdani method, logic regulations were assigned to the input parametric quantities retaining their values precisely the same as that acquired. A plan was written to name the map from Dewesoft. From dewesoft the files are exported to matlab by the 'export ' map which makes the file clear by matlab. The plan would so put to death the fuzzed logic regulations on the input parametric quantities. Two end product parametric quantities are assigned to the map which are, Control, and Execute Respectively. Gaussian rank map was assigned to all the input every bit good as end product parametric quantities. The scopes of each map in the input variables are as provided by the makers, but the end product parametric quantities are ranged between 0 and 1. The control [ 3 ] and execute manner has three several rank maps which are NIL LOWER AND ACCELERATE, and NIL, CONTROL, AND STOP. These MF 's are assigned for the easiness of plan to take a logic determination at the clip of mistake and is set to give a feedback every bit good as execute the map in the simulation manner.

Fig.2 Fuzzy Logic interface, and surface position

Fig.2 shows the consequences of fuzzification of the input parametric quantities in MATLAB and the surface diagram for the same. The defuzzified consequences are in the end product parametric quantities which are control and Execute. The plan is capable of reading the particular file which has the stored informations in the offline manner. The trials were conducted within the scope get downing at 20mph velocity to 80mph velocity and the readings of all parametric quantities that changed or showed a warning signal as mentioned earlier were noted diagrammatically. To prove the efficiency of the warning map, the vehicle was accelerated to velocities of 80mph for a little interval of clip. The map, as desired showed warning signals triggered on the in writing screen of dewesoft, every bit good as a written prompt shown on the Fuzzy logic screen. The factor which prompts the usage of logic regulations integrated with the acquired informations from dewesoft is the position interface which is non an added characteristic in dewesoft. This once more justifies the usage of Matlab.

The Fuzzy logic regulations that were assigned on MATLAB are shown in Fig.3 Here all the input parametric quantities assigned are shown in the signifier of xanthous columns and the defuzzified consequence is shown with a bluish taging intend at the right corner of the figure. The Red colored indent is the show parametric quantity which shows end product severally. And therefore the offline informations processing utilizing dewesoft ( independently ) , and matlab integrated informations processing was achieved.

Fig.3 Fuzzy Logic Rules With Limits and Output.

Therefore the experiment has so far proved success in capturing the offline informations and treating it. Further as mentioned is the Third and concluding stage of the experiment which is designed to import the exported informations live into matlab and execute the trials on the intercrossed vehicle at assorted velocity and burden conditions. Apart from this, the trial has been carried out on different theoretical accounts like the athleticss public-service corporation intercrossed vehicle FORD ESCAPE Hybrid, and besides the Toyota HIGHLANDER loanblend severally. For the same set of regulations mentioned earlier, and on the same trial conditions.

4. Processing Online Data

In the 3rd stage of the experiment, as informations supports fluxing in to the dewetron informations analyser, it is at the same time exported to matlab. This information is so linked to the matlab plan as a unrecorded cyclosis file. This is so delivered to the set of fuzzed logic regulations assigned to each input parametric quantity. All this is really similar to the work done in old phases, but for informations being readily captured from the OBD2 port of the intercrossed vehicle to the CAN port of the informations analyser and exported to Matlab. During this trial stage the same set of experimental tallies are conducted on the vehicle get downing from a running velocity of 20Mph to 70 Mph. This covers the vehicle get downing from the parking infinite to driving in the street conditions, and so come ining the main road. This is done to in turn increase the velocity of the vehicle. The vehicle was intensely tested on the main road where the unifying velocity was 45mph and the minimal velocity was 55Mph. the maximal bound allowed on main road conditions is 70Mph.Here when the vehicle is subjected to sudden acceleration to take the needed velocity from 55Mph to 70Mph difficult acceleration is required and in this velocity the intercrossed vehicle is designed to run in double manner, where in the electric motor powered by the battery provides excess tortuosity force to the wheels along with the tortuosity force of the engine. When the unrecorded information is captured, the regulations assigned here would help the system to accommodate the throttle degree to take down itself harmonizing to the assigned regulations. Fig.4 shows the online informations that is fluxing along with the regulations assigned to cut down the engine attempt for impeling the vehicle with regard to assorted driver behaviours. These include normal low and difficult acceleration forms.

Graph 1. Eng RPM, and Speed for 15sec runtime

Graph.1 shows the first 400 sec run clip of the vehicle where in after deduction of fuzzy regulations, the revolutions per minute v/s velocity was non fluctuation. This is in the fake manner where the fuzzy regulation would automatically set the RPM if the driver over accelerates the vehicle. In the instance of over acceleration, more fuel flow to the engine is obvious. Reducing the same is the premier motivation.

Fig.4 Online Data processed with regulations triggered

Along with connoting expression and regulations for the Engine, for ciphering values on both offline every bit good as online manners, expression to cipher the torsion of the electric motor was used in order to equilibrate the control scheme on motor and battery current operated propulsion side, every bit good as the engine propelled side severally. The torsion of the electric motor is calculated by utilizing the relation

t= ( 5252*HP ) /N ( 8 ) [ 7 ]

Where T = torsion, 5252 is a numerical invariable, HP = power of the motor in Horsepower, N= revolutions per minute of the motor. Besides the torsion is calculated utilizing another relation

t= ( 120*F ) /P ( 9 ) [ 8 ]

Where T = torsion, F = supply frequence in cycles/sec, P = figure of motor twist poles. Different expressions are used because some makers will hold different informations end product, and utilizing different expression eliminates the factor of mistake in computations.

5. Consequence and Discussion

The experimental apparatus was besides simulated utilizing LABVIEW package where manually adjustable parametric quantities were set to imitate the existent clip state of affairs of riders busying the vehicle. This was done to see the alteration in burden conditions and analyze the consequence of increased burden over public presentation of the vehicle under assorted route conditions and velocities severally. Variables were besides assigned on the LABVIEW deck to formalize the driver behaviour inputs like over acceleration, under acceleration, difficult breakage, and rearward gesture. Boolean map was used to bespeak the mistake if shown in the simulation manner. Fig.5 shows the deck where in all controls and variables were assigned to imitate the state of affairs. The Dials shown in the deck are similar to those in the existent car. [ 3 ] Additional dials are assigned to supervise the motor torsion and burden conditions which are non available as standard equipments in the instrument panel of vehicles.

Fig.5 LABVIEW deck

Fig. 6 shows the DEWESOFT deck where in at clip intervals the regulations implemented on the online informations takes topographic point and the graphical end product show is obtained. Here the experimental trial tally on FORD ESCAPE HYBRID is recorded for assorted tallies get downing from 20mph and in turn increasing the velocity in 10 MPH increases. The maximal velocity travelled was 75mph on the FORD vehicle. It is non a safe pattern even for the intent of proving to speed up the intercrossed vehicle above the velocity bounds because it may do unwanted harm to the motor control and propulsion system. Hence the 75mph was maintained as the peak bound of acceleration during the full scope of experiments conducted on each category of vehicle severally.

Fig.6 Fuzzy regulations implemented on unrecorded informations obtained from FORD ESCAPE

Fig. 7 shows the same experiment with same set of processs and same set of logic regulations applied for the Toyota Prius [ 5 ] which belongs to the Economy section. Here the blue lines depict the mistake trigger and the ruddy is the automatic rectification in the simulation manner. This is farther for communicating with the driver to hold a cheque on his manner of operation every bit good as to car rectify the system to stabilise the values obtained.

Fig.7 Toyota Prius with trigger and rectification graphs

In the concluding measure of the research conducted the same set of processs were repeated on the Toyota HIGHLANDER. The consequences obtained after the trials on SUV 's and Economy size autos of different makers are discussed in item in the undermentioned subdivision.

Fig.8 shows the Trigger manner and rectification graphs which are for the remainders conducted on the Toyota Highlander. Bing a mid size SUV [ 4 ] in it s section, the Toyota Highlander loanblend has potentially lesser usage of battery propulsion and the motor has lesser torsion end product compared to the little section and the Ford section of vehicles. [ 10 ] This is done by the makers, for maintaining the vehicle public presentation curves to the upper limit. Yet usage of the electric motor propulsion system in the HIGHLANDER does assist in cut downing the fuel ingestion particularly during the sail manner where the operation is carried out on a big graduated table in the double operating manner which lowers the propulsion burden to the engine.

Fig.8 Toyota Highlander with trigger and rectification graphs

Here the white lines represent usage of engine propulsion. It is seeable that at lower cases in comparing to the Prius, and Ford flight, the engine has to take bid of impeling the vehicle. The bluish lines are trigger for the system to place over acceleration, and the green trigger lines depict the control. Control is triggered during the beginning and at the terminal of the trial tally of the vehicle severally. The xanthous and pink bars are excitation current triggers that are shown when the vehicle is overloaded and the LED show at the underside of the screen shows a warning of harm to the system if operated above these regulation ranges. This is done to supply a feedback to the operator and cautiousness the control system at the same time. The response clip of the vehicle in fake manner with regulations applied, is much larger when compared to the normal operation. Thus it justifies the usage of fuzzed logic regulations in the government of fuel economic system, and energy preservation.

6. Decision

By utilizing the information acquisition system and implementing the Fuzzy regulations the control of fuel ingestion can be significantly reduced. The battery propulsion clip can be maximized. Small section vehicles have lesser mistakes triggered when compared to the SUV scope of vehicles. The burden value plays a really critical function in finding the return over clip for engine in impeling the vehicle. Simulated consequences show addition in fuel economic system when compared to the standard manner of operation of intercrossed vehicles under assorted driver and burden conditions. The fresh accomplishments are that the informations can be processed as and when it is acquired and disciplinary regulations can be applied for simulation outright which minimizes the mistakes, and maximizes the public presentation and fuel efficiency. The work carried out here is in a fake manner which limits direct communicating with the ECU of the vehicle. Further work can be done in this facet where the regulations assigned may straight be communicated to the running vehicle.

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Vehicles Using Data Acquisition And Fuzzy Logic Computer Science Essay. (2020, Jun 02). Retrieved from

Vehicles Using Data Acquisition And Fuzzy Logic Computer Science Essay
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