Challenge

Project 1

MMohammed Asad Ali21 Dec 2022

TITLE:

Deals with the development of a forward energy-based fuel consumption model of a conventional vehicle.

AIM:

Build Simulink model using blocks and complete the vehicle models.
Writting scripts in MATLAB to load all the variables required for running the Simulink model.
Tabulate the fuel economy numbers for UDDS and HWFET cycle.
Plot the time trace for engine, transmission input, transmission output and wheel speed on one plot.
Plot engine torque, engine power, fuel rate and fuel consumption using subplots.
Plot accelerator and brake pedal, gear command, vehicle desired and actual speed, vehicle distance using subplots.
Plot the engine operating points on the engine fuel map.
Generate the plots for both drive cycles.
Study of Conventional vehicle simulink model and creates graphs.

Conventional Vehicle:

Conventional vehicles use an fueled by gasoline or diesel to power the wheels. In this the heat engine uses the principle of heat release through combustion of a fuel with an oxidizer in a combustion chamber to create expanding gases which pushes against a piston creating forcce and torque output. Conventional vehicles use an internal combustion engine fueled by gasoline or diesel to power the wheels. Electricity is used for some accessories, but is not used to move the vehicle.

In this vehicle engine converts chemical energy stored in the fuel into heat which is used to produce useful work. Typically use fossil fuels like petroleum (petrol and diesel).

The most popular type of engine is used in this system is 4 stroke reciprocating engine. Engine in cyclinder process can be represented by ideal thermodynamic cycles such as Otto cycle, Diesel cycle. Otto cycle is a ideal cycle thet describes the cycle process for spark ignited SI engine. and the Diesel cycle is the ideal cycle thet describes the cycle process for a compression ignited CI engine.

Energy and performance of vehicle:

In this system trge power demand on the wheel need to instant supplied by the powertrain. In otyher word the power coming from the powertrain dictates the power demand on the wheels (as per performance viz, acceleration, speed etc is determined by it.

In tis vehicle the powertrain is consists of an engine connected to the wheels through a transmission therefore the power comes from the engine through the transmission.

Conventional powwertrain efficiency:

The engine generates power by converting chemical energy stored into the mechanical energy by the process of combustion. This process of energy conversion also has an efficiency and the energy input is in the form of fuel consumption.

Instantaneous fuel consumption is given as

m.f=Peηe⋅ηt⋅QLHV"> mf=Peηe⋅ηt⋅QLHV

m.f=Peηe⋅ηt⋅QLHV"> m.f=Peηe⋅ηt⋅QLHV

Where,

mf = mass flow rate in [kg]

pe = Power of engine

pt = power of transmission

nt = efficiency of transmission

ne = efficiency of engine

QLHV = it is th ecalorific value/ lower heating value of fuel.

In this system of IC engine if tge power at the wheels is zero the engine is idling and still consumes some fuel to overcome the engine drag loss and keep it spinning this is the idling fuel consumption and know as idling process. Above mentioned formula is applicable only when the wheel power is strictly power. When the wheel power is negative either th engine is disengaged and idling or the engiune is engaged and providing some engine braking due to the engine drag losses.

The engine is consuming fuel when it is idling but even for engine braking the engine might be consuming some fuel.

The fuel consumption not only depend on the power requirement at the wheels but also is required to generate power to run the various engine and vehicle accessory loads.

System Dynamics:

It deals with the modeling of multi domain systems through the use of a unified approach based on a ordinary differential and algebraic equation describing the static and dynamic effects. each systems has are two generalised variable

Efforts: It is generalised force variable. ex- torque, force, voltage, heating value.
Flow: It is the generalised flow/velocity variable. ex:- velocity, current, mass flow rate.
Power: It is the product of the effort and flow variable. It is interchangeable between energy domains.

Energy based models- Element models:

To models the internal physical properties of the element and the losses there are basically two kinds of approach.

Map based models: Elements are modeled using maps which characterized the relationship between the input and output , output and losses etc.

Analytical models: Elements are modeled using physical equation that represent the internal dynamics. These are the two extremes most models are a combination of the two with some dynamics models analytically and for some using maps.

In this model We use Forward Model:

It also known as dynamic simulation. Dynamic models are used for detailed simulations. In this model force/torque/power is the input and the vehicle state variable velocity and acceleration are outputs. The models and simulatios are more realistic due to phhysical casualty hence appropriate for control development. There is feedback loop to meet the desired vehicle state. It is mainly used to simulate a vehicle on a driving cycle to analyse the dynamic behaviour of the system helps significantly in control development.

MATLAB scripts:

Used to load all the variables required for running the Simulink model and for post-processing. The drive cycle selection is also done here. Before running the Simulink vehicle model, run this initialization script.

%% Skill-Lync -> Hybrid Drives Development

% Project 1 - Conventional Vehicle Model

% By - Mohammed Asad ali

% Description:

% Initialization File

% Initialization file for loading the variables for the Conventional vehicle simulation model

% Vehicle Parameters

M = 1500; % Vehicle weight [kg]

Cr = 0.015; % Rolling resistance coefficient

Rw = 0.35; % Rolling radius [m]

Cd = 0.33; % Drag coefficient

Af = 2; % Frontal area [m2]

rhoa = 1.225; % Air density [kg/m3]

Vwind = 0; % Wind velocity [kmph]

g = 9.81; % Gravitational acceleration [m/s2]

theta = 0; % Slope [deg]

myu_road_tire = 1; % Coefficient of friction between tire - road interface

% Powertrain Parameters

FDR = 3.73; % Final Drive Ratio / Diff Ratio

DiffEff = 0.99; % Differential efficiency (assumed independent of operating conditions)

NumGear = 4; % Number of gears in transmission

TransGear = [1:NumGear]; % Transmission Gears

TransRatio = [3.06, 1.62, 1, 0.7]; % Corresponding Transmission ratios

TransEff = 0.90; % Transmission efficiency (assumed independent of operating conditions)

TC_SR = [0, .5, .6, .7, .8, .87, .92, .94, .96, .97, 1]; % Torque Converter Speed Ratio (Matlab default) -> TurbSpd/PumpSpd

TC_TR = [2.232, 1.5462, 1.4058, 1.2746, 1.1528, 1.0732, 1.0192, 1, 1, 1, 1]; % Torque Converter Torque Ratio (Matlab default) -> TurbTrq/PumpTrq

TC_Cf = 1e-4 * [.72558, .66322, .63463, .59042, .51331, .4144, .29287, .22444, .12186, .04386, 0]; % Torque Converter Capacity Factor (Matlab default) -> PumpTrq/PumpSpd^2 [Nm/rpm^2]

load Vehicle_Data/Engine_data_1_9L_Saturn_95kW

load Vehicle_Data/Trans_Shift_map

% Driver Parameters

Kp = 10; % P gain for driver PI model

Ki = 1; % I gain for driver PI model

Drive_Cycle_Select = 2; % Drive cycle selection

% Drive cycle values in m/s

if Drive_Cycle_Select == 1

load DriveCycles/UDDS;

elseif Drive_Cycle_Select == 2

load DriveCycles/HWFET;

elseif Drive_Cycle_Select == 3

load DriveCycles/FTP;

elseif Drive_Cycle_Select == 4

load DriveCycles/US06;

elseif Drive_Cycle_Select == 5

load DriveCycles/NEDC;

elseif Drive_Cycle_Select == 6

load DriveCycles/WLTP;

end

time_end = cyc_time(end);

vel_end = cyc_vel(end);

Now let we Analyze the model:

lets see the simulink model of conventional vehicle,

In this project, a typical midsize car with a 1.9L engine and a 4-speed automatic transmission is considered. Data is taken from ADVISOR data base developed by NREL, USA. Transmission shift map and controller is taken from Matlab/Simulink example database.

The model developed has the capability to simulate the vehicle on different drive cycles like UDDS, HWFET, FTP, NEDC, etc.

Project1_InitFile.m: is a Matlab script used to load all the variables required for running the Simulink model and for post-processing. The drive cycle selection is also done here. Before running the Simulink vehicle model, run this initialization script

Driver Model:

It is modeled using a Driver block, which is a basic PI controller that generates accelerator and brake pedal outputs to match the desired and actual vehicle speeds.

In this we use drive cycles fro the based on these results of fuel consumption and emissions the vehicle gets certified and it also forms a basis of uniform asessment. It is based on time, velocity then its all integrate and creates output accelration pedal and brake pedal.

Graph created from Scope in Driver Model:

Control Model:

The Controller block is the brains of the vehicle, representing the electronic controls units. It is based on the driver inputs, it generates desired engine torque, desired transmission gear and desired brake torque. These inputs are fed to the block which represents the drivetrain of the vehicle.

1. Engine control Unit:

Graph created from scope for Engine control Unit:

In this control model We use engine control unit in that give input Engine speed through creating look up table. in that table we give input Maximum torque and map speed then also create 2nd look up trable in that give input ct torque and map speed then it substracts and multiplied. the engine speed also give to ideal contro speed to integrate from this we meet output of engine required torque.

2. Transmission Control Unit:

This unit we create steadfy flow variable state for controlling of gears of vehicle as in ascending and descending order with respect to time, speed, distance. and give input to this steady state is vehicle speed in m/s to mph

3. Brake Control Unit:

In this we will calculate the brake torque and then all three control signals output is give to powertrain.

PowerTrain:

The powertrain encompasses every component that converts the engine's power into movement. This includes the engine, transmission, the driveshaft, differentials, axles; basically anything from the engine through to the rotating wheels.

1. Engine:

In this model we used map based model to model the fuel consumption of engine. It is based on maximum Torque and minimum torque from the controller. and then it saturated torque and give output then saturated torque and speed map give fuel consumption takes the speed and torque then the output goes into saturated then its integrated.

Graph created from scope for Engine Torque:

2. Transmission:

In these model we take engine torque, gear selector from controllerb for transmission of gear ratio. The ratio is multiply by transmission input torque and then the transmission efficiency positive torque and negative torque gives transmission input speed. In this case torque converter connects the engine to transmission it is part of transmission. In it basically engine dynamics and that is decoupled.

Graph created from scope for;

1.Transmission in speed 2. Gear ratio 3. Differential Ratio.

3. Differential:

In this taking input is the transmission output then its going to the differential ratio multiplied the output and input torque then meet axle torque. The axle torque is a lower than the transmission torque and differential ratio.

4. Wheels:

In this model we take the axle torque from differentail and brake torque from controller. it is substracts and multiplied rolling radius and velocity then we meet the tractive force from axle and brake torque. and the wheelspeed is meet using velocity.

5. Vehicle Dynamics:

Graph created from scope for 1.Road load 2. Acceleration 3. Tractive force

In this takes tractive force and road load. It is dictates vehicle speed based on tractive force and road load.

Graph from scope for Road load;

The dynamic equation for calculating road load is given by;

MeqdVvehdt=ηtλtotRwTm-(12p⋅A⋅Cd⋅(Vveh+Vw∈d)2+Cr⋅M⋅g⋅cos(θ)+M⋅g⋅sin(θ))">MeqdVvehdt=ηtλtotRwTm−(12p⋅A⋅Cd⋅(Vveh+Vwind)2+Cr⋅M⋅g⋅cos(θ)+M⋅g⋅sin(θ))MeqdVvehdt=ηtλtotRwTm-(12p⋅A⋅Cd⋅(Vveh+Vw∈d)2+Cr⋅M⋅g⋅cos(θ)+M⋅g⋅sin(θ))

where,

p = air density [kg/m^3]

A = Frontal area [m^2]

Cd = drag Coefficiend

Vveh = velocity of vehicle [m/s^2]

Vwind = velocity of wind [m/s^2]

Cr = rolling resistance coefficient

M = mass in [Kg]

g = gravity.

Then we calculate and integrate then we meet acceleration of vehicle and velocity of vehicle it mean vehicle distance and vehicle speed.

lets now conclude the result;

We compute the Fuel economy numbers using UDDS and HWFET cycle:

1 UDDS Drive cycle:

1 Time VS Speed: In this the speed is increasing continuously.

Conclusion:

We clearly observe from the graph, how the speed is increasing according to time in this,

Total Power is increase slowly as increasing rolling resistance, aerodynamic drag and inertial resistance.

2. HWFET Drive Cycle:

Time VS Speed: Speed is continuosly increase wrt time:

Conclusion:

Total Power is increase slowly as increasing aerodynamic drag and inertial resistance. but in this drive cycle bad impact occurs due to decreasing rolling resistance;

Result:

As comparing both cycles we used to HWFET drive cycle. Its best for asses of fuel consumption and Pollutants emission of vehicle in a normalised way.

Now For creating Plots we use MATLAB coding So the coding will give down;

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%% Skill-Lync -> Hybrid Drives Development

% Project 1 - Conventional Vehicle Model

% By - Mohammed Asad Ali

% Description:

% The following file is to used with the Initialization file and Simulink

% results for post-processing of the data.

% The file is incomplete, has some examples of how the data is imported

% into a readable format, data analysis and plotting examples.

% Based on the project task, use commands in a similar format as given

% below, to analyse the data and generate required plots.

% You can change variable names if desired

% Post-Processing File

% File for post-processing the Conventional vehicle simulation model data

% Renaming variables

Time = out.tout;

Acc_Pdl = out.Acc_Pdl; % Accelerator pedal [%]

Brk_Pdl = out.Brk_Pdl; % Brake pedal [%]

Gear_Cmd = out.Gear_Cmd; % Transmission gear

Trans_In_Spd = out.Trans_In_Spd*30/pi; % Transmission input speed [rpm]

Trans_Out_Spd = out.Trans_Out_Spd*30/pi;% Transmission output speed [rpm]

Eng_Trq = out.Eng_Trq; % Engine brake torque [Nm]

Eng_Spd = out.Eng_Spd*30/pi; % Engine speed [rpm]

Eng_Fuel_Consump = out.Eng_Fuel_Consumption; % Engine fuel consumption [g]

Veh_Dist = out.Veh_Dist/1000; % Distance traveled by vehicle [km]

Veh_Spd = out.Veh_Spd*18/5; % Vehicle speed [kmph]

% Data Analysis

Eng_Pwr = Eng_Spd.*pi/30.*Eng_Trq/1000; % Engine brake power [kW]

rho_petrol = 0.7197; % Density of petrol [kg/L]

kmpl = Veh_Dist(end)/(Eng_Fuel_Consump(end)/1000/rho_petrol); % Fuel economy [kmpl]

% Plotting vehicle characteristics time trace

figure(1)

subplot(3,1,1)

hold on

grid on

plot(Time, Acc_Pdl, 'linewidth', 1, 'Color', 'black');

plot(Time, Brk_Pdl, 'linewidth', 1, 'Color', 'red');

ylabel('Pedal [%]')

xlim([0, Time(end)]);

legend('Accel','Brake');

subplot(3,1,2)

hold on

grid on

plot(Time, Trans_In_Spd, 'linewidth', 1, 'Color', 'black');

plot(Time, Trans_Out_Spd, 'linewidth', 1, 'Color', 'red');

ylabel('Speed [rpm]')

xlim([0, Time(end)]);

ylim([0, 6000]);

legend('Trans Input','Trans Output');

subplot(3,1,3)

hold on

grid on

plot(Time, Gear_Cmd, 'linewidth', 1, 'Color', 'black');

xlim([0, Time(end)]);

xlabel('Time [s]');

ylabel('Gear');

% Plotting engine operating points on engine fuel map

figure('Name','Engine Operating Points');

hold on

grid on

title('Engine Operating Points','fontsize',12);

p1=plot(Eng_map_spd*30/pi,Eng_max_trq,'linewidth',2,'color','r');

p2=line([Eng_map_spd(end)*30/pi Eng_map_spd(end)*30/pi],[0 Eng_max_trq(end)]);

set(p2,'linewidth',1,'linestyle','--','color','k');

p3=line([Eng_map_spd(1)*30/pi Eng_map_spd(1)*30/pi],[0 Eng_max_trq(1)]);

set(p3,'linewidth',1,'linestyle','--','color','k');

[c,h] = contour(Eng_map_spd'*30/pi,Eng_map_trq,Eng_fuel_map');

clabel(c,h);

xlabel('Engine Speed [rpm]');

ylabel('Engine Torque [Nm]');

title('Engine Fuel Consumption Map [g/s]');

p4 = plot(Eng_Spd,Eng_Trq,'.','color','k');

legend([p1 p4],{'Max Curve','Operating Points'});

axis([Eng_map_spd(1)*30/pi Eng_map_spd(end)*30/pi 0 Eng_map_trq(end)]);

%Plotting vehicle Speed and Distance

hold;

figure('Name','vehicle speed and distance');

subplot(2,1,1);

hold on;

grid on;

plot(Time,Veh_Spd,'linewidth',1,'Color','black');

xlim([0,Time(end)]);

xlabel('Time(s)');

ylabel('vehicle speed(m/s)');

subplot(2,1,2);

hold on;

grid on;

plot(Time,Veh_Dist,'linewidth',1,'Color','black');

xlim([0,Time(end)]);

xlabel('Time(s)');

ylabel('vehicle distance(m)');

We have plotted different graph from given data and we analyse the the Out put of each charastistic and find the result below here ;

In this we can see that as decrease torque speed is increase:

END;Thank you.

Project 1

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Ship work like thisin your own course.

Talk to a career counsellor

Ship work like this
in your own course.