Project 1

DEVELOPMENT OF A FORWARD ENERGY-BASED FUEL CONSUMPTION MODEL OF A CONVENTIONAL VEHICLE.

AIM :

1. Compute the fuel economy numbers for the given vehicle (kmpl) on the urban cycle (UDDS) and highway cycle (HWFET). Also, analyse and understand the vehicle operating points in terms of engine speed, torque, transmission speeds, pedal positions, gear, distance travelled, etc.
2. Build Simulink model using blocks and complete the vehicle Model
3. Write scripts in MATLAB to load all the variables needed to run the simulation model.
4. Analyse data and create plots using MATLAB scripts.

Task :

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

Forward-Modelling Approach

1. The forward modelling approach is also known as cause-effect, engine-to-wheel or rear-to-front method. The direction of calculation is the same as the direction of positive power flow, from engine-to-wheel.
2. This approach is based on vehicle dynamics equations and the behaviour of each powertrain components and their dynamic interaction.
3. Moreover, a driver controller is required to control the vehicle to follow a desired speed and set the start of the calculations. The controller is implemented to model the driver that will either push the accelerator or the brake pedal to match the desired speed which enables to study the driver behaviour. The controller is used as part of a feedback loop that will determine the energy required to drive the vehicle.

Vehicle Energy Consumption Estimation Models

Energy consumption estimation models can be classified in three main categories:

Analytical models:

Analytical models are representative of the Vehicle behaviour and can be developed in MATLAB/Simulink by creating an vehicle model. They are based on fundamental theoretical equations. A model of the vehicle dynamics is built based on vehicle dynamics theory to calculate the power required at the wheels to overcome the resistances forces opposed to the vehicle motion: Aerodynamic Drag, Rolling, Inertia, Gradient and Transmission resistances. The power required derives from the tractive force expressed as the sum of the resistances forces above

Statistical models:

Statistical models are built based on real world driving data analysis to derive empirical relationships between the different factors affecting Vehicles energy consumption.

Computational models:

There are mainly two kinds of computational models for energy consumption estimation: Artificial Neural Networks (ANN) based model. Clustering based models.

Conventional Vehicle

Conventional vehicles use an internal combustion engine fuel 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 force and torque output.

Forward Model of conventional vehicle:-

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 simulation are more realistic due to physical 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.

1. This model includes a driver model that provides input commands acceleration and brake torque depending on the drive cycle and the actual speed of the vehicle.
2. A control model that controls the engine torque to the desired range and changes the gear command and control brake torque according to the driver input.
3. The transmission model that gives the actual speed of the vehicle as feedback to the driver model. The model includes the engine, transmission, differential, wheel and vehicle dynamics models separately.

VEHICLE MODEL- Pre-Processing:

%% Initialization File

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

%% Vehicle Parameter

clc;

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 = 1; % 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);

The Drive Cycle can be selected from this MATLAB Script, By changing the variable Drive_Cycle_Select to,

1 UDDS

2 HWFET

First to udds drive cycle so be done so the varible selected is 1.

SIMULINK MODELLING FOR COVENTIONAL VEHICLE:

1. The Simulink model for the convection model is created using 3 main subblocks for the driver model, control model and powertrain block.
2. The actual vehicle speed is given as feedback to the driver block, and the driver block gives the controller block the required acceleration or brake output, while the controller block provides control signals for engine torque, gear shifting and brake torque to the Powertrain model.

Driver Model :

1. Driver Model is a basic PI controller that generates accelerator and brake pedal outputs to match the desired and actual vehicle speeds.

2. The PI controller controls the error between the required drive cycle and actual vehicle speed by providing acceleration and brake pedal output to the controller.

Controller Model:

1. The Controller block is the brains of the vehicle, representing the electronic controls units. Based on the driver inputs, it generates desired engine torque, desired transmission gear and desired brake torque. These inputs are fed to the Powertrain block which represents the drivetrain of the vehicle.
2. The controller consists of an engine control unit, a transmission control unit and a brake control unit.
3. All control signals can be scoped.

Engine Control Unit:

1. Engine Control Unit Compare engine speed with the maximum torque and minimum torque curves of the lookup table and give the required torque command to the engine block. The throttle input is combined with the idle speed throttle and gains with the torque output from the lookup table to estimate the torque input required for the engine.
2. Throttle input, idle throttle and their sum can be scoped from this block.

Transmission Control Block:

1. In this unit we create steady 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

Brake Control Unit :

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

Power Train :

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. This includes Engine, Transmission, Differential, Wheels and the vehicle body dynamics subsystems.

Engine Model :

1. Based on the Engine speed The required torque from the controller is saturated between Max torque curve and min torque curve and passes the Engine torque to the Transmission block
2. The 2D lookup table is used to calculate the fuel flow rate based on engine speed and engine torque. The fuel flow rate is then integrated to find the fuel consumption.
3. Estimated fuel flow rate, fuel consumption, engine torque and engine speed are passed to the MATLAB workspace using the to-workspace block.
4. Engine fuel flow rate and Fuel consumption are scoped.

Transmission Model :

1. In these model we take engine torque, gear selector from controller 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.
2. The engine block is connected to the automatic transmission via a torque converter. The output of the transmission is connected to the differential.

Torque Converter :

1. The gear selection from the transmission control unit is multiplied by the engine torque in this block, and the efficiency and engine dynamics of the torque converter are applied in this model.

Differential Model :

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.

Wheel Model :

1. The brake torque from the control model is subtracted from the Axle torque and divides from the rolling radius to determine the Tractive force.
2. The Wheel speed is calculated from the vehicle velocity and saved in the workspace.

Vehicle Dynamics :

1. In this takes tractive force and road load. It is dictates vehicle speed based on tractive force and road load.
2. the dynamic equation for calculating road load is

where,

p = air density [kg/m^3]

A = Frontal area [m^2]

Cd = drag Co efficient

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.

SIMULATION RESULTS :

1. UDDS Drive Cycle:

Drive Cycle and Actual Velocity :

Control Signals (Engine Torque, Transmission Gear and Brake torque) :

Engine Fuel Rate and Fuel Consumption :

Engine Torque :

Gear Ratio, Transmission Input and Output Speed :

Tractive Force, Road Load and Acceleration:

%% Post-Processing File

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

%% Renaming variables

clc;

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]

Whl_Spd = out.Whl_Spd*30/pi;

Eng_Fuel_Rate = out.Eng_Fuel_rate; % Engine fuel rate [%]

%% 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(2,1,1)

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(2,1,2)

hold on

grid on

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

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

xlabel('Time [s]');

ylabel('WHL_SPD');

figure(2)

subplot(4,1,1)

hold on

grid on

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

ylabel('ENGINE TORQUE')

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

subplot(4,1,2)

hold on

grid on

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

ylabel('POWER IN KW')

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

subplot(4,1,3)

hold on

grid on

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

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

xlabel('Time [s]');

ylabel('Eng_Fuel_Consump');

subplot(4,1,4)

hold on

grid on

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

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

xlabel('Time [s]');

ylabel('Eng_Fuel_Rate');

figure(3)

subplot(4,1,1)

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(4,1,2)

hold on

grid on

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

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

xlabel('Time [s]');

ylabel('Gear');

subplot(4,1,3)

hold on

grid on

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

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

xlabel('Time [s]');

ylabel('veh_spd');

subplot(4,1,4)

hold on

grid on

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

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

xlabel('Time [s]');

ylabel('veh_dist');

%% 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)]);

Plot the time trace for engine, transmission input, transmission output and wheel speed on one plot.

In this graph we see the transmission input and transmission out will fluctuates up and down as increasing speed and time.

Plot engine torque, engine power, fuel rate and fuel consumption using subplots.

In this engine torque, Engine power, engine fuel rate and engine fuel consumption is increase increasing with time.

Plot accelerator and brake pedal, gear command, vehicle desired and actual speed, vehicle distance using subplots.

In this the engine fuel consumption is suddenly increase as increasing fuel economy but if fuel economy is constant then the fuel consumption is also constant

In this the gear shifting is required as increase vehicle speed. as shifting order is gear1, then gear2, then gear3, and then gear4.

Plot the engine operating points on the engine fuel map.

SIMULATION RESULTS :

1. HWFET Drive Cycle:

Drive Cycle and Actual Velocity :

Control Signals (Engine Torque, Transmission Gear and Brake torque) :

Engine Fuel Rate and Fuel Consumption :

Engine Torque :

Gear Ratio, Transmission Input and Output Speed :

Tractive Force, Road Load and Acceleration:

Plot the time trace for engine, transmission input, transmission output and wheel speed on one plot.

In this graph we see the transmission input and transmission out will fluctuates up and down as increasing speed and time.

Plot engine torque, engine power, fuel rate and fuel consumption using subplots.

In this engine torque, Engine power, engine fuel rate and engine fuel consumption is increase increasing with time.

Plot accelerator and brake pedal, gear command, vehicle desired and actual speed, vehicle distance using subplots.

In this the engine fuel consumption is suddenly increase as increasing fuel economy but if fuel economy is constant then the fuel consumption is also constant

In this the gear shifting is required as increase vehicle speed. as shifting order is gear1, then gear2, then gear3, and then gear4.

Plot the engine operating points on the engine fuel map.

END....

NOTE:...

Execution of script meshgrid as a function is not supported:
C:\Users\SATHISH RAVEN\Documents\MATLAB\meshgrid.m

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