Project 1

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

Aim:-

1] Build Simulink model using blocks and complete the vehicle model.

2] Writting 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.

OBJECTIVES

1. Tabulate the fuel economy numbers (kmpl) for UDDS and HWFET cycle.
2. Plot the time trace for engine, transmission input, transmission output and wheel speed on one plot.
3. Plot engine torque, engine power, fuel rate and fuel consumption using subplots.
4. Plot accelerator and brake pedal, gear command, vehicle desired and actual speed, vehicle distance using subplots.
5. Plot the engine operating points on the engine fuel map.
6. Write down appropriate brief comments on each of these plots, describing the vehicle behaviour throughout the drive cycle.
7. Generate the plots for both drive cycles.

THEORY

Forward-Modelling Approach

• 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.
• This approach is based on vehicle dynamics equations and the behaviour of each powertrain components and their dynamic interaction.
• 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 Modelling

Architecture of the forward conventional vehicle model is presented in Figure:

• 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.
• 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.
• 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

• A MATLAB script has been created to load all the variables needed to run and post-process the Simulink model. Drive cycle selection is also done from here. Before running the Simulink vehicle model, the script is initialized at MATLAB to load all the variables in the workspace.

%% Skill-Lync -> Hybrid Drives Development
% Project 1 - Conventional Vehicle Model
% By - Anas K Y 

% 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 = 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
3	FTP
4	US06
5	NEDC
6	WLTP

SIMULINK MODELLING

• The Simulink model for the convection model is created using 3 main subblocks for the driver model, control model and powertrain block.
• 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

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

• 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

• 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

• The controller consists of an engine control unit, a transmission control unit and a brake control unit.
• All control signals can be scoped.

Engine Control Unit

Idle Speed Controller:

• 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.
• Throttle input, idle throttle and their sum can be scoped from this block.

Transmission Control Block

• The transmission control block controls the gear shifting using the study flow Variable chart.

• The Gear shifting is Controlled according to the vehicle speed and throttle input.

• The gear command is passed to the Transmission block to select the desired gear ratio.

Brake Control Unit

• Brake Control unit Produce required brake torque according to the brake pedal input and passes the brake torque in to the wheel block in the transmission model.

Powertrain Model

• The powertrain model includes all the components that convert the power of the engine into vehicle motion.

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

Engine Model

• 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
• 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.
• Estimated fuel flow rate, fuel consumption, engine torque and engine speed are passed to the MATLAB workspace using the to-workspace block.
• Engine fuel flow rate and Fuel consumption are scoped.

Transmission Model

• 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

• 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

• The transmission output torque is multiplied by the differential ratio and differential efficiency in this block and the torque is transmitted to the wheel as the axle torque.

Wheel Model

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

Vehicle Dynamics

• The longitudinal dynamics of the vehicle along with the road load is represented in the ‘Vehicle Dynamics’ block

• Tractive Force and Road loads are used to calculate the vehicle velocity,

ηt⋅λt⋅TeRw=MdVvehdt+12ρaAfCd(Vveh+Vwd)2+CrMgcosθ+Mgsinθη�⋅λ�⋅����=�����ℎ��+12ρ�����(���ℎ+���)2+����cosθ+��sinθ

ηt⋅λt⋅TeRwη�⋅λ�⋅���� = Tractive force

12ρaAfCd(Vveh+Vwd)2+CrMgcosθ+Mgsinθ12ρ�����(���ℎ+���)2+����cosθ+��sinθ = Road Load

Where,

ρ a - Air Density

A f - Frontal Ares

C d - Drag Coefficient

C r - Rolling Coefficient

M - Mass of Vehicle

g - Acceleration due to gravity

θ - Slope

• The variables are loaded on the workspace using MATLAB Script.
• The Acceleration, Velocity and Distance travelled are Calculated and stored in the workspace.

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

Different Road Loads

Tractive Force, Road Load and Acceleration

2. HWFET Drive Cycle

Drive Cycle and Actual Velocity

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

Engine Torque

Engine Fuel Rate and Fuel Consumption

Gear Ratio, Transmission Input and Output Speed

Different Road Loads

Tractive Force, Road Load and Acceleration

Post-Processing

• A MATLAB script is Created to analyse data and to generate plots. Here we rename the variables loaded from the model, do some basic post-process calculations and generate results and plots.

%% 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_rate = out.Eng_Fuel_Rate; % Engine Fuel Rate [gkW]
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;    % Wheel speed [rpm]
Desired_spd = out.Desired_Velo*18/5; % Desired 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] 

% Display Fuel Economy of the Cycle
fprintf('nFuel Economy = %2.2fKmplnn',kmpl);

%% Plotting vehicle characteristics time trace
% Plotting transmission input, transmission output and wheel speed
figure(1)
hold on
grid on
plot(Time, Trans_In_Spd, 'linewidth', 1, 'Color', 'black');
plot(Time, Trans_Out_Spd, 'linewidth', 1, 'Color', 'red');
plot(Time, Whl_Spd, 'linewidth', 1, 'Color', 'blue');
xlabel('Time [s]')
ylabel('Speed [rpm]')
xlim([0, Time(end)]);
legend('Trans Input','Trans Output','Wheel Speed');
% Plotting engine torque, engine power, fuel rate and fuel consumption
figure(2)
subplot(4,1,1)
hold on
grid on
plot(Time, Eng_Trq, 'linewidth', 1, 'Color', 'black');
ylabel('Torque [Nm]')
xlim([0, Time(end)]);
subplot(4,1,2)
hold on
grid on
plot(Time, Eng_Pwr, 'linewidth', 1, 'Color', 'black');
ylabel('Power [KW]')
xlim([0, Time(end)]);
subplot(4,1,3)
hold on
grid on
plot(Time, Eng_Fuel_rate, 'linewidth', 1, 'Color', 'black');
xlim([0, Time(end)]);
xlabel('Time [s]');
ylabel('Engine Fuel rate');
subplot(4,1,4)
hold on
grid on
plot(Time, Eng_Fuel_Consump, 'linewidth', 1, 'Color', 'black');
xlim([0, Time(end)]);
xlabel('Time [s]');
ylabel('Fuel Consumption [g]');
%Plotting accelerator and brake pedal gear command, vehicle desired and actual speed, vehicle distance
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', 'black');
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');
plot(Time, Desired_spd, 'linewidth', 1, 'Color', 'black');
ylabel('Speed [Kmph]')
xlim([0, Time(end)]);
legend('Actual speed','Desired speed');
subplot(4,1,4)
hold on
grid on
plot(Time, Veh_Dist, 'linewidth', 1, 'Color', 'black');
xlim([0, Time(end)]);
xlabel('Time [s]');
ylabel('Distance [Km]');

%% Plotting Drive Cycle
figure(4)
hold on
grid on
plot(cyc_time, cyc_vel, 'linewidth', 1, 'Color', 'black');
xlabel('Time [s]')
ylabel('Velocity [Mph]')
title('Drive Cycle')
% Test Results
Avg_speed = sum(Veh_Spd)/length(Veh_Spd);
Max_Speed = maxk(Veh_Spd,1);
table(Avg_speed,Max_Speed,kmpl,'VariableNames',{'Average Speed [kmph]','Maximum Speed [kmph]','Fuel Economy [kmpl]'})

%% 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]');
legend([p1 p3],{'Max Curve','Operating Points'});
axis([Eng_map_spd(1)*30/pi Eng_map_spd(end)*30/pi 0 Eng_map_trq(end)]);

RESULTS

1.Drive cycles

UDDS:

HWFET:

2. Fuel economy numbers (kmpl) for UDDS and HWFET cycle.

UDDS:

HWFET:

• The City Fuel Economy is much lower than the highway drive as the brake pedal engagement and idle time on the drive cycle is less.

3. The time trace for engine, transmission input, transmission output and wheel speed

UDDS:

HWFET:

• Much of the fluctuating input speed is facilitated by the torque converter, and the transmission input speed fluctuates according to the engine torque required to achieve the drive cycle.
• Wheel speed and transmission output speed vary according to gear and differential ratio.

4. Engine torque, engine power, fuel rate and fuel consumption

UDDS:

HWFET:

• The torque requirements of the drive cycle are different, so the fuel flow rate varies and fuel consumption is calculated.
• The HWFET drive cycle has higher power and torque requirements and less negative torque and power.

5. Accelerator and brake pedal, gear command, vehicle desired and actual speed, vehicle distance

UDDS:

• Acceleration and brake pedals are used more because the drive cycle is not very smooth and the gear has to be shifted frequently.

HWFET:

• Brake requirements are lower, more accelerator pedals are applied on the highway drive cycle, and gear shifts often occur because the drive cycle is smoother.
• Acceleration and brake pedal, the gear command of the transmission control unit, the required speed of the vehicle, the actual vehicle speed and the actual vehicle distance are plotted

6. The engine operating points on the engine fuel map

UDDS:

HWFET:

• The engine operating points are plotted on the fuel map and the operating point is under the maximum torque curve.

CONCLUSION

• Hence, the forward energy-based fuel consumption model of a conventional vehicle has been successfully Simulated using MATLAB and Simulink.
• Analyses and understands vehicle operating points based on engine speed, torque, transmission speed, pedal positions, gear, and distance travelled.
• Post process calculations were performed and the required graphs were successfully plotted.
• Successfully calculated vehicle fuel efficiency numbers (kmpl) for the given city cycle (UDDS) and highway cycle (HWFET).

Conventional Vehicle:-

Conventional vehicles use an internal combustion engine 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.

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

.mf=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 variables-

1] Efforts:- It is generalised force variable. ex- torque, force, voltage, heating value.

2] Flow:- It is the generalised flow/velocity variable. ex:- velocity, current, mass flow rate.

3] 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.

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

2] 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 - Dharmesh Ravindra Bonde 

% 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 = 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);

Now,

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 ‘Powertrain’ block which represents the drivetrain of the vehicle.

1] 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.

Graph created from Scope in Driver Model:-

2] Transmission Control Unit:-

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

3] 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 in Driver Model:- 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 in Driver Model:- 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:-

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

the dynamic equation for calculating road load is

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

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.

Graph created from Scope in Driver Model:- 1] Road load 2] Acceleration 3] Tractive force

Graph created from Scope in Driver Model:- 1] Road load

Now,

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.

2] Aerodyanamic drag:- Drag as increase when the power increase.

3] Rolling Resistance:- Its also increase when power is increase wrt time.

4] Total power = aerodynamic drag+rolling resistance+inertial resistance

Conclusion:-

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

2] HWFET Drive Cycle:-

1] Time VS Speed:- Speed is continuosly increase wrt time.

2] Aerodynamic Drag:- Drag as increase when the power increase.

3] Rolling Resistance:- In this resistance is decrease wrt time.

4] Total Power:- aerodynamic drag+rolling resistance+inertial resistance

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 UDDS 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 - Dharmesh Ravindra Bonde

% 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]
Wheel_Speed = out.wh_speed;     % Wheel speed [kmph]
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 transmission IN/OUT and Wheel Speed
figure(1)
hold on
grid on
plot(Time, Trans_In_Spd, 'linewidth', 1, 'Color', 'black');
plot(Time, Trans_Out_Spd, 'linewidth', 1, 'Color', 'red');
plot(Time, Wheel_Speed, 'linewidth',1, 'color', 'blue')
ylabel('Speed [rpm]')
xlabel('Timem [s]')
xlim([0, Time(end)]);
ylim([0, 6000]);
legend('Trans Input','Trans Output','Wheel Speed');

%% Plotting vehicle characteristics time trace to Engine speed, Engine Torque, Engine Power, 
%  And Engine fuel rate, Engine fuel consumption
% Data conversion from Double Timeseries

Eng_spd = Eng_Spd.Data(:,1);   % engine speed
Eng_trq = Eng_Trq.Data(:,1);   % engine torque
Eng_pwr = Eng_Pwr.Data(:,1);   % engine power
Eng_fuel_rate = Eng_Fuel_Rate.Data(:,1);   % engine fuel rate
Eng_fuel_consumption = Eng_Fuel_Consump.Data(:,1);   % engine fuel consumption
Veh_dist = Veh_Dist.Data(:,1);   % vehicle distance
Veh_spd = Veh_Spd.Data(:,1);    % vehicle speed

% Plotting Engine speed, Engine Torque, Engine Power,Engine fuel rate,Engine fuel consumption wrt Time 
figure(2)
subplot(2,2,1)
plot(Time,Eng_trq,'linewidth',1,'color','black')
xlabel('Time [s]')
ylabel('Torque [Nm]')
legend('Engine Torque')
grid on
subplot(2,2,2)
plot(Time,Eng_pwr,'linewidth',1,'color','blue')
xlabel('Time [s]')
ylabel('Power [KW]')
legend('Engine Power')
grid on
subplot(2,2,3)
plot(Time,Eng_fuel_rate,'linewidth',1,'color','red')
xlabel('Time [s]')
ylabel('Fuel Rate [%]')
legend('Engine Fuel Rate')
grid on
subplot(2,2,4)
plot(Time,Eng_fuel_consumption,'linewidth',1,'color','green')
xlabel('Time [s]')
ylabel('Fuel Consumption')
legend('Engine Fuel Consumption')
grid on

% Plotting fuel economy Vs Engine Fuel consumption 
figure(3)
plot(Eng_fuel_consumption,Eng_fuel_rate,'color','r')
xlabel('Fuel consumption')
ylabel('Fuel Rate [%]')
title('Fuel economy Vs Fuel Consumption')
grid on

%% Potting Accelerator pedal, Brake pedal and gear command with respect to time
figure(4)
subplot(2,1,1)
hold on
plot(Time, Acc_Pdl, 'linewidth', 1, 'Color', 'black');
plot(Time, Brk_Pdl, 'linewidth', 1, 'Color', 'red');
ylabel('Pedal [%]')
xlabel('Time [s]');
xlim([0, Time(end)]);
grid on
legend('Accel','Brake');
title('Acceleration & Brake Pedal wrt Time')

subplot(2,1,2)
plot(Time, Gear_Cmd, 'linewidth', 1, 'Color', 'black');
xlim([0, Time(end)]);
xlabel('Time [s]');
ylabel('Gear');
grid on
title('Gear command wrt Time')

% Plotting gear command vehicle desired with actual speed
figure(5)
subplot(2,1,1)
plot(Veh_spd, Gear_Cmd, 'linewidth', 1, 'Color', 'black');
xlabel('Speed [rpm]');
ylabel('Gear');
grid on
title('Gear command wrt vehicle speed')

subplot(2,1,2)
plot(Veh_dist, Gear_Cmd, 'linewidth', 1, 'Color', 'red');
xlabel('Distance [kmpl]');
ylabel('Gear');
grid on
title('Gear command wrt Distance')

%% 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]');
legend([p1 p3],{'Max Curve','Operating Points'});
axis([Eng_map_spd(1)*30/pi Eng_map_spd(end)*30/pi 0 Eng_map_trq(end)]);

1] Plotting vehicle characteristics time trace transmission IN/OUT and Wheel Speed:-

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

2] Plotting vehicle characteristics time trace to Engine speed, Engine Torque, Engine Power, And Engine fuel rate, Engine fuel consumption

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

3] Plotting fuel economy Vs Engine Fuel consumption:-

In this the engine fuel consumption is suddensly increase as incresing fuel economy but if fuel economy is constant then the fuel consumption is also constant.

4] Potting Accelerator pedal, Brake pedal and gear command with respect to time:-

In this the as comparing brake of vehicle the accelaration of vehicle is more so the brake torque is min required. and the gear shifting is also required wrt time.

5] Plotting gear command vehicle desired with actual speed:-

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

6] Plotting engine operating points on engine fuel map:-

In this the as decrease torque speed is increase.