Project 9 - SIMULINK - SIMULATION - DEVLOPMENT OF EFFICIENT MODEL OF A CONVENTIONAL VEHICLE

Project 1: 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] Witting 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, and 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.

 

Conventional Vehicle:-

Conventional vehicles use an internal combustion engine fuelled 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. 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 cylinder process can be represented by ideal thermodynamic cycles such as Otto cycle, Diesel cycle. Otto cycle is a ideal cycle that describes the cycle process for spark ignited SI engine. And the Diesel cycle is the ideal cycle that describes the cycle process for a compression ignited CI engine

 

Energy and performance of vehicle:-

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

 In this 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 powertrain 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. –

In this system of IC engine if the 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 known as idling process. Above mentioned formula is applicable only when the wheel power is strictly power. When the wheel power is negative either the engine is disengaged and idling or the engine 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 modelling 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 modelled using maps which characterized the relationship between the input and output, output and losses etc.

 

2] Analytical models: - Elements are modelled 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 simulations 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.

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.

% 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);

% 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,

Let’s 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 Mat lab/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 Mat lab 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 modelled 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 from the based on these results of fuel consumption and emissions the vehicle gets certified and it also forms a basis of uniform assessment. It is based on time, velocity then it’s all integrate and creates output acceleration 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 treble in that give input ct torque and map speed then it subtracts and multiplied. The engine speed also give to ideal control 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 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.

 

3] Brake Control Unit:-

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

 

3] 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] 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 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.

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 it’s 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 differential and brake torque from controller. it is subtracts and multiplied rolling radius and velocity then we meet the tractive force from axle and brake torque. And the wheel speed is meet using velocity.

 

 5] Vehicle Dynamics:-

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

3] Rolling Resistance:- It’s 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 continuously 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.

For creating Plots we use MATLAB coding So the coding will give down:-

% 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

 

% 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

 

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

 

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

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

3] Plotting fuel economy Vs Engine Fuel consumption:-

Engine fuel consumption is suddenly increase as increasing 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:-

 

as comparing brake of vehicle the acceleration 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:-

 

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

In this the as decrease torque speed is increase.

 

Conclusion –

 

1. The fuel economy is best in HWFET as HWFET is highway driving as it doesn't require much variations in driving conditions.
2. The fluctuations in UDDS are more than HWFET.
3. The more the fluctuations in speed, the change of gears must be more frequent. In the 3rd graph from both cycles, the UDDS requires shifting of gears more often.
4. From the above observations it is obvious that more frequent changes in engine operating conditions is not economical as the fuel economy will decrease