Challenge

Project 2

BBhoopendra Taram30 May 2023

Description:
As discussed in Lecture 11, Project 2 deals with the development of a forward energy-based fuel consumption model of a P1 hybrid vehicle. This model is based on the conventional vehicle model from Project 1.

The main goal of this project is to understand the functionality of a P1 hybrid and be able to model it.

Honda Insight motor and battery are considered for this project. Data is taken from ADVISOR data base developed by NREL, USA. Data provided in the project files.

“Project2_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.

Task:

The task of this project is to quantify the improvement in fuel economy numbers for the hybrid vehicle (kmpl), compared to the conventional vehicle, on the urban cycle (UDDS) and highway cycle (HWFET). Also, to analyze and understand the vehicle operating points in terms of engine and motor speed, torque, transmission speeds, pedal positions, gear, distance traveled, battery voltage, current, SoC etc.

To do that, the following steps need to be followed:

1) Load the Project2 folder as the Current Folder in Matlab.
2) Run the “Project2_InitFile.m” Matlab script.
3) Copy the Conventional vehicle Simulink model created for Project 1 in this folder. Figure 1: P1 Hybrid ArchitectureHybrid Drives Development
4) Modify this model into a hybrid vehicle model. The lecture presentations can be used as reference. Battery and Supervisory controller models developed in the assignments can be reused.
5) Add additional ‘To Workspace’ blocks for transferring the desired Simulink vehicle data into the Matlab workspace.
6) Simulate the hybrid model on the desired drive cycles.
7) Write a project report that contains the following:
a. Tabulate the fuel economy numbers (kmpl) for UDDS and HWFET cycle. Compare these numbers with the baseline numbers for conventional vehicle model from Project 1.
b. Plot the time trace for engine, transmission input, transmission output and wheel speed on one plot.
c. Plot engine torque, engine power, fuel rate and fuel consumption using subplots.
d. Plot accelerator and brake pedal, gear command, vehicle desired and actual speed, vehicle distance using subplots.
e. Plot the engine operating points on the engine fuel map.
f. Plot motor speed, torque, motor input power using subplots.
g. Plot pack voltage, current and SoC using subplots.
h. Plot the motor operating points on the motor input power map, with the motor max motoring and generating torque curves.
i. Write down appropriate brief comments on each of these plots, describing the vehicle behavior throughout the drive cycle.
j. Generate the plots for both drive cycles

Click here to download the attachment

ANSWER

Theory of P1 Hybrid:

The P1 architecture, with the electric machine connected directly to the crankshaft, is the solution adopted by Honda on their first generation Integrated Motor Assist (IMA) technology. The electric motor functions as a generator, during vehicle deceleration, as an engine starter, and as a motor (to assist the engine) during vehicle accelerations.

One of the biggest advantages of this solution is that the electric motor can provide higher torque than the BiSG(Belt integrated Starter Generator) since there is no belt limitation (due to slipping). However, since there is no speed/torque ratio between the electric machine and crankshaft, the torque requirements on the electric motor can be quite demanding.

Mild Hybrid Vehicle:

Mild Hybrid Electric Vehicles (MHEV), looks into the positioning of the components on the vehicle, the possible control functions, and advantages and disadvantages in terms of fuel efficiency and drive ability.

Different types of hybrid electric vehicles are:

Micro hybrid electric vehicles
Mild hybrid electric vehicles
Full hybrid electric vehicles
Plug-in hybrid electric vehicles

By architecture, topology, or configuration we understand the positioning of the main components of the hybrid electric system on the vehicle. Since the only mechanical link between the electrical system and the rest of the vehicle is done through the electric machine, the MHEV architecture is defined by the position of the electric machine and the type of connection with the power train/drive train (belt, integrated, or gear mesh).

The 48V electrical system has become the industry standard for mild hybrid MHEV applications for several reasons:

It’s relatively simple to integrate into a vehicle it’s modular, safe, and compact the mass of the components is relatively small so the impact on the total weight of the vehicle is limited the system cost-to-performance (fuel efficiency, torque boost) ratio is very competitive

Except for the crankshaft-mounted integrated starter generator (Honda IMA and Mercedes Benz Blue Hybrid), all the mild hybrid vehicle architectures presented in this article are based on 48V systems.

The power train configuration of the MHEV system has a significant impact on the performance and characteristics of the vehicle, in terms of integration cost, fuel efficiency & dynamic performance (power train torque enhancement)

Automotive OEMs and automotive Tier 1 system suppliers are currently analyzing and evaluating several major power train architectures for MHEVs. The electric machine can be positioned, relative to the other power train components, in five major points:

Image: MHEV power train architectures

A brief description of the electric machine connection points is done in the table below.

P0 Hybrid:

The electric machine is connected with the internal combustion engine through a belt, on the front end accessory drive (FEAD)

P1 Hybrid:

The electric machine is connected directly to the crankshaft of the internal combustion engine

P2 Hybrid:

The electric machine is side-attached (through a belt) or integrated between the internal combustion engine and the transmission; the electric machine is decoupled from the ICE and it has the same speed as the ICE (or multiple of it)

P3 Hybrid:

The electric machine is connected through a gear mesh with the transmission; the electric machine is decoupled from the ICE and its speed is a multiple of the wheel speed.

P4 Hybrid:

The electric machine is connected through a gear mesh on the rear axle of the vehicle; the electric machine is decoupled from the ICE and it’s located in the rear axle drive or the hub of the wheel.

Thesis:

Forward-Modelling of Vehicle Approach:

This kind of approach is known as the cause-effect, engine-to-wheel or rear-to-front method, the direction of calculation is the same as the direct positive power flow method: power flow is from the engine to the wheel.

This approach is based on the vehicle dynamics equation and the behavior of single power train components and their dynamic interaction between the wheels.

A Driver Controller is required to control the vehicle to follow desired speed and to calculate.

The Controller is implemented to model that the driver will either push the accelerator or the brake pedal to match the desired speed which enables to study the braking of the vehicle

It also acts as a feedback loop for the vehicle which was used to determine the energy requirements to drive the vehicle.

Convectional Vehicle Modelling:

Basic architecture model of forwarding Vehicle Modeling:

3 Stages to Compute the Simulation:

Pre-Processing
Run the Simulink Model
Post-Processing

Pre-Processing

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

%% Skill-Lync -> Hybrid Drives Development

% Project 1 - Conventional Vehicle Model

% By - BHOOPENDRA

% 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('Trans_Shift_map.mat')

% load('Torque_Converter_data.mat')

% load('ESS_NiMH6.mat')

% load('Engine_data_1_9L_Saturn_95kW.mat')

% load('EM_Westinghouse_75kW_AC_Induction.mat')

% load('EM_Honda_Insight_10kW.mat')

load Vehicle_Data/Engine_data_1_9L_Saturn_95kW

load Vehicle_Data/Trans_Shift_map

load Vehicle_Data/EM_Honda_Insight_10kW

load Vehicle_Data/ESS_NiMH6

load Vehicle_Data/Torque_Converter_data

load Vehicle_Data/EM_Westinghouse_75kW_AC_Induction

ESS_init_soc = 0.8;

ESS_soc_max = 0.9;

ESS_soc_min = 0.6;

%% 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);

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

UDDS = 1
HWFET = 2
FTP = 3
USO6 = 4
NEDC = 5
WLTP = 6

Simulink Model:

The Model of Simulink is for the convection model of the vehicle in which it has been divided into 3 main sub-blocks are:

Driver
Controller
Power train

The Actual Vehicle speed is given as feedback to the driver block, and the driver block gives into the controller block in which the acceleration or brake output is connected.

While the controller provides a control signal for engine torque, gear shifting, and brake torque to the power train block.

Driver Model:

In this Driver Block, it is a basic PI controller model that is used to generate accelerator and brake pedal Outputs that are used to match the desired and actual vehicle speeds.

The PI controller controls the error between the required drive cycle and the actual vehicle speed is been provided by the acceleration and brake pedal output of the controller unit.

Controller:

The brain of the vehicle unit is the 'Controller' block, which represents the ECU, Transmission Unit, and Basic Control Unit

Based on the Driver Inputs, it generates desired engine Torque, desired transmission gear, and desired brake torque. These will be sent as feedback to the power train blocks as input which is represented the drive train of the vehicle

Mode Selection:

This block is used to select the mode for the drive condition with 4 different inputs, such as Stop, Start, Drive, and Regenerative Braking.

The input of these blocks is Acceleration, Brake, Engine Speed, State Of Charge(SOC), SOC min, SOC max, Engine Idle Speed, and Clutch-Lockup.

The Output of these blocks is Mode, Engine ON Condition, Engine Throttle, and Electric Motor Torque.

A state flow machine is used to represent the supervisory control unit, which decides the operating mode while it is start/stop mode or cruising mode or regen braking mode for each modes the Engine drive and the motor drive are controlled.

The Input to the state flow charts is Acceleration and Brake pedal input from the driver unit, Engine actual speed, Actual vehicle speed, SOC status and the clutch lockup state. And the State flow Outputs to the individual controllers are the Mode, Engine On/ off request, Engine input throttle and Electric motor input Throttle

The controller consists of an engine control unit, a transmission control unit, Electric Motor control unit and a brake control unit.

All control signals re passed to transmission model which represents the drive train.

Engine Control Unit:

In this Block, ECU is the main unit that used to run the vehicle

Inputs such as Engine Speed, Maximum Torque Curve, Minimum Torque Curve, Idle Speed Control and the output is Engine Torque.

Engine Idle Speed (Idle speed controller):

This block is used is find the idle condition of the engine when it is turned on.

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.

ELECTRIC MOTOR CONTROL UNIT

Motor Control Unit Compare EM speed with the maximum torque curve and maximum torque generation curves of the lookup table and give the required torque command to the Electric Motor. The EM throttle input is used to compare the lookup tables and to estimate the torque input required for the E-Motor.

Transmission Control unit:

This Control unit has 2 inputs whereas Throttle, Vehicle Speed, and Gear Command as Output.

Has Increasing the Input Throttle speed, Changing of gear commands take place accordingly 1,2,3,4 and vice versa for speed dropping condition.

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

Gear Selection:

The Gear command is been created by using the Stateflow block for this simulation.

It is been controlled accordingly by Vehicle Speed and Throttle inputs.

The gear command is been passed to the transmission block to select the desired gear ratio.

Compute Thershold

Electric Motor Unit:

In this unit, motor throttle request is fed in as an input, and based on the speed of motor and throttle, either motoring torque or generating torque is selected from the look-up table. This torque is multiplied to get the electric motor torque request. Since the engine is integrated wi the electrical machine, the speed of an engine is taken as an input speed for a motor. The figure shows the Motor control unit

Brake Control Unit:

It consists of the basic control unit which requires brake torque according to the brake pedal input and passes the brake torque into the wheel block in the transmission model.

POWERTRAIN:

To move up the vehicle we need a Body structure that is used to move the vehicle in forward and reverse directions as required.

It consists of all components that will convert the Fuel power of the engine for the motion to move the vehicle.

It consists of an Engine, Electric Motors, Battery System Transmission, Wheels, Crank Hub, Differential, and Vehicle Dynamics.

The control signals from the control model are transmitted via a bus to the corresponding blocks in the power train model.

Battery System:

In the battery subsystem based on the input power demand from the motor, the current from the battery is considered. And based on the current drawn, the SOC level of the battery is calculated by considering the technique called coulomb counting The Voltage of the battery is the function of the SOC level of the battery, so the open circuit voltage is calculated by the SOC level of battery

2 different sub blocks have been created to determine the SOC and terminal voltage separately, the SOC being used to calculate the terminal voltage.

Figure shows the Simulink block arrangement for the battery subsystem

SOC Estimation Model

Pack current in Ampere is converted to AmpHour and divides with Max Capacity and adds with the Initial soc to determine the SOC of the battery at each instance, the SOC value is limited between 0 to 1.

Terminal Voltage Model

Ideal voltage and internal resistance are determined by ID lookup tables, and battery voltage is determined by using the model.

The specified values are sent back to workspace using the Two Workspace block and can used for post processing.

Engine Block:

In this block based on the engine speed and torque request from the controller, fuel consumption is taken out by using a 2D look-up table, The torque from the engine is also calculated based on the torque request and engine speed and based on the torque engine speed is also calculated at the transmission block.

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.

Figure shows the arrangement of the Simulink block in the engine subsystem.

Electric Motor:

In the motor subsystem also, based on the motor torque request from the controller and engine speed the torque and input power to the motor are calculated. The engine speed is considered because it is considered that the engine and motor are running at the same speed in Pl Hybrid Vehicle.

Based on the Engine speed The required EM torque from the controller is saturated between Max EM torque curve and max EM torque Generation curve and passes the EM torque to the Crank Hub.

The 2D lookup table is used to calculate the Input Electric Power to the Motor based on EM speed and EM torque.

Figure shows the Simulink block arrangement for the Motor subsystem.

CrankHub:

This block is used to compute the both output from the Engine and E-Motor Power into a Single Output to supply it in Transmission System.

Crank Hub adds up the EM torque and the Engine torque and pass it to the transmission block as the crank torque.

Transmission System:

In the transmission subsystem based on the input crank torque and Transmission speed, the torque from the torque converter is calculated. Based on the gear command from the controller, the gear ratio is selected by considering the gear and gear ratio data from the lookup table, product of gear ratio and torque from the torque converter gives us the total output torque from the transmission. Based on the nature of torque its efficiency is assigned and output torque is calculated.

The Crank is connected to the automatic transmission via a torque converter. The output of the transmission is connected to the differential. New Clutch LockUp feature is adding to this Hybrid version.

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.

Figure shows the block arrangement for the transmission subsystem.

Torque Converter:

A torque converter is a type of fluid coupling that transfers rotating power from a prime mover like an internal combustion engine, to a rotating driven load. In a vehicle with an automatic transmission, the torque converter connects the power source to the load. It is usually located between the engine's flex plate and the transmission. The equivalent location in a manual transmission would be the mechanical clutch.

Torque converter defines the pump and turbine torque by the clutch lock up status, and the combination gives us the desired transmission torque.

Differential:

In the differential subsystem, the transmission output torque is multiplied by the final drive ratio of the system and based on the nature of the torque negative or positive axle torque is calculated.

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.

Wheels:

This takes input from brake torque in the control model is been subtracted from the axle torque and divided from the rolling radius to determine the tractive force.

The wheel speed is calculated from the vehicle velocity and saved into the workspace.

Vehicle Dynamics:

This block consists of the longitudinal dynamics of the vehicle which consists of Aerodynamics force, Rolling Resistance Force, Grade Force, etc..,

Formulas used:

where

ρa- Air Density

Af - Frontal Area

Cd-Drag Coefficient

M - Mass of a vehicle

g - Acceleration due to gravitational force

θ - Slope angle

These variables have already been preloaded to the workspace using a MATLAB script

The Acceleration, Velocity and Distance travelled are Calculation and stored in the workspace.

Post-Processing

%% Post-Processing File

% File for post-processing the Conventional vehicle with P1 Hybrid 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]

Whl_Spd = out.Whl_spd; % Wheel speed [kmph]

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

EM_Spd = out.MotorSpd*18/5; % Electric Motor Speed

EM_Pwr = out.MotorPwr; % Electric Motor Power

EM_Trq = out.MotorTrq; % Electric Motor Torque

Ter_Volt = out.V_batt; % Terminal Voltage of battery

Soc = out.SOC; % State Of Charge

Pack_Current = out.pack_current; %Pack current of battery

%% 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, Whl_Spd, 'linewidth',1, 'color', 'blue')

% ylabel('Speed [rpm]')

% xlabel('Timem [s]')

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

% ylim([0, 6000]);

% legend('Trans Input','Trans Output','Wheel Speed');

% figure()

% 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');

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 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

Pack_Current = Pack_Current.Data(:,1);

SOC = Soc.Data(:,1);

V_batt = Ter_Volt.Data(:,1);

EM_Pwr = EM_Pwr.Data(:,1);

EM_Trq = EM_Trq.Data(:,1);

EM_Spd = EM_Spd.Data(:,1);

%% Plotting Engine speed, Engine Torque, Engine Power,Engine fuel rate,Engine fuel consumption wrt Time

figure(2)

subplot(4,1,1)

plot(Time,Eng_trq,'linewidth',1,'color','black')

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

xlabel('Time [s]')

ylabel('Torque [Nm]')

legend('Engine Torque')

grid on

subplot(4,1,2)

plot(Time,Eng_pwr,'linewidth',1,'color','blue')

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

xlabel('Time [s]')

ylabel('Power [KW]')

legend('Engine Power')

grid on

subplot(4,1,3)

plot(Time,Eng_fuel_rate,'linewidth',1,'color','red')

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

xlabel('Time [s]')

ylabel('Fuel Rate [%]')

legend('Engine Fuel Rate')

grid on

subplot(4,1,4)

plot(Time,Eng_fuel_consumption,'linewidth',1,'color','green')

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

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,'linewidth', 1,'color','r')

xlabel('Fuel consumption')

ylabel('Fuel Rate [%]')

title('Fuel economy Vs Fuel Consumption')

grid on

% figure(3)

% plot(Eng_fuel_consumption,Eng_fuel_rate,'linewidth', 1,'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)]);

%% State of Charge, Pack Current, And Terminal Voltage with respect to Time

figure(7)

subplot(3,1,1)

plot(Time,Pack_Current,'linewidth',1,'color','black')

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

xlabel('Time [s]')

ylabel('Current [W]')

legend('Pack Current')

grid on

subplot(3,1,2)

plot(Time,SOC,'linewidth',1,'color','blue')

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

xlabel('Time [s]')

ylabel('Charging [%]')

legend('State Of Charge')

grid on

subplot(3,1,3)

plot(Time,V_batt,'linewidth',1,'color','red')

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

xlabel('Time [s]')

ylabel('Voltage [V]')

legend('Battery Voltage')

grid on

%% Motor Speed, Torque, Motor input power with respect to Time

figure(8)

subplot(3,1,1)

plot(Time,EM_Pwr,'linewidth',1,'color','black')

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

xlabel('Time [s]')

ylabel('Power [W]')

legend('Electric Motor Power ')

grid on

subplot(3,1,2)

plot(Time,EM_Trq,'linewidth',1,'color','blue')

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

xlabel('Time [s]')

ylabel(' Torque [Nm]')

legend('Electric Motor Torque')

grid on

subplot(3,1,3)

plot(Time,EM_Spd,'linewidth',1,'color','red')

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

xlabel('Time [s]')

ylabel('Speed [Rpm]')

legend('Electrical Motor Speed')

grid on

%% Plotting EM Operating Points on motor input power Map

figure('Name','Electric Motor Operating Points');

hold on

grid on

title('Electric Motor Operating Points','fontsize',12);

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

p2=line([EM_map_spd(end)*30/pi EM_map_spd(end)*30/pi],[0 EM_max_trq(end)]);

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

p3=line([EM_map_spd(1)*30/pi EM_map_spd(1)*30/pi],[0 EM_max_trq(1)]);

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

[c,h] = contour(EM_map_spd'*30/pi,EM_map_trq,EM_inpwr_map');

clabel(c,h);

xlabel('Motor Speed [rpm]');

ylabel('Motor Torque [Nm]');

title('Motor Input Power Map [g/s]');

legend([p1 p3],{'Max Curve','Operating Points'});

axis([EM_map_spd(1)*30/pi EM_map_spd(end)*30/pi 0 EM_map_trq(end)]);

Result:

The model had been run for the different cycles and different parameters were analyzed and compared with the conventional vehicle model.

Comparision for Different Drive Cycle:

Two drive cycles UDDS and HWFET were considered and different system level parameters were analyzed.

UDDS:

HWFET:

Comparision for Speed for engine, wheel, and Transmission for both Drive cycles:

Fig(a) shows the speed of the engine wheel and transmission system for the UDDS cycle while Fig(b)shows the same for the HWFET cycle. In the UDDS cycle, we can observe the average speed of the wheel transmission and the engine is higher in the case of the HWFET cycle as the vehicle has to propel with higher velocity as compared with the UDDS cycle. It was also observed that in the UDDS cycle, there is a more frequent rise and fall in the speed of the wheel, engine, and transmission than in the HWFET cycle. This is due to the frequent stop-start condition in urban driving conditions.

UDDS:

HWFET:

Engine Analysis for HWFET cycle and UDDS cycle:

In the UDDS cycle, we can observe that there is a frequent rise and fall in engine power and torque while for the HWFET cycle, the fall and rise are comparatively smaller than in the UDDS cycle as shown in figures . This enables the HWFET cycle to work in a more efficient region as compared with the UDDS cycle which lowers the fuel consumption rate. The highest fuel consumption rate for the UDDS cycle is around 4 gm/sec, while for the HWFET cycle it is around 3.2 gm/sec. The HWFET cycle works more in a higher efficient region when compared with the UDDS cycle. In the HWFET cycle, we can observe the minimum amount of negative torque from the engine when compared with the UDDS cycle, in highway vehicles need not have to deal with frequent start-stop conditions. At the end of the drive cycle, the overall consumption was around 700 gm for the HWFET cycle and around 600 gm for the UDDS cycle.

UDDS:

HWFET:

Accelerator, Brake pedal position, and Gear Selected for different drive cycle

Figure a shows the brake pedal and accelerator pedal position for the UDDS cycle. We can observe that when the vehicle is following the UDDS cycle the use of the brake pedal is quite often and the intensity for the pedal is pressed is also high as compared with the HWFET cycle in figure b. The vehicle is often in a higher gear for the HWFET cycle as compared with the UDDS cycle. The vehicle has covered a distance of around 12 km for the UDDS cycle and around 17km for the HWFET cycle.

UDDS:

HWFET:

Analysis of an Electric Powertrain

In this We going to compare the data of UDDS & HWFET cycle of Fuel Economy, Gear Command, Pack Current, State Of Charge, Terminal Voltage are been discuss and plot are shown below in following order,

Gear Command for Vehicle speed and Distance:

UDDS: