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Challenge

Project 2

HHemant Sagar1 Feb 2023
HEVMATLAB

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

%% Skill-Lync -> Hybrid Drives Development
% Project 1 - Conventional Vehicle Model
% By - Ullekh Gambhira
% Description:
%% Initialization File
% Initialization file for loading the variables for the Conventional vehicle simulation model
%% Vehicle Parameters
clc
M = 1500; % Vehicle weight [kg]
Cr = 0.015; % Rolling resistance coefficient
Rw = 0.35; % Rolling radius [m]
Cd = 0.33; % Drag coefficient
Af = 2; % Frontal area [m2]
rhoa = 1.225; % Air density [kg/m3]
Vwind = 0; % Wind velocity [kmph]
g = 9.81; % Gravitational acceleration [m/s2]
theta = 0; % Slope [deg]
myu_road_tire = 1; % Coefficient of friction between tire - road interface
%% Powertrain Parameters
FDR = 3.73; % Final Drive Ratio / Diff Ratio
DiffEff = 0.99; % Differential efficiency (assumed independent of operating conditions)
NumGear = 4; % Number of gears in transmission
TransGear = [1:NumGear]; % Transmission Gears
TransRatio = [3.06, 1.62, 1, 0.7]; % Corresponding Transmission ratios
TransEff = 0.90; % Transmission efficiency (assumed independent of operating conditions)
TC_SR = [0, .5, .6, .7, .8, .87, .92, .94, .96, .97, 1]; % Torque Converter Speed Ratio (Matlab default) -> TurbSpd/PumpSpd
TC_TR = [2.232, 1.5462, 1.4058, 1.2746, 1.1528, 1.0732, 1.0192, 1, 1, 1, 1]; % Torque Converter Torque Ratio (Matlab default) -> TurbTrq/PumpTrq
TC_Cf = 1e-4 * [.72558, .66322, .63463, .59042, .51331, .4144, .29287, .22444, .12186, .04386, 0]; % Torque Converter Capacity Factor (Matlab default) -> PumpTrq/PumpSpd^2 [Nm/rpm^2]
load Vehicle_Data/Engine_data_1_9L_Saturn_95kW
load Vehicle_Data/Trans_Shift_map
load Vehicle_Data/EM_Honda_Insight_10kW
load Vehicle_Data/ESS_NiMH6
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 = 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);

Simulink Blocks:-

The below Simulink blocks are the blocks that are introduced in the Conventional Vehicle Simulink Model to make it a Hybrid Electric Vehicle Model. Also, there are some changes made to the existing blocks to function according to the new blocks and act as a complete Hybrid Drive. The P1 Parallel Hybrid concept is applied to this Hybrid Vehicle.

P1 Parallel Hybrid

The electric machine is connected directly to the crankshaft of the internal combustion engine. P0 and P1 architectures do not allow the mechanical disconnection of the electric machine from the engine. On the other side P2, P3 or P4 configurations disconnects the electric machine from the engine through a clutch.

A hybrid system that is connected directly to the crankshaft of the engine with some type of clutch/fluid coupling between the electric motor and the gearing.

Two examples of P1 MHEV architectures are:

  • Honda Insight Hybrid 2009 (with Integrated Motor Assist technology)
  • Mercedes Benz S400 Bluehybrid 2010

These systems are known as integrated motor-generator (IMG), integrated motor assist (IMA), and flywheel assist system (FAS).

SIMULINK MODELLING
  • The Simulink model for the convection model is created using 3 main sub-blocks 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 motor torque, desired transmission gear and desired brake
    torque. These inputs are fed to the 'Powertrain' block which represents the drivetrain of the vehicle
  • A state flow machine is used to represent the supervisory control unit, which decides the operationg 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 :

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 Unit:

  • 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.

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.

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, Battery Pack, Electric Motor, Crank Hub, Transmission, Differential, Wheels and the vehicle
    body dynamics subsystems.
  • The control signals from the control model are transmitted via a bus to the corresponding blocks in the
    powertrain model.

Battery Model:

  • A Battery pack Subsystem is created with Electric power input, and Battery SOC as output.
  • 2 different subblocks have been created to determine the SOC and terminal voltage separately, the SOC being
    used to calculate the terminal voltage.

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

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.

Electric Motor Model :

  • 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.

Crank Hub :

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

Transmission Model :

  • 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
    efficiencv and enaine dunamics of the torque converter are apolied in this model.

Torque Converter :

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

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 :

%% Skill-Lync -> Hybrid Drives Development
% Project 2 - P1 Hybrid Electric Vehicle Model
% By - Ullekh Gambhira
% 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 P1 Hybrid Electric 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]
Eng_Fuel_Rate = out.Eng_Fuel_Rate; % Engine fuel rate [%]
EM_Spd = out.EM_Spd*30/pi; % Motor speed [rpm]
EM_Trq = out.EMTrq; % Motor torque [Nm]
EM_Pwr = out.ElecPwr; % Motor input power [kW]
PackCurrent = out.Pack_Current; % Packcurrent
SOC = out.SOC; % State of charge
Voltage = out.Veh_Spd; % Voltage
%% Data Analysis
Eng_Pwr = Eng_Spd.*pi./30.*Eng_Trq./1000; % Engine brake power [kW]
rho_petrol = 0.7197; % Density of petrol [kg/L]
kmpl = Veh_Dist(end)/(Eng_Fuel_Consump(end)/1000/rho_petrol); % Fuel economy [kmpl]
%% Plotting vehicle characteristics time trace
figure()
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(1:136901), Gear_Cmd, 'linewidth', 1, 'Color', 'black');
xlim([0, Time(end)]);
xlabel('Time [s]');
ylabel('Gear');
%% Plotting engine operating points on engine fuel map
figure('Name','Engine Operating Points');
hold on
grid on
title('Engine Operating Points','fontsize',12);
p1=plot(Eng_map_spd*30/pi,Eng_max_trq,'linewidth',2,'color','r');
p2=line([Eng_map_spd(end)*30/pi Eng_map_spd(end)*30/pi],[0 Eng_max_trq(end)]);
set(p2,'linewidth',1,'linestyle','--','color','k');
p3=line([Eng_map_spd(1)*30/pi Eng_map_spd(1)*30/pi],[0 Eng_max_trq(1)]);
set(p3,'linewidth',1,'linestyle','--','color','k');
[c,h] = contour(Eng_map_spd'*30/pi,Eng_map_trq,Eng_fuel_map');
clabel(c,h);
xlabel('Engine Speed [rpm]');
ylabel('Engine Torque [Nm]');
title('Engine Fuel Consumption Map [g/s]');
p4 = plot(Eng_Spd,Eng_Trq,'.','color','k');
legend([p1 p4],{'Max Curve','Operating Points'});
axis([Eng_map_spd(1)*30/pi Eng_map_spd(end)*30/pi 0 Eng_map_trq(end)]);
%% Engine outputs
figure(3)
subplot(2,1,1)
grid on
plot(Time,Eng_Fuel_Rate, 'linewidth', 1, 'Color', 'black')
xlabel('Time [s]')
ylabel('Engine Fuel Rate [%]')
subplot(2,1,2)
grid on
plot(Time,Eng_Fuel_Consump, 'linewidth', 1, 'Color', 'red')
xlabel('Time [s]')
ylabel('Engine Fuel Consumption [g]')
figure(4)
subplot(3,1,1)
grid on
plot(Time,Eng_Trq, 'linewidth', 1, 'Color', 'black')
xlabel('Time [s]')
ylabel('Engine Torque [Nm]')
subplot(3,1,2)
grid on
plot(Time,Eng_Spd, 'linewidth', 1, 'Color', 'red')
xlabel('Time [s]')
ylabel('Engine Speed [rpm]')
subplot(3,1,3)
grid on
plot(Time,Eng_Pwr, 'linewidth', 1, 'Color', 'black')
xlabel('Time [s]')
ylabel('Engine brake power [kW]')
%% Vehicle Dynamics
figure(5)
subplot(2,1,1)
grid on
plot(Time, Veh_Spd, 'linewidth', 1, 'Color', 'black')
xlabel('Time [s]')
ylabel('Vehicle Speed [kmph]')
subplot(2,1,2)
grid on
plot(Time, Veh_Dist, 'linewidth', 1, 'Color', 'red')
xlabel('Time [s]')
ylabel('Distance traveled by vehicle [km]')
%% Motor
figure(6)
subplot(3,1,1)
grid on
plot(Time, EM_Spd, 'linewidth', 1, 'Color', 'black')
xlabel('Time [s]')
ylabel('Motor speed [rpm]')
subplot(3,1,2)
grid on
plot(Time, EM_Trq, 'linewidth', 1, 'Color', 'red')
xlabel('Time [s]')
ylabel('Motor torque [Nm]')
subplot(3,1,3)
grid on
plot(Time, EM_Pwr, 'linewidth', 1, 'Color', 'black')
xlabel('Time [s]')
ylabel('Motor input power [kW]')
%% Batery
figure(7)
subplot(3,1,1)
grid on
plot(Time, PackCurrent, 'linewidth', 1, 'Color', 'black')
xlabel('Time [s]')
ylabel('Packcurrent [Amp]')
subplot(3,1,2)
grid on
plot(Time, SOC, 'linewidth', 1, 'Color', 'red')
xlabel('Time [s]')
ylabel('State of charge')
subplot(3,1,3)
grid on
plot(Time, Voltage, 'linewidth', 1, 'Color', 'black')
xlabel('Time [s]')
ylabel('Voltage [V]')
%% Motor operating points on the motor input power map
figure('Name','Motor Operating Points');
hold on
grid on
title('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 [kW]');
p4 = plot(EM_Spd,EM_Trq,'.','color','k');
legend([p1 p4],{'Max Curve','Operating Points'});
axis([EM_map_spd(1)*30/pi EM_map_spd(end)*30/pi 0 EM_map_trq(end)]);

1.UDDS Drive Cycle:

HWFET Drive Cycle:

UDDS Drive Cycle

Engine Fuel Consumption The Map

Max curve remains the same for both the drive cycles. Operating points of the UDDS drive cycle seem to be moving at a slower pace to higher Engine Speed from starting. But not in the case of the HWFET drive cycle, operating points move towards a higher speed at an instant from the starting point. Operating points on the highway seem more stable than in the city condition.

HWFET Drive Cycle

Engine Fuel Consumption Map

UDDS Drive Cycle

In the below plot with an increase in Engine torque, brake power increases and when there is a decrease in Engine torque, brake power also decreases. Comparing the drive cycles, in UDDS, more fluctuations can be seen than in the HWFET condition. Both have a negative value as well.

Engine fuel rate and Engine speed plot have almost the same movement as the other plots. Engine Speed in highway conditions is at maximum and most of the time consistent without any fluctuation. In city conditions, the Engine Speed fluctuates most of the time and is not consistent.

Engine fuel consumption gradually increases as the Engine fuel rate in both drive cycles.

HWFET Drive Cycle

UDDS Drive Cycle

In UDDSthe drive cycle the Vehicle speed oscillate more than that of the HWFET condition. In highway conditions, the Vehicle Speed seems to be consistent, ent and the vehicle moves at high speed most of the distance traveled.

Distance traveled by the vehicle curve in the UDDS drive cycle seems to have some slight up and down movements whereas in highway conditions the distance traveled curve is a straight line without any up and down movements.

HWFET Drive Cycle

UDDS Drive Cycle

In the below case of the Motor, the pattern or the reaction of the curves is the same. Motor input power follows the Motor torque curve pattern in both drive cycles. The pattern of the Motor speed and Motor torque is iso the same in both drive cycles. With the speed, the curve increases torque curve also increases but when the vehicle is at moving at some speed the Motor torque stays at 0 and this movement is similar in both drive cycles.

The oscillation of the Motor speed curve is more in UDDS drive cycle and the vehicle is moving at maximum Motor speed consistently in the HWFET drive cycle.

HWFET Drive Cycle

UDDS Drive Cycle

The Packcurrent and Voltage curve are opposite to each other. Increasing in Packcurrent, Voltage decreases, and vice versa. This movement is the same for both drive cycles. SOC curve gradually decreases as the battery is discharging due to the usage by the Motor and it is similar in both the drive cycles.

Due to the city conditions, the UDDS drive cycle has oscillation in the curve whereas in highway conditions HWFET drive cycle does not have any.

HWFET Drive Cycle

UDDS Drive Cycle

In Motor Input Power Map for both UDDS and HWFET drive cycles, the max curve remains the same. Even the movements of the Operating points do not have any variations.

HWFET Drive Cycle

Fuel economy numbers (kmph) for UDDS and HWFET cycle.

UDDS :

For convectional Vehicle Model (Projet 1)

Fuel Economy = 28.77 kmpl

For P1 Hybrid Vehicle Model

Fuel Economy = 15.429 Kmpl

  • Fuel economy improves as engine eliminates negative torque and negative power. Negative Energy can be revived with regional breaking.

HWFET :

For convectional Vehicle Model (Projet 1)

Fuel Economy = 40.27 kmpl

For P1 Hybrid Vehicle Model

Fuel Economy = 45.95 kmpl

  • Fuel efficiency is slightly improved as highway has less negative power when driving. But all negative power will be regenerated with regen braking and battery SOC will increase.
  • The City Fuel Economy is much lower than highway drive as the brake padel engagement and idle time on drive cycle is less.
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