Project 2

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.

Task :

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

Answer :

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.

P1 Hybrid

• 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.

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 - Ullekh Gambhira 

% 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
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 = 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,

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

Theory:-

P1 Mild Hybrid Electric Vehicle:-

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 advantage of this solution is that the electric motor can provide higher torque than the BiSG, since there is no belt limitation (due to slip). 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.

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.

Process:-

Load the Project2 folder as the Current Folder in Matlab.

Run the “Project2_InitFile.m” Matlab script.

The Drive cycle selection is done here.

After run this coding file open simulink model.

SIMULINK MODELLING

2] Driver:-

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.

3] Controller:-

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 desired motor torque and desired brake torque. These inputs are fed to the ‘Powertrain’ block which represents the drivetrain of the vehicle. In this we require to create stateflow block it explain in next figure.

4] Stateflow for Controller:-

In this we created four conditions as stop, start, drive and regenration braking. The initial block represent the stop condition. In which the engine is off. The engine and motor throttle conditions are zero. Then the second block represent start condition. In which the mode is 1 the electric motor throttle is only used to provide the required torque and power to the vehicle. the engine is off and engine throttle conditionis zero.

The state 1 and state 2 are connected using the links i.e. it moves from state 1 to state 2 when acceleration is less than one or SOC is less then the minimum SOC.

State 3 Represtents mode 2 ii.e. engine is on and the engine throttle is also having positive value from the driver but the electric motor throttle is zero. It changes form state 2 to state 3 when engine speed is graeter than or equal to engine idle speed.

state 4 represents regenrative braking which is model 3 In this state the engine is on but the engine throttle is having no input from driver whereas the electric moto receives the negative power due to braking.

The inputs are given to chart is Acceleration, barke, engine speed, vehicle speed, SOC, and clutch lockup.

The output we get from the chart is mode, engine condition, engine throttle condition, and electric motor throttle condition.

Modification done in all Unit modeling. they showed in next figures.

5] 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.

6] 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.

7] Shiftlogic In this unit:-

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 :

• 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⋅TeRω=MdVvehdt+12ρaAfCd(Vveh+Vωd)2+CrMgcosθ+Mgsinθ��⋅��⋅����=�����ℎ��+12������(���ℎ+���)2+����cos�+��sin�

ηt⋅λt⋅TeRω=��⋅��⋅����=Tractive force

12ρaAfCd(Vveh+Vωd)2+CrMgcosθ+Mgsinθ=12������(���ℎ+���)2+����cos�+��sin�= Road Load

Where,

ρa=��= Air Density

Af=��= Frontal Area

Cd=��= Drag Coefficient

Cr=��= 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 Calculation and stored in the workspace.

SIMULATION RESULTS

1. UDDS Drive Cycle :

Drive Cycle and Actual Velocity

Model

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_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]


%% 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, 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)]);

Result :

1. Drive Cycle

UDDS :

HWFET :

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

UDDS :

For convectional Vehicle Model (Projet 1)

Average Speed[kmph]31.35AverageSpeed[���ℎ]31.35=MaximumSpeed[kmph]91.99MaximumSpeed[���ℎ]91.99=Fuel Economy[kmpl]16.89Fuel Economy[����]16.89

For P1 Hybrid Vehicle Model

Fuel Economy = 18.07 Kmpl

Average Speed[kmph]31.531AverageSpeed[���ℎ]31.531=MaximumSpeed[kmph]91.395MaximumSpeed[���ℎ]91.395=Fuel Economy[kmpl]18.072Fuel Economy[����]18.072

• 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)

Average Speed[kmph]77.683AverageSpeed[���ℎ]77.683=MaximumSpeed[kmph]96.925MaximumSpeed[���ℎ]96.925=Fuel Economy[kmpl]21.228Fuel Economy[����]21.228

For P1 Hybrid Vehicle Model

Fuel Economy = 22.00 Kmpl

Average Speed[kmph]77.682AverageSpeed[���ℎ]77.682=MaximumSpeed[kmph]96.401MaximumSpeed[���ℎ]96.401=Fuel Economy[kmpl]21.997Fuel Economy[����]21.997

• 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.

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 fluctates 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 troque, 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

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

7. The Motor speed, torque, motor input power.

UDDS:

• Motor torque request is for engine start and region braking only. Electric input power is calculated by referring the motor speed according to the EM motor torque request.

HWFET:

• EM motor torque demand is low as the vehicle moves at high speeds and less idle on highway drive
  

8. The Pack voltage, current and Soc

UDDS:

• Battery SOC is responding according to the Motor torque request and the Regen braking.
  

HWFET:

• Battery SO increases on highway drive cycles due to lower EMotor usage and region braking battery charging.

9. The motor operating points on the motor input power map, with the motor max motoring and generating
torque curves.

• Electric motor can operates between max motoring torque curve and max generating torque curve.

Threeshold Value in This controller: Electric Motor Control Unit:-

Brake Control Unit:-

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

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.

Electric Motor:-

Crankhub:-

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.

Torque Converter:-

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.

Wheel:-

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.

Vehicle Dynamics:-

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

Battery Modeling:-

Estimated storage system:-

In this we give input as power then it multiply and give to pack current. then from soc estimation meet output SOC its give to terminal voltage as input and then meet terminal voltage as output.

Terminal Voltage:-

In this use input SOC it is connected to 1d lookup table 1st table variable interpolated as state of charge and open voltage circuit. In 2nd table variable interpolated as resistance discharge and state of charge and in 3rd table variable interpolated as resistance charge and state of charge. Then it is passed to switch block for controlling the threeshold. then they give voltage limits as max volts and min volts. then meet terminal Voltage.

Conclusion :

The Various performance of characteristics and operating point of UDDS drive cycle and HWFET drive cycle are analysed and graphs are plotted.

• Time trace for engine, Transmission input, Transmission output and wheelspeed with respect to time.
• Engine torque, Engine power, Fuel rate and Fuel consumption with respect to time.
• Accelerator and brake pedal, gear command and vehicle desired, actual speed, vehicle distance with respect to time.
• Engine operating points on the engine fuel map with respect to time
• Motor speed, Torque, Motor input power with respect to time.
• Pack voltage, Current and State of charging with respect to time.
• The motor operating points on the motor input power map with respect to time.

and also it comparing with HWFET drive cycle and their numbers also compared.