Modelling and control of an aftermarket HEV model

1. MODELLING AND CONTROL OF AN
AFTERMARKET PARALLEL HYBRID
ELECTRIC VEHICLE
AUTHOR: WISDOM ENANG

2. RESEARCH
BACKGROUND
INFORMATION

3. DEFINITION OF HYBRID ELECTRIC VEHICLE
 Hybrid electric vehicles (HEV) combine the internal combustion engine of
normal vehicles with battery and electric motor.
HEV ADVANTAGES
 Greater operating efficiency because HEVs use regenerative braking, which
helps to minimize energy loss and recover the energy used to slow down or
stop a vehicle.
 Greater fuel efficiency because hybrids consume significantly less fuel than
vehicles powered by ICE alone
 Cleaner operation because HEVs can run on alternative fuels: electricity
(which have lower emissions), thereby decreasing the dependency on
fossil fuels.

4. HEV CONFIGURATION USED IN THIS RESEARCH
AFTERMARKET
PARALLEL
HYRBRID
ELECTRIC VEHICLE
UNIQUENESS OF THIS MODEL
 Hybrid electric system added to conventional diesel engine vehicle.
 Hybrid system only controls electric motor thus preserving the original
vehicle warranty.
 Hybridization kits (electric motor and electric battery) are small in size and
affordable.

5. POWER FLOW POSSIBLE IN THE AFTERMARKET PARALLEL HEV
Motor only mode Power assist mode
Engine only mode
Regenerative braking mode Recharge control mode

6. RESEARCH
QUESTIONS

7. RESEARCH AIM
 To produce a robust real time controller for a parallel aftermarket HEV.
RESEARCH OBJECTIVES
 To produce a parallel HEV model capable of accurately predicting fuel
consumption in real world driving scenarios.
 To identify the interactions between human driver behaviour and fuel
consumption using the validated HEV model
 Computation of a rule based control for the HEV
 Optimal control of the HEV using Dynamic programming
 Intelligent control of the HEV using GPS information
 Intelligent control of the HEV using information from on-board driving
pattern learning algorithm taking in to consideration driver behaviour.

8. WHAT DATA IS NEEDED AND HOW IT CAN BE COLLECTED?
 Engine fuel
consumption map at
each torque and
speed operating
point.
 Engine transient
testing of real world
drive cycle for model
validation. Chassis Dynamometer
 Motor efficiency map
at each torque and
speed operating
point.
Electric motor test rig

9. WHAT HEV CONTROL OPTIONS ARE THERE?

10. WHAT HEV MODELLING OPTIONS ARE THERE?
This approach makes the
assumption that the vehicle
meets the target performance,
so that the vehicle speed is
supposed known a priori; thus
enjoying the advantage
simplicity and low
Backward or Kinematic Approach computational cost.
This approach makes use of a
driver model typically a PID
which compares that target
vehicle speed (drive cycle
speed) with the actual speed
profile, and then generates a
power demand profile which
is needed to follow the target
vehicle speed profile by
solving the differential motion
Quasi Static Approach equation of the vehicle.

11. RESEARCH
PROGRESS

12. OVERALL RESEARCH PROGRESS
Testing Modelling Control
Experimental testing Rules based HEV
of Engine for fuel control
consumption map HEV modelling
and model
Optimal HEV control
validation
using dynamic control
Intelligent HEV control
using GPS
Intelligent HEV control
using driver style
learning algorithm
Electric motor test
HEV model
for motor efficiency
validation
map
Real time
implementation of
HEV controllers

13. RESEARCH
RESULTS

14. RESULTS FROM EXPERIMENTAL TESTING
Motor testing results Engine testing results

15. HEV MODELLING STRUCTURE – QUASI STATIC APPROACH
gear_demand [gear_demand]
[wheel_torque] wheel torque (Nm) [gear_demand] gear_demand Engine Torque (Nm) [Engine_torque]
cycle_gear_demand cy cle_gear_demand Goto1 v ehicle v elocity (m/s) [vehicle_velocity]
From4 From3 Goto8
wheel torque (Nm) [wheel_torque] Goto
[shift_flag] shif t f lag (-) [vehicle_velocity] [engine_speed]
v ehicle v elocity (m/s) engine speed (RPM)
Goto2
cycle_speed_demand From11
From Goto7
cy cle_speed_demand (km/h) Shif t_f lag [-] [shift_flag]
[speed_signal] speed_signal
Wheel Tractiv e Force - Engine (N) -T-
Goto9 -T- Wheel tractiv e f orce - Engine (N) idle f lag [-] [idle_flag]
chassis_dyno_speed_dmd
From13 Goto3
Speed Signal [speed_signal] From5 Goto10
Goto13 [Motor_Torque] Motor Torque (Nm)
[vehicle_velocity] v ehicle_v elocity (m/s) [shift_flag] [Engine_Power]
Shif t f lag [-] Engine Power (KW)
Power demand (w) [Power_demand] From7
From2 Wheel Braking f orce (N) From12 Goto5
Goto15 [current_mode] Current_mode Drive Train
Driver Subsystem Terminator
Use orange switch inside to include or exclude
From22
Engine idling when cycle speed demand is 0
Vehicle Dynamics
[Motor_Power] Motor_power
Plots
From6
Scope1
[Power_demand] Power_demand
Initialize Model Parameters
From8
Engine Power
[Engine_Power] Engine_power
Scope3
From9
[SOC] Plot Analysis
From14
1
Constant SOC
Hy brid_Switch Motor Torque (Nm) [Motor_Torque]
0 [current_mode] [idle_flag] idle f lag (-)
Current Mode [vehicle_velocity]
Manual Switch v ehicle v elocity (m/s)
Hy brid Switch Goto4
Constant2 Fuel Consumption (g)
Goto6 From1
[Power_demand] Fuel Consumption g
Pdemand
SOC (%) [SOC]
From16
[Engine_torque] Engine_torque (Nm)
[motor_speed] Goto18
motor_speed (RPM)
Motor Power [Motor_Power] From18
From17 [Motor_Power] Motor Power demand (W)
[engine_speed] Goto20 [motor_speed]
Engine_speed (RPM) Motor Speed (RPM)
From20 Fuel Sav ings (%)
From19 Goto14
Hybrid Control System [engine_speed] Enginespeed (rpm) Fuel Savings %
Battery and Electric Motor Subsystem
From10
Note: Time delay factor added
to the Hybrid Scope2 Engine
Controller to make the system results Scope
more useful in real life
This approach makes use of a driver model typically a PID which compares that target vehicle speed
(drive cycle speed) with the actual speed profile, and then generates a power demand profile which is
needed to follow the target vehicle speed profile by solving the differential motion equation of the
vehicle.

16. HEV MODEL VALIDATION
HIGHLIGHTS FROM MODEL VALIDATION
 Model validation carried out over the NEDC (New European Drive Cycle)
 NEDC testing results proves it to be highly repeatable and hence why it has been chosen for the model
validation
 Level of accuracy achieved: 99% model accuracy

17. RULE BASED CONTROL STRUCTURE
Overview of the control structure Traction mode control structure
Braking mode control structure

18. RULE BASED CONTROL RESULTS
Instantaneous Fuel consumption
Drive cycle speed time profile Power split profile
profile comparison
Cumulative fuel consumption
Engine operating point Battery state of Charge profile
profile comparison
 State of charge boundaries: Highest allowable (80%) and lowest allowable (20%)
 Fuel savings achieved over the NEDC 12.58%
 Lowest state of charge encountered 27%

19. Future Work

20. Optimal HEV Intelligent HEV Real time
Intelligent HEV
control using control using driver implementation
control using
dynamic style learning of HEV
GPS
control algorithm controllers
PhD RESEARCH PROJECT GANTT CHART

21. QUESTIONS
PLEASE?

22. THANK YOU FOR
LISTENING