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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.
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.
POWER FLOW POSSIBLE IN THE AFTERMARKET PARALLEL HEV Motor only mode Power assist mode Engine only modeRegenerative braking mode Recharge control mode
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.
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
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 lowBackward 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.
OVERALL RESEARCH PROGRESS Testing Modelling ControlExperimental 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 modelfor motor efficiency validation map Real time implementation of HEV controllers
RESULTS FROM EXPERIMENTAL TESTINGMotor testing results Engine testing results
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 1Constant 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 Goto4Constant2 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 lifeThis 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 isneeded to follow the target vehicle speed profile by solving the differential motion equation of thevehicle.
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
RULE BASED CONTROL STRUCTUREOverview of the control structure Traction mode control structure Braking mode control structure
RULE BASED CONTROL RESULTS Instantaneous Fuel consumptionDrive 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%
Optimal HEV Intelligent HEV Real time Intelligent HEVcontrol using control using driver implementation control using dynamic style learning of HEV GPS control algorithm controllers PhD RESEARCH PROJECT GANTT CHART