LOOSELY COUPLED HARDWARE IN THE LOOP · 2015-01-19 · Daniel Auger LOOSELY COUPLED HARDWARE IN THE...

Stefano Longo Lilantha Samaranayake Daniel Auger

LOOSELY COUPLED HARDWARE IN THE LOOP

TESTING FOR ELECTRIC VEHICLE POWERTRAINS

CRANFIELD UNIVERSITY

BIGGER PICTURE

Developing FUTURE Vehicles Fundamental Understanding of Technologies for Ultra Reduced Emission Vechicles

FUTUREVehicles

Three work packagesThree cross-cutting scientific themes

Cross-cutting Theme 1: Thermal analysis, failure and degradation diagnosis and prognosis

Energy flow

Information flow

Cross-cutting Theme 2: Reduced order models

Cross-cutting Theme 3: Limits of performance by application of advanced control

Work Package 1: chemical energy storage systems plant modelling

Work Package 2: electrical energy

converter systems plant

modelling

Work Package 3: reduced order modelling and

controls

Unified modelling framework

Optimisation and control design

Fuel cells, batteries and

supercapacitors Electrical machinesPower electronics

OBJECTIVES

Minimise components degradation via control design

Hardware-in-the-loop testing

MINIMISE COMPONENTS DEGRADATION VIA CONTROL DESIGN

Batt. SoC

Sup. Cap SoC


Results

Conclusions

x�Degradation with Optimal control is lower compared to Maximum Torque Per Ampere control

x�Lower overshoot and settling time in Torque transients

x�Both transient and steady state voltage and current demands are lower; which allows

��Smaller battery��Smaller converter��Lower battery degradation��Lower cooling requirements

x�However, high computation power is required to complete real-time control law calculation within the sampling interval (0.5 ms).

x�Output quality depends on model accuracy.

Max. Torque / Ampere (MTPA) controlMethodology

Optimal Control for Minimizing Degradation in Electric Motors

Stefano Longo, Lilantha Samaranayake, Daniel AugerCentre for Automotive Engineering

FUTURE Vehicles

Objectives

Use multi objective optimization containing Closed Loop performance (JCL) and Degradation (JDEG) cost functions in deriving the optimal control law for Field Oriented Control of Permanent Magnet Synchronous Motors (PMSM) in Electric Vehicles.

Optimal Controller

IPMSM Electrical Circuit

IPMSM Electromagnetic

Circuit

vd, vq id, iq eWmWrefZVehicle Dynamics

>> Read Motor output>>Calculate Optimal Control Law using �Reduced order Motor Model to Minimize

Cost Function

�Predicted torque profile �i.e., Model Predictive Control

>>Apply First Step of Optimal Control Law>> Repeat

� �1CL DEGJ J JD D � �

oqqe iLZldL mdLR

cRdv

di odi

cdi

pmeOZ

odde iLZlqL mqLR

cRqv

qi oqi

cqi

d-axis

q-axis

d

q

pmO

sO

oqmqiL

odmdiL

si

odi

oqioqmqe iLjZ

odmde iLjZ

sv J

Worst possible transient from maximum positive torque to minimum negative torque transient is presented.

<< Comparison of motor degradation with Optimal Control & MTPA Control

Comparison of input voltages in the d-axis and q-axis as calculated by the Optimal Control & MTPA Control

<< Comparison of motor currents on d-axis and q-axis with Optimal Control and MTPA Control

Motor Data

Optimal Control Algorithm

>>


Results

Conclusions










FUTURE Vehicles

Objectives


Optimal Controller



Circuit



Cost Function




oqqe iLZldL mdLR

cRdv

di odi

cdi

pmeOZ

odde iLZlqL mqLR

cRqv

qi oqi

cqi

d-axis

q-axis

d

q

pmO

sO

oqmqiL

odmdiL

si

odi

oqioqmqe iLjZ

odmde iLjZ

sv J





Motor Data


>>

Results

Conclusions










FUTURE Vehicles

Objectives


Optimal Controller



Circuit



Cost Function




oqqe iLZldL mdLR

cRdv

di odi

cdi

pmeOZ

odde iLZlqL mqLR

cRqv

qi oqi

cqi

d-axis

q-axis

d

q

pmO

sO

oqmqiL

odmdiL

si

odi

oqioqmqe iLjZ

odmde iLjZ

sv J





Motor Data


>>


Results

Conclusions










FUTURE Vehicles

Objectives


Optimal Controller



Circuit



Cost Function




oqqe iLZldL mdLR

cRdv

di odi

cdi

pmeOZ

odde iLZlqL mqLR

cRqv

qi oqi

cqi

d-axis

q-axis

d

q

pmO

sO

oqmqiL

odmdiL

si

odi

oqioqmqe iLjZ

odmde iLjZ

sv J





Motor Data


>>

Results

Conclusions










FUTURE Vehicles

Objectives


Optimal Controller



Circuit



Cost Function




oqqe iLZldL mdLR

cRdv

di odi

cdi

pmeOZ

odde iLZlqL mqLR

cRqv

qi oqi

cqi

d-axis

q-axis

d

q

pmO

sO

oqmqiL

odmdiL

si

odi

oqioqmqe iLjZ

odmde iLjZ

sv J





Motor Data


>>

Results

Conclusions










FUTURE Vehicles

Objectives


Optimal Controller



Circuit



Cost Function




oqqe iLZldL mdLR

cRdv

di odi

cdi

pmeOZ

odde iLZlqL mqLR

cRqv

qi oqi

cqi

d-axis

q-axis

d

q

pmO

sO

oqmqiL

odmdiL

si

odi

oqioqmqe iLjZ

odmde iLjZ

sv J





Motor Data


>>

OBJECTIVES



HIL TEST RIG ExperimentsTest-Rig

Test Rig for Electric Vehicle Powertrain Optimization


FUTURE Vehicles

ObjectivesDesign and develop a test-rig which enables experimental verification of control techniques designed for powertrain components such as the motor, battery and supercapacitors in order to have

9�Improved closed loop control performance9�Minimized component degradation

DC Power linesAC Power linesSignal lines

3 Phase Brushless DC

Motor

DC3 Phase Brushless DC GeneratorDC

3 Ph. AC

3 Ph. AC

ACGrid

AC

DC

DC

DC

DC

DC

Cell/Module/Battery under test

Supercapacitors under test

Torque Sensor

Torque & Shaft SpeedRead: Voltage & Current

Write: Torque reference Read: Voltage & CurrentWrite: Load reference

HIL System

Battery State of Charge

Supercapacitor State of Charge

Power DemandPo

wer

/ 10

(W)

1. Degradation cost function evaluation based on power measurements using��Cumulative Work Ratio (CWR)��Cumulative Loss Ratio (CLR)��Cumulative Input Energy Ratio

(CIER).

2. Closed loop speed control loop performance of nonlinear 3 Phase Brushless DC motor plant with ��PID��Type 1 Fuzzy Logic Control

(T1-FLC) ��Type 2 Fuzzy Logic Control

(T2-FLC).

3. Optimal control to minimize battery degradation using battery-supercapacitor hybrid energy storage.

Experiment 1Power Demand

Pow

er /

10 (W

)

-18.1

-38.3

-12.9

-43.2-50

-45

-40

-35

-30

-25

-20

-15

-10

-5

0NEDC ARTEMIS

Percentage reduction in total energy demandPercentage reduction in peak power demand

Experiment 2

Experiment 3

(A)

(C)

(B)

(A) Experiment with combined ARTEMIS drive cycleTracking accuracy of CASE 1 > CASE 2 > CASE 3.Integral Absolute Error (IAE) CASE 1 < CASE 2 < CASE 3(B)Degradation cost functions, CWR, CLR & CIER CASE 1 > CASE 2 > CASE 3

(C)Same torque demand in all CASEs

CONCLUSIONS�CWR, CLR & CIER can successfully represent degradation.�They can be used as degradation cost functions in control design.

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ifinal

iinitial

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0 _

1_

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_

_

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³

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life

ifinal

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dtttTtPCLR

0 _

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life

ifinal

iinitial

t

ratedm

N

i

t

t imim

dtP

dtttTCWR

0 _

1__

_

_

Z

CONCLUSIONS�Peak power demand

T2-FLC < T1-FLC < PID�Total energy demand

T2-FLC < T1-FLC < PID

Battery – Supercapacitor hybrid optimal control gives the least battery degradation

HIL TEST RIG

IN SUMMARY

Development of control algorithms to mitigate

components degradation

IN SUMMARY

Development of control algorithms to mitigate components

degradation

IN SUMMARY

Optimal power flow control impacts on

degradation


degradation

IN SUMMARY


degradation


degradation

IN SUMMARY


degradation


degradation

A HIL rig to evaluate, test and optimise electric

powertrains

IN SUMMARY


degradation

A HIL rig to evaluate, test and optimise

electric powertrains


degradation

IN SUMMARY


degradation




degradation

HIL components under test do not need to match

in terms of power

IN SUMMARY


degradation




degradation

HIL components under test do not need to match in terms of

power

THANK YOU

LOOSELY COUPLED HARDWARE IN THE LOOP · 2015-01-19 · Daniel Auger LOOSELY COUPLED HARDWARE IN THE...

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