ev powertrain simulations in saber

49
Alan Courtay October 29, 2015 Paris Saber Seminar, La Defense Modeling of PMSM Motor Drive Multi Time Scale Analysis with Saber

Upload: alan-courtay

Post on 14-Apr-2017

452 views

Category:

Engineering


3 download

TRANSCRIPT

Page 1: EV Powertrain Simulations in Saber

Alan Courtay

October 29, 2015

Paris Saber Seminar, La Defense

Modeling of PMSM Motor Drive Multi Time Scale Analysis with Saber

Page 2: EV Powertrain Simulations in Saber

© 2015 Synopsys, Inc. 2

Simplified Electric Vehicle Powertrain Modeled after Market Available Electric Vehicle

Published

PMSM Electric Motor Max power / torque: 80 kW / 280 Nm

Li-Ion Battery

Total energy: 24 kWh

Max power > 90 kW

Number of cells: 192 (2 parallel, 96 series)

Cell voltage: 3.8 V

Nominal system voltage: 364.8 V

Gear Ratio 1/7.94

Curb Weight 1521 kg

0-100 km/h ~ 10 sec

Drag Coefficient 0.28

Inverter Frequency 5 kHz

Assumed

PMSM Electric Motor Max power / torque: 100 kW / 178 Nm, 8 poles

Inverter Efficiency 90%

Gear Efficiency 97%

Wheel Radius 0.3 m

Page 3: EV Powertrain Simulations in Saber

© 2015 Synopsys, Inc. 3

Simplified Electric Vehicle Powertrain Modeled after Market Available Electric Vehicle

Published

PMSM Electric Motor Max power / torque: 80 kW / 280 Nm

Li-Ion Battery

Total energy: 24 kWh

Max power > 90 kW

Number of cells: 192 (2 parallel, 96 series)

Cell voltage: 3.8 V

Nominal system voltage: 364.8 V

Gear Ratio 1/7.94

Curb Weight 1521 kg

0-100 km/h ~ 10 sec

Drag Coefficient 0.28

Inverter Frequency 5 kHz

Assumed

PMSM Electric Motor Max power / torque: 100 kW / 178 Nm, 8 poles

Inverter Efficiency 90%

Gear Efficiency 97%

Wheel Radius 0.3 m

IPMSM model from

JMAG-RT Motor Model

Library

Page 4: EV Powertrain Simulations in Saber

© 2015 Synopsys, Inc. 4

1 2

3 4

• Level 1

– Behavioral Li-Ion battery

– Dynamic thermal dq inverter and PMSM

– Thermal network

• Level 2

– Average/non-switching inverter /w TLU losses

– LdLq or detailed FEA-based PMSM

• Level 3

– Ideal switch inverter /w TLU losses

• Level 4

– Improved datasheet-driven IGBT1

Abstraction Levels

Page 5: EV Powertrain Simulations in Saber

© 2015 Synopsys, Inc. 5

1

Page 6: EV Powertrain Simulations in Saber

© 2015 Synopsys, Inc. 6

1 Simplified Vehicle Dynamics

Page 7: EV Powertrain Simulations in Saber

© 2015 Synopsys, Inc. 7

1

ia,va

ib,vb

ic,vc

a

b

c

Sinusoidal currents and switching/PWM voltages are abstracted to only

retain phase and amplitude of signals in synchronous reference frame

iq

id

vq

vd

i

v

Page 8: EV Powertrain Simulations in Saber

© 2015 Synopsys, Inc. 8

1

FEA-based look-up tables used for

flux saturation Ld(id) and Lq(iq), and

speed/current dependent iron loss

Page 9: EV Powertrain Simulations in Saber

© 2015 Synopsys, Inc. 9

1 Reactance Torque

Page 10: EV Powertrain Simulations in Saber

© 2015 Synopsys, Inc. 10

1

N S

Reactance Torque

Page 11: EV Powertrain Simulations in Saber

© 2015 Synopsys, Inc. 11

1 Reactance Torque

angle

torque

90o

Page 12: EV Powertrain Simulations in Saber

© 2015 Synopsys, Inc. 12

1 Reluctance Torque

Br

Hc

m

The permanent magnets have low

permeability / high reluctance (~ air

gap). The rotor orients itself in the

position of least flux resistance.

Page 13: EV Powertrain Simulations in Saber

© 2015 Synopsys, Inc. 13

1 Average Inverter Model

including Efficiency Map

Page 14: EV Powertrain Simulations in Saber

© 2015 Synopsys, Inc. 14

Switching Losses 1

≈ 𝛼 ∙ 𝒗𝒐𝒇𝒇 ∙ 𝒊𝒐𝒏

on+off

𝑷𝒔𝒘 = 𝑬𝒔𝒘 ∙ 𝒇𝒔

= 𝑬𝒔𝒘 (𝒗𝒐𝒇𝒇, 𝒊𝒐𝒏) rec +

𝒊𝒐𝒏 (𝑨) 𝒗𝒐𝒇𝒇 (𝑽)

𝑬𝒔𝒘 (𝑱)

Page 15: EV Powertrain Simulations in Saber

© 2015 Synopsys, Inc. 15

v

i

one 1D look-up table: 𝑃𝑐(𝑖) = 𝑖. 𝑣(𝑖)

Conduction Losses 1

Page 16: EV Powertrain Simulations in Saber

© 2015 Synopsys, Inc. 16

1

Field Oriented Control

Vector Control

Page 17: EV Powertrain Simulations in Saber

© 2015 Synopsys, Inc. 17

1

𝑖∗2 = 𝑖𝑑

∗2 + 𝑖𝑞∗2

𝜕𝑇

𝜕𝑖∗= 0

𝑖𝑑∗ =

𝜑𝑚 − 𝜑𝑚2 + 8 𝐿𝑞 − 𝐿𝑑

2𝑖∗2

4 𝐿𝑞 − 𝐿𝑑

𝑖𝑞∗ = 𝑠𝑔𝑛(𝑖∗) 𝑖∗2 − 𝑖𝑑

∗2

Maximum Torque Per Amp

𝑖𝑑∗

𝑖𝑞∗

𝑖∗

Field Oriented Control Field Oriented Control

MTPA 𝑖∗

𝑖𝑑∗

𝜃𝑖

𝑖𝑞∗

𝑖𝑑

𝑖𝑞

Page 18: EV Powertrain Simulations in Saber

© 2015 Synopsys, Inc. 18

1 Flux Weakening

𝑖𝑑∗

𝑖𝑞∗

𝑖𝑑

𝑖𝑞

Field Oriented Control

Page 19: EV Powertrain Simulations in Saber

© 2015 Synopsys, Inc. 19

1 Flux Weakening

𝑖𝑑∗

𝑖𝑞∗

𝑖𝑑

𝑖𝑞

Field Oriented Control

𝑉𝑞 = 𝑅𝑖𝑞 + 𝐿𝑞𝑑𝑖𝑞𝑑𝑡

+ 𝜔 𝐿𝑑𝑖𝑑 + 𝜑𝑚

𝑉𝑑 = 𝑅𝑖𝑑 + 𝐿𝑑𝑑𝑖𝑑𝑑𝑡

− 𝜔𝐿𝑞𝑖𝑞

𝑇 =3

4𝑝 𝜑𝑚𝑖𝑞 + 𝐿𝑑 − 𝐿𝑞 𝑖𝑑𝑖𝑞

R neglected,

steady-state

Page 20: EV Powertrain Simulations in Saber

© 2015 Synopsys, Inc. 20

1 Flux Weakening

𝑖𝑑∗

𝑖𝑞∗

𝑖𝑑

𝑖𝑞

Field Oriented Control

𝑉𝑞 = 𝜔 𝐿𝑑𝑖𝑑 + 𝜑𝑚

𝑉𝑑 = −𝜔𝐿𝑞𝑖𝑞

𝑇 =3

4𝑝 𝜑𝑚𝑖𝑞 + 𝐿𝑑 − 𝐿𝑞 𝑖𝑑𝑖𝑞

At high speed, back-EMF

exceeds DC link voltage

Page 21: EV Powertrain Simulations in Saber

© 2015 Synopsys, Inc. 21

1 Flux Weakening

𝑖𝑑∗

𝑖𝑞∗

𝑖𝑑

𝑖𝑞

Field Oriented Control

𝑉𝑞 = 𝜔 𝐿𝑑𝑖𝑑 + 𝜑𝑚

𝑉𝑑 = −𝜔𝐿𝑞𝑖𝑞

𝑇 =3

4𝑝 𝜑𝑚𝑖𝑞 + 𝐿𝑑 − 𝐿𝑞 𝑖𝑑𝑖𝑞

Increase current angle (negative

component of id) to “weaken”

magnet flux and reduce back-EMF

Page 22: EV Powertrain Simulations in Saber

© 2015 Synopsys, Inc. 22

𝑖𝑑∗

𝑖𝑞∗

𝑖𝑑

𝑖𝑞

Field Oriented Control

MTPA 𝑖

−𝜑𝑚𝐿𝑑

𝜑𝑚𝐿𝑞 − 𝐿𝑑

𝑖𝑞

𝑖𝑑

1 Flux Weakening

𝑉𝑞 = 𝜔 𝐿𝑑𝑖𝑑 + 𝜑𝑚

𝑉𝑑 = −𝜔𝐿𝑞𝑖𝑞

𝑇 =3

4𝑝 𝜑𝑚𝑖𝑞 + 𝐿𝑑 − 𝐿𝑞 𝑖𝑑𝑖𝑞

Increase current angle (negative

component of id) to “weaken”

magnet flux and reduce back-EMF

𝑣2 = 𝑣𝑑2 + 𝑣𝑞

2

Voltage Limit Ellipse

𝑣2

𝜔2 = 𝐿𝑑𝑖𝑑 + 𝜑𝑚2 + 𝐿𝑞

2𝑖𝑞2

𝜃𝑖

Page 23: EV Powertrain Simulations in Saber

© 2015 Synopsys, Inc. 23

𝑖𝑑∗

𝑖𝑞∗

𝑖𝑑

𝑖𝑞

Field Oriented Control

MTPA

𝑖

−𝜑𝑚𝐿𝑑

𝜑𝑚𝐿𝑞 − 𝐿𝑑

Increasing Speed

𝑖𝑞

𝑖𝑑

1 Flux Weakening

𝑉𝑞 = 𝜔 𝐿𝑑𝑖𝑑 + 𝜑𝑚

𝑉𝑑 = −𝜔𝐿𝑞𝑖𝑞

𝑇 =3

4𝑝 𝜑𝑚𝑖𝑞 + 𝐿𝑑 − 𝐿𝑞 𝑖𝑑𝑖𝑞

Increase current angle (negative

component of id) to “weaken”

magnet flux and reduce back-EMF

𝜃𝑖

𝑣2 = 𝑣𝑑2 + 𝑣𝑞

2

Voltage Limit Ellipse

𝑣2

𝜔2 = 𝐿𝑑𝑖𝑑 + 𝜑𝑚2 + 𝐿𝑞

2𝑖𝑞2

Page 24: EV Powertrain Simulations in Saber

© 2015 Synopsys, Inc. 24

1

Field Oriented Control

𝑉𝑞 = 𝜔 𝐿𝑑𝑖𝑑 + 𝜑𝑚

𝑉𝑑 = −𝜔𝐿𝑞𝑖𝑞

𝑇 =3

4𝑝 𝜑𝑚𝑖𝑞 + 𝐿𝑑 − 𝐿𝑞 𝑖𝑑𝑖𝑞

Feedforward Compensation

Page 25: EV Powertrain Simulations in Saber

© 2015 Synopsys, Inc. 25

1 • Analyze system efficiency over long driving cycles

• Evaluate energy flow in critical regimes

(deceleration, braking)

• Handle power dissipation and cooling

• Design stable motor control (e.g. FOC)

Page 26: EV Powertrain Simulations in Saber

© 2015 Synopsys, Inc. 26

1 • Analyze system efficiency over long driving cycles

• Evaluate energy flow in critical regimes

(deceleration, braking)

• Handle power dissipation and cooling

• Design stable motor control (e.g. FOC)

Page 27: EV Powertrain Simulations in Saber

© 2015 Synopsys, Inc. 27

1 2

Sinusoidal currents and voltages (no switching)

Page 28: EV Powertrain Simulations in Saber

© 2015 Synopsys, Inc. 28

1 2

a

b

c

𝜃𝑚

𝜃𝑖

i

Accounts for

1. Mutual coupling between phases

2. Flux saturation

3. Spatial harmonics

Page 29: EV Powertrain Simulations in Saber

© 2015 Synopsys, Inc. 29

1 2

• Analyze system dynamics

• Evaluate energy flow in critical regimes

(deceleration, braking)

• Design stable motor control (e.g. FOC)

• Evaluate torque ripples

Motor

Torque

Regenerative

Braking

Sloped Terrain Startup

Page 30: EV Powertrain Simulations in Saber

© 2015 Synopsys, Inc. 30

1 2

• Analyze system dynamics

• Evaluate energy flow in critical regimes

(deceleration, braking)

• Design stable motor control (e.g. FOC)

• Evaluate torque ripples

Torque ripples due to

spatial harmonics

Page 31: EV Powertrain Simulations in Saber

© 2015 Synopsys, Inc. 31

1 2

3

• Design PWM control (e.g. compensate dead time distortion)

• Mitigate faults in critical regimes (e.g. in flux weakening mode)

Dead time distortion

(corrected and uncorrected)

Page 32: EV Powertrain Simulations in Saber

© 2015 Synopsys, Inc. 32

1 2

3 4

• Optimize gate drive tradeoff losses vs. EMI noise

• Control current/voltage overshoot

• Prevent accidental turn-on

𝑖 = 𝐶𝑐𝑔 ∙𝑑𝑉𝑐𝑒

𝑑𝑡≫ 1

Vg < Vge(th)

Rg

Vgei > Vge(th) c

e

𝑉 = 𝐿𝑒 ∙𝑑𝑖𝑐𝑑𝑡

≪ −1

Accidental turn-on

mechanisms

Page 33: EV Powertrain Simulations in Saber

© 2015 Synopsys, Inc. 33

2016.03 IGBT Tool

• Improved matching of transient

characteristics

– Cge made non-linear

– Control of turn-off voltage oscillations

– Decoupling between turn-on and turn-off

• Easier characterization

– Optimizer at most steps, including

transient characteristics

– Turn-on and turn-off characteristics

combined in one view

– Improved DC anchor points

– Library of pre-characterized components

– Numerous bug fixes

Page 34: EV Powertrain Simulations in Saber

© 2015 Synopsys, Inc. 34

IGBT Principle Collector/Anode

Emitter/Cathode

P+ Emitter

Gate

P

N- Base

P+

N+

• Two junctions

– J1 space charge region develops

when Vce < 0

– J2 space charge region develops

when Vce > 0 and Vge < Vge(th)

– Wide and low doped N- base region

→ large blocking voltage

• BJT+MOSFET

– Insulated gate → voltage control

– Holes injected from P+ emitter →

conductivity modulation

– High forward conduction current

density: 𝑖𝑐 = 𝑖𝑚𝑜𝑠 + 𝑖𝑝

• Slow removal of carriers in the

base → longer switching time

during turn-off and tail current

J1

J2

+

+

Page 35: EV Powertrain Simulations in Saber

© 2015 Synopsys, Inc. 35

IGBT Principle Collector/Anode

Emitter/Cathode

P+ Emitter

Gate

P

N- Base

Rb

PNP

N-MOS

P+

N+

imos ip

(𝛽)

+ +

+

holes

electrons

• Two junctions

– J1 space charge region develops

when Vce < 0

– J2 space charge region develops

when Vce > 0 and Vge < Vge(th)

– Wide and low doped N- base region

→ large blocking voltage

• BJT+MOSFET

– Insulated gate → voltage control

– Holes injected from P+ emitter →

conductivity modulation

– High forward conduction current

density: 𝑖𝑐 = 𝑖𝑚𝑜𝑠 + 𝑖𝑝

• Slow removal of carriers in the

base → longer switching time

during turn-off and tail current

Page 36: EV Powertrain Simulations in Saber

© 2015 Synopsys, Inc. 36

IGBT Principle Collector/Anode

Emitter/Cathode

P+ Emitter

Gate

P

N- Base

P+

N+

imos ip +

+

• Two junctions

– J1 space charge region develops

when Vce < 0

– J2 space charge region develops

when Vce > 0 and Vge < Vge(th)

– Wide and low doped N- base region

→ large blocking voltage

• BJT+MOSFET

– Insulated gate → voltage control

– Holes injected from P+ emitter →

conductivity modulation

– High forward conduction current

density: 𝑖𝑐 = 𝑖𝑚𝑜𝑠 + 𝑖𝑝

• Slow removal of carriers in the

base → longer switching time

during turn-off and tail current

Page 37: EV Powertrain Simulations in Saber

© 2015 Synopsys, Inc. 37

IGBT Principle Collector/Anode

Emitter/Cathode

P+ Emitter

Gate

P

N- Base

P+

N+

+ • Two junctions

– J1 space charge region develops

when Vce < 0

– J2 space charge region develops

when Vce > 0 and Vge < Vge(th)

– Wide and low doped N- base region

→ large blocking voltage

• BJT+MOSFET

– Insulated gate → voltage control

– Holes injected from P+ emitter →

conductivity modulation

– High forward conduction current

density: 𝑖𝑐 = 𝑖𝑚𝑜𝑠 + 𝑖𝑝

• Slow removal of carriers in the

base → longer switching time

during turn-off and tail current

Page 38: EV Powertrain Simulations in Saber

© 2015 Synopsys, Inc. 38

Page 39: EV Powertrain Simulations in Saber

© 2015 Synopsys, Inc. 39

IKW75N65EL5

Static Characteristics

Page 40: EV Powertrain Simulations in Saber

© 2015 Synopsys, Inc. 40

Quasi-Static Characteristics

IKW75N65EL5

Page 41: EV Powertrain Simulations in Saber

© 2015 Synopsys, Inc. 41

IKW75N65EL5

Quasi-Static Characteristics

Page 42: EV Powertrain Simulations in Saber

© 2015 Synopsys, Inc. 42

Ic

Vcc

Inductive Clamp Test Circuit

Vcc

Rg(off)

Vg(on)

Vg(off)

Lp

DUT

(IGBT)

-15V

Ic

DUT

(Diode)

Rg(on)

Vg(on)

Page 43: EV Powertrain Simulations in Saber

© 2015 Synopsys, Inc. 43 1 1

1

1

2

2

2

2

3 3

3

3

4

4

4 4

5

5

5

5

𝐶𝑟𝑒𝑠 = 𝐶𝑔𝑐 𝐶𝑖𝑒𝑠 = 𝐶𝑔𝑐 + 𝐶𝑔𝑒

𝐶𝑜𝑒𝑠 = 𝐶𝑔𝑐 + 𝐶𝑐𝑒

Page 44: EV Powertrain Simulations in Saber

© 2015 Synopsys, Inc. 44

𝐶𝑟𝑒𝑠 = 𝐶𝑔𝑐 𝐶𝑖𝑒𝑠 = 𝐶𝑔𝑐 + 𝐶𝑔𝑒

𝐶𝑜𝑒𝑠 = 𝐶𝑔𝑐 + 𝐶𝑐𝑒

Cies = dQg / dVgs

Miller plateau Vgs

~1.2nF

~1.2nF

Page 45: EV Powertrain Simulations in Saber

© 2015 Synopsys, Inc. 45

IKW75N65EL5

Non Quasi-Static Characteristics

Page 46: EV Powertrain Simulations in Saber

© 2015 Synopsys, Inc. 46

IKW75N65EL5

Non Quasi-Static Characteristics

Page 47: EV Powertrain Simulations in Saber

© 2015 Synopsys, Inc. 47

IKW75N65EL5

Non Quasi-Static Characteristics

Page 48: EV Powertrain Simulations in Saber

© 2015 Synopsys, Inc. 48

IKW75N65EL5

Thermal Characteristics

Cauer network Foster network

Duty cycle zero

sufficient to match

the other curves Only physical if

connected to

temperature source