« electric superbike regenerative braking study using emr · 2017. 7. 20. · « electric...

EMR’17

University Lille 1

June 2017

Summer School EMR’17

“Energetic Macroscopic Representation”

« Electric Superbike Regenerative

Braking Study using EMR »

Félix-A. LEBEL, Pascal MESSIER, Louis PELLETIER, João P. TROVÃO

e-TESC Lab., Université de Sherbrooke, QC, Canada

[email protected]

mailto:[email protected]

EMR’17, University of Lille 1, June 20172

« Electric Superbike Regenerative Braking Study using EMR »

- Outline -

1. SuperBike Context

• eMotoRacing varsity challenge

• Vehicle specifications

2. Modeling and control of the SuperBike

• EMR

• Inversion-based Control

• Energy strategy

3. Racetrack Simulations

• Speed-time generation

• TSBK raceway results

• Results and analysis

4. Conclusion

EMR’17

University Lille 1

June 2017



« SuperBike Context »



- eMotoRacing varsity challenge -



- Vehicle specifications -

• Aerospace inspired hollow aluminum chassis designed to maximize

battery space;

• The motorcycle’s dry weight is 220kg and is powered by a 160kW

EMRAXR axial flux permanent magnet synchronous machine.

• Fixed (21:35) dual stage gear ratio.

• Rinehart Motion Systems PM150-DZ Drive is used to control the motor.

EMR’17

University Lille 1

June 2017



« Modeling and control of the

SuperBike »



- EMR and IBC of the SuperBike -

vbatt

ibatt

Bat

Tm_ref

Tgb

Ww

Transmission

Drive

+ Motor

EMR

Local

controlTgb_ref

Tm

StrategyGlobal

control

Tw_ref

vBike_ref

Fbike

Road

vbike

Chassis

+ Inertia

Tb

Ww

Ww vbike

Wheels

TW FW

Brake

Tb_ref

Fv_ref

Kb

Wm




vBat

iBat

Bat

« Battery Pack »




vbatt

ibatt

Bat

« Battery Pack »



Quasi-static model


vbatt

ibatt

Tm_ref

Drive + Motor

Tm

Wm

« Drive and Motor »



n = 21:35 reduction


Tm_ref

Transmission

Tgb_ref

Tgb

Ww

Tm

Wm

« Transmission »




Tgb_ref

T_ref

Tb

Ww

Brake

Tb_ref

Kb

Tgb

Ww Ww

TW

Kb

Kb

« Brake System »




Tw_ref

Ww vbike

Wheels

TW FW

Fv_ref

« Wheels »



Fveh

Fres


Fres

vBike_ref

Fbike

vbike

Chassis

+ Inertia

vbike

FW

Fv_ref

« Chassis and Inertia »




Fbike

Road

vbike

« Environment »



No Regenerative Braking

• Negative battery current is not

allowed

With Regenerative Braking

• Electric braking is used as

much as possible

• Mechanical braking (Front and

rear wheel)

• Regen. is disabled if Battery

SOC goes above 100%

- Energy Strategy -

vbatt

ibatt

Strategy

Tw_ref

Kb

« Strategy »

EMR’17

University Lille 1

June 2017



« Racetrack Simulations »



- Speed-time generation -

VBike_ ref(t)

Wms

vBat

iBat

Bat

Tm_ref

Tgb

Ww

Transmission

Drive

+ Motor

EMR

Local

controlTgb_ref

Tm

StrategyGlobal

control

Tw_ref

vBike_ref

Fbike

Road

vbike

Chassis

+ Inertia

Tb

Ww

Ww vbike

Wheels

TW FW

Brake

Tb_ref

Fv_ref

Kb




1. Cornering speed

2. Fastest aceleration

3. Hardest braking




Mont-Tremblant raceway (TSBK)

EMUS



- TSBK raceway results -



Mont Tremblant New Jersey Highway

- Results and analysis -

EMR’17

University Lille 1

June 2017



« Conclusion »



- Conclusion -

• The simulation results obtained with the dynamic model

using EMR show that regenerative braking could bring

improvements in range up to 14% on a race track.

• This can only be achieved with maximum use of motor

braking by the rider, and minimal use of the mechanical

brakes.

• The model also provided information on the maximum

current demand to the battery during normal operation.

• EMR made the modeling of the propulsion system more

structured than other representation methods.

• Future works include validation using actual racetrack

data.

EMR’17

University Lille 1

June 2017



« BIOGRAPHIES AND REFERENCES »



- Authors -

Pascal MESSIER

MSc Candidate in Electrical Engineering at University of Sherbrooke

Félix-Antoine LeBel

MSc in Electrical Engineering at University of Sherbrooke (2017), PhD Candidate.

Louis PELLETIER

MSc Candidate in Electrical Engineering at University of Sherbrooke

Research topics: Electric MotoBike, Batteries Design and Health, BMS, Optimization

Prof. João P. TROVÃO

PhD in Electrical Engineering at University of Coimbra (2012)

Research topics: Electric Vehicles, Multiple Energy Storages, Energy Management



- References -

[1] D. Montesinos-Miracle, C. Fontan-Tebar, and H. Vidal-Salvia, “Simulation of an electric

racing car using energetic macroscopic representation,” in 2014 IEEE Vehicle Power

and Propulsion Conference (VPPC), pp. 1–6, Oct 2014.

[2] L. Rodgers, “Designing an electric motorcycle for the isle of man tt zero race, and how

electric vehicle racing could be used to spur innovation,” in Proceedings for the Electric

Vehicle Symposium, 2012.

[4] A. Bouscayrol, B. Davat, B. de Fornel, B. François, J. P. Hautier, F. Meibody-Tabar, and M.

Pietrzak-David, “Multimachine multiconverter system: application for electromechanical

drives,” European Physics Journal - Applied Physics, vol. 10, no. 12, pp. 131–147,

2000.

[5] N. Faria, J. P. Trovao, A. F. Ramos, and P. G. Pereirinha, “Comparison of different battery

technologies for electric minibuses using energetic macroscopic representation,” in

2014 IEEE Vehicle Power and Propulsion Conference (VPPC), pp. 1–6, Oct 2014.

[6] N. Faria, P. G. Pereirinha, and J. P. Trovao, “Modelling of an urban electric minibus using

energetic macroscopic representation graphic description,” in 2015 IEEE Vehicle Power

and Propulsion Conference (VPPC), pp. 1–6, Oct 2015.

[8] J. P. Trovao, M. R. Dubois, M. A. Roux, E. Menard, and A. Desrochers, “Battery and

supercapacitor hybridization for a pure electric three-wheel roadster,” in 2015 IEEE

Vehicle Power and Propulsion Conference (VPPC), pp. 1–6, Oct 2015.

[9] V. Cossalter, Motorcycle Dynamics. Vittore Cossalter, 2006

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