«Electric Superbike Regenerative Braking Study using EMR»

Transcription

1 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 felix-antoine.lebel@usherbrooke.ca

2 - Outline 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

3 EMR 17 University Lille 1 June 2017 Summer School EMR 17 Energetic Macroscopic Representation «SuperBike Context»

4 - emotoracing varsity challenge - 4

5 - emotoracing varsity challenge - 5

6 - Vehicle specifications - 6 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.

7 EMR 17 University Lille 1 June 2017 Summer School EMR 17 Energetic Macroscopic Representation «Modeling and control of the SuperBike»

8 - EMR and IBC of the SuperBike - 8 Drive + Motor Transmission Wheels Chassis + Inertia Bat v batt T m T gb T W F W v bike Road i batt W m W w W w v bike F bike EMR Tm_ref Brake T b W w Tb_ref Local control T gb_ref T w_ref F v_ref vbike_ref K b Global control Strategy

9 «Battery Pack» - EMR and IBC of the SuperBike - 9 Bat v Bat i Bat

10 «Battery Pack» - EMR and IBC of the SuperBike - 10 Bat v batt i batt

11 - EMR and IBC of the SuperBike - «Drive and Motor» Quasi-static model 11 Drive + Motor v batt T m i batt W m Tm_ref

12 - EMR and IBC of the SuperBike - 12 «Transmission» n = 21:35 reduction Transmission T m T gb W m W w Tm_ref T gb_ref

13 «Brake System» - EMR and IBC of the SuperBike - 13 T gb T W W w W w Brake T b W w Tb_ref T _ref K b T gb_ref K b K b

14 «Wheels» - EMR and IBC of the SuperBike - 14 Wheels T W F W W w v bike T w_ref F v_ref

15 - EMR and IBC of the SuperBike - «Chassis and Inertia» 15 Fres Chassis + Inertia F W v bike Fveh Fres v bike F bike F v_ref v Bike_ref

16 «Environment» - EMR and IBC of the SuperBike - 16 v bike Road F bike

17 «Strategy» - Energy Strategy - 17 T w_ref v batt K b Strategy i batt 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%

18 EMR 17 University Lille 1 June 2017 Summer School EMR 17 Energetic Macroscopic Representation «Racetrack Simulations»

19 - Speed-time generation - 19 Drive + Motor Transmission Wheels Chassis + Inertia v Bat T m T gb T W F W v bike Bat i Bat W ms W w W w v bike Road F bike EMR Tm_ref T b Brake W w Tb_ref Local control T gb_ref T w_ref F v_ref v Bike_ref K b Global control Strategy VBike_ ref(t)

20 - Speed-time generation Cornering speed 2. Fastest aceleration 3. Hardest braking

21 - Speed-time generation - Mont-Tremblant raceway (TSBK) 21 EMUS

22 - TSBK raceway results - 22

23 - Results and analysis - 23 Mont Tremblant New Jersey Highway

24 EMR 17 University Lille 1 June 2017 Summer School EMR 17 Energetic Macroscopic Representation «Conclusion»

25 - Conclusion - 25 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.

26 EMR 17 University Lille 1 June 2017 Summer School EMR 17 Energetic Macroscopic Representation «BIOGRAPHIES AND REFERENCES»

27 - Authors - 27 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

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