Regenerative Intelligent Brake Control for Electric Motorcycles
Abstract
:1. Introduction
2. Electric Motor Model
3. Estimation of Road Type and Vehicle Parameters
4. Regenerative Control
5. Simulations
5.1. Low Adhesion Condition Simulation
5.2. High to Low Adhesion Transition Simulation
5.3. Controls Comparison
5.4. Regenerative vs. Conventional Brake Comparison
6. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
Appendix A. Experimental Vehicle
Component | Parameter | Description |
---|---|---|
Vehicle | Motorcycle weight | 135 Kg |
Chassis Type | Steel Tubular | |
Height of gravity centre | 621 mm | |
Distance between axis | 1370 mm | |
Wheel radius | 300 mm | |
Distance from the COG to the front axle | 670 mm | |
Front tire | 95/70 R 17 | |
Rear tire | 115/70 R 17 | |
Electric motor | Brand | Heinzmann PMS 150 |
Type | Axial Flux Permanent Magnet | |
Maximum speed | 6000 rpm | |
Maximum torque | 80 Nm | |
Torque constant (Km) | 0.145 Nm/A | |
Maximum power | 34.1 KW (46.36 CV) | |
Battery | Battery Type | LiPo |
Cell layout | 26S5P | |
Total capacity | 4.8 KWh | |
Rated Voltage | 96 V | |
Maximum discharge current | 1250 A | |
Maximum load current | 300 A |
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Description | Description | ||
---|---|---|---|
M | Mass | J | Moment of inertia of the wheel |
ax | Longitudinal acceleration | Iy | Inertia on the Y axis |
az | Vertical Acceleration | Nf | Normal front force |
x | Longitudinal displacement | Nr | Normal rear force |
z | Vertical displacement | lf | Front half length |
θ | Pitch angle | lr | Rear half-length |
Rr | Rear tire radius | kf | Stiffness of front suspension |
T | Torque applied on rear wheel | kr | Stiffness of rear suspension |
C | Aerodynamic coefficient | cf | Front Damping |
Fxr | Longitudinal rear wheel force | cr | Rear damping |
ωr | Rear wheel angular speed | L0 | Suspension length |
Km | Torque-Intensity Ratio | I | Intensity |
Rule Number | Friction Coefficient | Slip | dμ/ds | RCI |
---|---|---|---|---|
1 | MPR | mid | --- | VSRF |
2 | MPR | high | --- | ZRF |
3 | MPR | zero | Lmud | MRF |
4 | MPR | zero | Mmud | LRF |
5 | MPR | zero | Hmud | LRF |
6 | PR | mid | --- | SRF |
7 | PR | high | --- | VSRF |
8 | PR | zero | Lmud | LRF |
9 | PR | zero | Mmud | LRF |
10 | PR | zero | Hmud | VLRF |
11 | RM | mid | --- | MRF |
12 | RM | high | --- | MRF |
13 | RM | zero | Lmud | LRF |
14 | RM | zero | Mmud | VLRF |
15 | RM | zero | Hmud | HRF |
16 | RN | mid | --- | VLRF |
17 | RN | high | --- | LRF |
18 | RN | zero | Lmud | VLRF |
19 | RN | zero | Mmud | HRF |
20 | RN | zero | Hmud | ERF |
21 | MUR | mid | --- | HRF |
22 | MUR | high | --- | ERF |
23 | MUR | zero | Lmud | HRF |
24 | MUR | zero | Mmud | ERF |
25 | MUR | zero | Hmud | ERF |
Description Value | |
---|---|
Total mass | 275 kg |
Wheel radius | 0.32 m |
Moment of inertia of the wheel | 0.484 kg·m2 |
Distance from the COG * to the front axle | 0.86 m |
Distance from the COG * to the rear axle | 0.67 m |
Height of gravity centre | 0.4 m |
Front area | 0.6 m2 |
Aerodynamic coefficient | 0.55 |
Motor-wheel transmission ratio | 1:6.4 |
Surface/Control | Vo (km/h) | Vf (km/h) | Energy (Wh) |
---|---|---|---|
High adhesion/Regen. cte. (10%) | 80 | 55.52 | 4.53 |
High adhesion/Controlled regeneration | 80 | 22.4 | 15.93 |
Medium adhesion/Regen. cte. (10%) | 80 | 55.54 | 4.53 |
Medium adhesion/Controlled regeneration | 80 | 36.5 | 11.37 |
Low adhesion/Regen. cte. (10%) | 80 | 55.54 | 4.53 |
Low adhesion/Controlled regeneration | 80 | 56.1 | 4.54 |
Surface | Control | ABS | Regenerative Reg. Max. = 50% | Regenerative Reg. Max. = 80% |
---|---|---|---|---|
High adhesion | Time (s) | 1.39 | 1.42 | 1.40 |
Distance (m) | 19.54 | 20.24 | 20.85 | |
Mean deceleration (m/s2) | 11.95 | 11.71 | 11.86 | |
Low adhesion | Time (s) | 3.90 | 3.76 | 3.80 |
Distance (m) | 53.51 | 52.19 | 52.62 | |
Mean deceleration (m/s2) | 4.27 | 4.44 | 4.38 |
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Share and Cite
Castillo Aguilar, J.J.; Pérez Fernández, J.; Velasco García, J.M.; Cabrera Carrillo, J.A. Regenerative Intelligent Brake Control for Electric Motorcycles. Energies 2017, 10, 1648. https://doi.org/10.3390/en10101648
Castillo Aguilar JJ, Pérez Fernández J, Velasco García JM, Cabrera Carrillo JA. Regenerative Intelligent Brake Control for Electric Motorcycles. Energies. 2017; 10(10):1648. https://doi.org/10.3390/en10101648
Chicago/Turabian StyleCastillo Aguilar, Juan Jesús, Javier Pérez Fernández, Juan María Velasco García, and Juan Antonio Cabrera Carrillo. 2017. "Regenerative Intelligent Brake Control for Electric Motorcycles" Energies 10, no. 10: 1648. https://doi.org/10.3390/en10101648