# Regenerative Intelligent Brake Control for Electric Motorcycles

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Electric Motor Model

_{m}is the angular speed of the motor, TR is the Throttle or Regeneration rate and f(·) is the adjustment function of the look-up table.

## 3. Estimation of Road Type and Vehicle Parameters

_{x}the longitudinal velocity of the vehicle.

_{k}and v

_{k}represent the noise of the model and the measures respectively, x

_{k}is the state vector and j

_{k}is the measurement vector. The state vector is:

^{®}(Ann Arbor, MI, USA) where the parameters necessary for the determination of the slip and the coefficient of adhesion are estimated. The test consists of the following sections: 40 m of high adhesion surface, 40 m of low adhesion surface and a third section with high adhesion again. The initial speed is 120 km/h. In these simulations the signal from the sensors has been altered with zero-mean random noise. A subscript “k” in the legend indicates that it is an estimated magnitude. The parameters estimated by means of the EKF fit quite accurately to the values provided by the simulation software. It can be verified that the speed (v

_{x}), front vertical force (F

_{zf}), rear vertical force (F

_{zr}), rear traction force (F

_{t}) and brake torque (T) are estimated correctly.

_{x}), measured by a high frequency Global Positioning System-based speed sensors and the measured brake torque. It can be seen that the speed is perfectly estimated. The brake torque is also correctly estimated except in the final part of the test, where a higher error is observed.

## 4. Regenerative Control

## 5. Simulations

^{®}was used for this purpose. Simulations were carried out on Simulink

^{®}(Natick, MA, USA), using BikeSim

^{®}as S-function. BikeSim

^{®}was incorporated to the model to simulate vehicle behaviour. The model has the outputs from the control system as inputs and provided the measures obtained in the vehicle. These simulations allow evaluating the potential and feasibility of the proposed regeneration algorithm. Table 3 shows the main characteristics of the vehicle used in the 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 (K_{m}) | 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 |

a_{x} | Longitudinal acceleration | I_{y} | Inertia on the Y axis |

a_{z} | Vertical Acceleration | N_{f} | Normal front force |

x | Longitudinal displacement | N_{r} | Normal rear force |

z | Vertical displacement | l_{f} | Front half length |

θ | Pitch angle | l_{r} | Rear half-length |

R_{r} | Rear tire radius | k_{f} | Stiffness of front suspension |

T | Torque applied on rear wheel | k_{r} | Stiffness of rear suspension |

C | Aerodynamic coefficient | c_{f} | Front Damping |

F_{xr} | Longitudinal rear wheel force | c_{r} | Rear damping |

ω_{r} | Rear wheel angular speed | L_{0} | Suspension length |

K_{m} | 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·m^{2} |

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 m^{2} |

Aerodynamic coefficient | 0.55 |

Motor-wheel transmission ratio | 1:6.4 |

Surface/Control | V_{o} (km/h) | V_{f} (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/s^{2}) | 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/s^{2}) | 4.27 | 4.44 | 4.38 |

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

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

**AMA Style**

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 Style**

Castillo 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