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Figure 6. Domain transformations required for the field-oriented controller (FOC) control using space vector modulation (SVM). This process is shown in Figure 6.

Figure 6 Domain transformations required for the field-oriented controller (FOC) control using space vector modulation (SVM). This process is shown in Figure 6.

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Figure 1. Mid-drive powertrain and hub-type powertrain. The location of assistance motors indicating where motors power is applied is shown in red. (a) Mid-drive propulsion—motor transfers power directly to the drivetrain; (b) front hub propulsion—motor is placed to spin the front tire; and (c) rear hub propulsion—motor is placed to spin the back tire. Figure 1. Mid-drive powertrain and hub-type powertrain. The location of assistance motors indicating
Figure 1. Mid-drive powertrain and hub-type powertrain. The location of assistance motors indicating where motors power is applied is shown in red. (a) Mid-drive propulsion—motor transfers power directly to the drivetrain; (b) front hub propulsion—motor is placed to spin the front tire; and (c) rear hub propulsion—motor is placed to spin the back tire. Figure 1. Mid-drive powertrain and hub-type powertrain. The location of assistance motors indicating
Figure 2. Methodology to quantify Bogota City bike users’ requirements. The proposed methodology consists of acquiring and analyzing quantitative data related to the riding behavior of different type of cyclists in Bogota City (Figure 2). The data are gathered trough a custom-made on-board computer (OBC) installed on each bicycle. The OBC records riding data for one week and then the information is downloaded and processed by software specifically developed for this project that returns the average values of ground velocity, pedaling frequency, cyclist power and travel time, the stops along each path, the geographical coordinates of the ride, and the voltage and current of the e-bike battery.
Figure 2. Methodology to quantify Bogota City bike users’ requirements. The proposed methodology consists of acquiring and analyzing quantitative data related to the riding behavior of different type of cyclists in Bogota City (Figure 2). The data are gathered trough a custom-made on-board computer (OBC) installed on each bicycle. The OBC records riding data for one week and then the information is downloaded and processed by software specifically developed for this project that returns the average values of ground velocity, pedaling frequency, cyclist power and travel time, the stops along each path, the geographical coordinates of the ride, and the voltage and current of the e-bike battery.
Figure 3. Data acquisition device on-board computer (OBC).
Figure 3. Data acquisition device on-board computer (OBC).
Figure 4. Developed software flowchart.
Figure 4. Developed software flowchart.
Table 1. Resistance power equations. The efficiency factor from the drivetrain to the ground (frictional and elasticity losses) is set to 0.975. As described in Equations (2)-(6) (see Table 1), the resistance power, Pres, is calculated as the sum of the powers related to: aerodynamic drag, P;i;; rolling resistance, P,o1); bearing resistance, Prearing; power due to changes in potential energy, Pp,; and, power due to changes in kinematic energy, Px.
Table 1. Resistance power equations. The efficiency factor from the drivetrain to the ground (frictional and elasticity losses) is set to 0.975. As described in Equations (2)-(6) (see Table 1), the resistance power, Pres, is calculated as the sum of the powers related to: aerodynamic drag, P;i;; rolling resistance, P,o1); bearing resistance, Prearing; power due to changes in potential energy, Pp,; and, power due to changes in kinematic energy, Px.
Table 2. Average parameter values. Va, is assumed to be 0 m/s, neglecting its average impact when rolling in urban areas. The wind velocity is assumed to be 0. The time-dependent variables used in Equations (2)-(6) (e.g., Ve(t), Or(t), a(t)) are calculated with a sample time of 1s from the captured data. my and r values, required in Equations (3), (5) and (6), depend on the current user and may vary between different users. The above parameters are presented in Table 3.
Table 2. Average parameter values. Va, is assumed to be 0 m/s, neglecting its average impact when rolling in urban areas. The wind velocity is assumed to be 0. The time-dependent variables used in Equations (2)-(6) (e.g., Ve(t), Or(t), a(t)) are calculated with a sample time of 1s from the captured data. my and r values, required in Equations (3), (5) and (6), depend on the current user and may vary between different users. The above parameters are presented in Table 3.
Table 3. Time-variable parameters. 3.3. BLDC Based Motor Propulsion System for an Electric Bicycle To provide a natural feeling to a cyclist when using a pedaling assistance system (PAS), a common strategy consists of performing an automatic adjustment of assistance level, proportional to the instantaneous power provided by the cyclist [9]. Hence, a method to obtain this cyclist power value must be incorporated in any design of a propulsion system for a bicycle.
Table 3. Time-variable parameters. 3.3. BLDC Based Motor Propulsion System for an Electric Bicycle To provide a natural feeling to a cyclist when using a pedaling assistance system (PAS), a common strategy consists of performing an automatic adjustment of assistance level, proportional to the instantaneous power provided by the cyclist [9]. Hence, a method to obtain this cyclist power value must be incorporated in any design of a propulsion system for a bicycle.
In general, cyclist power is proportional to the torque applied by the legs over the pedal levers. The shape of this torque waveform over time depends on factors like the type of pedals, cyclist behavior and road conditions.
In general, cyclist power is proportional to the torque applied by the legs over the pedal levers. The shape of this torque waveform over time depends on factors like the type of pedals, cyclist behavior and road conditions.
Therefore, Kp can be calculated to adjust the desired bandwidth in the frequency response (where Wo is the G(s) cutoff frequency): Figure 7. Current control loop and brushless-DC motor (BLDC) motor first order model. 3.3.3. Motor Control Implementation Details
Therefore, Kp can be calculated to adjust the desired bandwidth in the frequency response (where Wo is the G(s) cutoff frequency): Figure 7. Current control loop and brushless-DC motor (BLDC) motor first order model. 3.3.3. Motor Control Implementation Details
Figure 8. Implementation of the FOC algorithm. In red, analog input data. In green, digital signals with 32-bit resolution in floating point. In blue, pulse-width modulation (PWM) output signals. As shown in Figure 8, the controller receives two set points: Iq and Id. To implement the controller in the micro-processor, the controller digital constants (Kiz, Kpz) used in the FOC algorithm, are calculated. Ki, is simply determined by Equation (10):
Figure 8. Implementation of the FOC algorithm. In red, analog input data. In green, digital signals with 32-bit resolution in floating point. In blue, pulse-width modulation (PWM) output signals. As shown in Figure 8, the controller receives two set points: Iq and Id. To implement the controller in the micro-processor, the controller digital constants (Kiz, Kpz) used in the FOC algorithm, are calculated. Ki, is simply determined by Equation (10):
Figure 9. BiCiSOLAR charging station renders at Sergio Arboleda University. In 2017, the School of Exact Sciences and Engineering (ECEI) of the Sergio Arboleda University in Bogota, Colombia, placed the BiCiSOLAR Solar Charging station for electric bikes at the disposal of the university community, free of charge. This charging station is powered exclusively by solar energy, in a stand-alone configuration, and can charge up to five e-bikes simultaneously. The BiCiSOLAR charging station has several extra parking spots with a DC charger directly from the main grid reserved for experimental research purposes only. These extra parking spots are not available to the public. Figure 9 shows renders of the mechanical design of the BiCiSOLAR Solar Charging station. In broad terms, the station comprises (i) a control panel that regulates voltage from the panels and outputs a steady 120 VAC for charging bikes, (ii) four solar panels and (iii) five public charging spots for bikes. shows renders of the mechanical design of the BiCiSOLAR Solar Charging station. In broad terms,
Figure 9. BiCiSOLAR charging station renders at Sergio Arboleda University. In 2017, the School of Exact Sciences and Engineering (ECEI) of the Sergio Arboleda University in Bogota, Colombia, placed the BiCiSOLAR Solar Charging station for electric bikes at the disposal of the university community, free of charge. This charging station is powered exclusively by solar energy, in a stand-alone configuration, and can charge up to five e-bikes simultaneously. The BiCiSOLAR charging station has several extra parking spots with a DC charger directly from the main grid reserved for experimental research purposes only. These extra parking spots are not available to the public. Figure 9 shows renders of the mechanical design of the BiCiSOLAR Solar Charging station. In broad terms, the station comprises (i) a control panel that regulates voltage from the panels and outputs a steady 120 VAC for charging bikes, (ii) four solar panels and (iii) five public charging spots for bikes. shows renders of the mechanical design of the BiCiSOLAR Solar Charging station. In broad terms,
Figure 10. BiCiSOLAR charging station at Sergio Arboleda University.
Figure 10. BiCiSOLAR charging station at Sergio Arboleda University.
Figure 11. BlueSolar Charge Controller MPPT 150/35.
Figure 11. BlueSolar Charge Controller MPPT 150/35.
Figure 12. Filtered and non-filtered elevation profile vectors. 4.1.1. Cyclist Power Estimation
Figure 12. Filtered and non-filtered elevation profile vectors. 4.1.1. Cyclist Power Estimation
Figure 13. Power vector comparison and average power comparison. 4.1.2. E-Bike Battery Power Measurement
Figure 13. Power vector comparison and average power comparison. 4.1.2. E-Bike Battery Power Measurement
Figure 15. Acquired average velocity and power.
Figure 15. Acquired average velocity and power.
Figure 16. Test bench for the BLY342S-48V-3200 motor. Two different test benchmarks are used for the assessment of each of the motor torque controllers. The first platform is a stationary bench, which allows easy adjustment of the BLDC motor load. This bench is based on an induction motor directly coupled to the BLDC motor under test as shown in Figure 16. The load (Siemens 1LA7 070-4YC60 motor) is controlled using a Sinamics G120 inverter operating in vector mode to adjust the torque delivered to the BLDC motor.
Figure 16. Test bench for the BLY342S-48V-3200 motor. Two different test benchmarks are used for the assessment of each of the motor torque controllers. The first platform is a stationary bench, which allows easy adjustment of the BLDC motor load. This bench is based on an induction motor directly coupled to the BLDC motor under test as shown in Figure 16. The load (Siemens 1LA7 070-4YC60 motor) is controlled using a Sinamics G120 inverter operating in vector mode to adjust the torque delivered to the BLDC motor.
Table 4. Motor and controller parameters. 4.2.2. BLY3425-48V-3200 MOTOR
Table 4. Motor and controller parameters. 4.2.2. BLY3425-48V-3200 MOTOR
Figure 17. Test bench for the MTO 5065-170-HA-C motor. 4.2.1. Experimental Results The first step in the experimental validation is the computation of the controller constants. For the motor BLY342S-48V-3200 (Rs = 0.19693256 Q, Ls = 0.00052149 H), the results obtained are presented in Equations (13)-(16):
Figure 17. Test bench for the MTO 5065-170-HA-C motor. 4.2.1. Experimental Results The first step in the experimental validation is the computation of the controller constants. For the motor BLY342S-48V-3200 (Rs = 0.19693256 Q, Ls = 0.00052149 H), the results obtained are presented in Equations (13)-(16):
Figure 18. Transient response of the controller.
Figure 18. Transient response of the controller.
Figure 19. Steady state of the controller. 4.2.3. MTO 5065-170-HA-C MOTOR
Figure 19. Steady state of the controller. 4.2.3. MTO 5065-170-HA-C MOTOR
Figure 20. Motor current under control. Reference signal frequency: 1 Hz.
Figure 20. Motor current under control. Reference signal frequency: 1 Hz.
Figure 21. Motor current under control. Reference signal frequency: 10 Hz.
Figure 21. Motor current under control. Reference signal frequency: 10 Hz.
Figure 22. Motor current behavior after locking the motor by means of the brakes.
Figure 22. Motor current behavior after locking the motor by means of the brakes.
Figure 23. Motor current behavior after a sudden change in the pedaling frequency: 5 to 15 Hz
Figure 23. Motor current behavior after a sudden change in the pedaling frequency: 5 to 15 Hz
Figure 24. Motor current behavior after a sudden change in the pedaling frequency: 1 to 12 Hz
Figure 24. Motor current behavior after a sudden change in the pedaling frequency: 1 to 12 Hz
Figure 25. Motor current behavior after a step in the peak current reference from 18 A to 21 A. Pedaling frequency, 10 Hz.
Figure 25. Motor current behavior after a step in the peak current reference from 18 A to 21 A. Pedaling frequency, 10 Hz.
Table 5. Average settling time for different perturbations. 4.3. Charging Station for E-Bike Sharing Systems
Table 5. Average settling time for different perturbations. 4.3. Charging Station for E-Bike Sharing Systems
Figure 26. Motor current behavior after a step in the peak current reference from 22 A to 15 A. Pedaling frequency, 10 Hz.
Figure 26. Motor current behavior after a step in the peak current reference from 22 A to 15 A. Pedaling frequency, 10 Hz.
Figure 27. BiCiSOLAR charging station. East charging station (left) and west charging station (right). In Figures 27 and 28, the BiCiSolar charging stations available at Sergio Arboleda University are shown. Two stations were implemented so that ten e-bikes can be simultaneously charged. Each station is located in the university campus with the solar panels facing south at a fixed 6° angle to the horizontal.
Figure 27. BiCiSOLAR charging station. East charging station (left) and west charging station (right). In Figures 27 and 28, the BiCiSolar charging stations available at Sergio Arboleda University are shown. Two stations were implemented so that ten e-bikes can be simultaneously charged. Each station is located in the university campus with the solar panels facing south at a fixed 6° angle to the horizontal.
Figure 28. West BiCiSOLAR charging station. nonconventional maximum power-point detection techniques [44]. In Figure 29, the PSC phenomenon in the BiCiSOLAR charging stations is presented and in Figure 30 the P-V curve deformation due to such phenomena is depicted.
Figure 28. West BiCiSOLAR charging station. nonconventional maximum power-point detection techniques [44]. In Figure 29, the PSC phenomenon in the BiCiSOLAR charging stations is presented and in Figure 30 the P-V curve deformation due to such phenomena is depicted.
Figure 29. Partial shading phenomenon PSC in BiCiSOLAR charging station.
Figure 29. Partial shading phenomenon PSC in BiCiSOLAR charging station.
Figure 31. Maximum power generated by the PV array in the BiCiSOLAR charging station (top), battery voltage (center) and charging station consumption (bottom).
Figure 31. Maximum power generated by the PV array in the BiCiSOLAR charging station (top), battery voltage (center) and charging station consumption (bottom).
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