Motor controllers

  1. BMSBattery S series
    1. BMSBattery S06S
      1. S06ST (torque sensor version)
      2. S06S-BL (Bluetooth version)
      3. PWM signals
        1. very low speed - 6 steps
        2. low speed up to max speed - sineware
      4. Phase B current signal
      5. Throttle
    2. BMSBattery S06SC
    3. BMSBattery S12S
      1. Programming header
      2. PWM signal at max speed - sineware
      3. Phase B and motor total current signals
    4. BMSBattery bottle battery controller
    5. LCD control panel
      1. LCD protocol
    6. Kunteng mobile app
    7. Bluetooh
      1. DIY Bluetooth module
    8. How to open the controller and solder the programming header
    9. Hardware mods
  2. Other controllers
    1. BMSBattery S06P
      1. various info
        1. 01
        2. 02
    2. Kunteng 18 mosfets motor controller
    3. Lishui motor controllers
      1. LSW-675
        1. Datasheets
        2. PWM signals
    4. JinHui motor controllers
  3. GreenEBikeKit


  1. BMSBattery Q75
  2. BMSBattery Q85
  3. BMSBattery Q100
  4. BMSBattery Q100C
  5. Tongsheng TSDZx mid drive motors

Development tools

  1. Linux
    1. Step-by-step tutorial development tools
      1. Hardware tools to flash and debug the firmware
      2. Tools to build the firmware
        1. stm8-binutils-gdb
      3. Tools to flash the firmware
        1. How to erase and unlock protected read memory
      4. (optional) Tools to debug the firmware
      5. (optional) Tools to debug using serial port
      6. (optional) Tools to see diffs on the source code
  2. Windows
  3. C library
  4. Other tools
    1. Battery voltage boost step-up converter

Motor control

  1. Torque speed
  2. Motor control scheme of S06S controller
  3. BLDC 6 steps
  4. PWM schemes
    1. So, Which PWM Technique is Best? (Part 1)
    2. So, Which PWM Technique is Best? (Part 2)
    3. So, Which PWM Technique is Best? (Part 3)
    4. So, Which PWM Technique is Best? (Part 4)
    5. So, Which PWM Technique is Best? (Part 5)
    6. So, Which PWM Technique is Best? (Part 6)
    7. So, Which PWM Technique is Best? (Part 7)
  5. PWM control and Dead Time Insertion
  6. Low inductance motors
  7. Throttle Control Modes
  8. Phase angle FOC
  9. PWM frequency VS motor eRPM
    1. Max motor speed using FOC
    2. Kelly controllers ultra high speed
  10. Sinusoidal Control of BLDCM with Hall Sensors Based
  11. Self-Learn Hall Sensor Calibration Mode
  12. STM8S105 Alternatives
  13. PID algorithm - negative output values
  14. Regeneration
    1. Regen in SimonK firmware
  15. FOC
    1. Shane Colton documentation and firmware
      1. Firmware
      2. Part 1: Field-Oriented
      3. Part 2: Field-Oriented
      4. Sensorless Pneu Scooter - part 1
      5. Sensorless Pneu Scooter - part 2
      6. Sensorless Pneu Scooter - part 3
      7. Observer
        1. Sensorless - 2011.04.10
        2. Sensorless - 2011.07.05

Datasheets and application notes

  1. STM8S105C6T6
    1. Interrupts
  2. forum messages
    1. 2017.04.25 - Initial forum message
    2. 2017.05.08 - First flash and debug on a dev board
    3. 2017.05.18 - First code flashing and running
    4. 2017.05.20 - more new information
    5. 2017.08.23 - SxxP versus SxxS versus LSW-675
    6. 2017.09.01 - Trying to figure out an algorithm to automatically adjust ui8_position_correction_value
    7. 2017.09.02 - How to do FOC on the BMSBattery S06S/Kunteng STM8 motor controllers
    8. 2017.09.03 - more ideas about zero crossing for FOC
    9. 2017.09.05 - measuring IQ current and manually adjusting position_correction_value
    10. 2017.09.15 - our OpenSource firmware efficiency compared to Lishui 12 FET FOC
    11. 2017.09.19 - measuring motor current
    12. 2017.10.23 - FOC and no FOC comparison
    13. 2018.01.10 - How to measure FOC_READ_ID_CURRENT_ANGLE_ADJUST
    14. 2018.02.20 - Reading motor phase current from the DC link current (shunt)

Torque sensors

  1. BMSBattery torque sensor


  1. STM8S003 board
  2. 2017.12.01 - Regen ebrake like coast brakes
  3. BEMF


  1. 2017.05.22 - Hackaday Links: May 21, 2017

Smart BMS with bluetooth

Sensorless Pneu Scooter - part 3

Friday, December 23, 2011

More Sensorless
I'm still tweaking the sensorless routine for

Pneu Scooter

. The software framework for sensorless sinusoidal commutation and field-oriented current control is set, and now I am playing with the different parameters to see if they have the effects I think they should.

First, I hypothesized in the

last post

that the positive relationship between current and flux estimator offset (with respect to the Hall effect sensors), seen


, was due to underestimated inductance. At high current, the rotor electrical angle predicted by the flux estimator would lead the angle interpolated from the Hall effect sensors, which I trust to be more accurate. Based on

this figure

, if the inductance parameter is too low, the [-IL] vector will be too short and the estimated flux vector will lead the actual flux vector at high current. 
To test this, I changed only the


 parameter in the sensorless code definitions, from 200μH to 300μH, leaving all other settings the same. After another 15-minute outdoor test drive, I plotted the flux estimator offset as a function of current once again. Here it is with the old data for comparison:

The effect is exactly what I thought it would be, flattening out the slope so that the offset is constant (near zero) though the entire range of load currents. I may have even gone a bit too far, creating a very slightly negative slope. But now I know that the inductance parameter has as predictable effect. The lead induced by using an inductance parameter that was 33% too low was about 20º electrical at maximum current. The error sensitivity is easy to predict using the vectors in this figure, but I will spare you the analysis in this post.
A closed-loop flux observer, which some day in the future I will certainly try out, would seek to minimize the effect of an over- or under-estimated inductance parameter. The ultimate goal would be plug-and-play sensorless that adapts to any motor on the fly. But for now, I can live with nicely-behaved dependencies like this. Though, without the parallel-processed Hall effect sensor data, I would be lost as to how to characterize any of it.
I also tried out the sensorless controller on a different motor:
The thing clamped to the table is Kitmotter, a demonstration motor with very similar characteristics to Pneu Scooter's wheel motor. They have virtually the same measured inductance. Kitmotter has a higher resistance, which I modified in the sensorless code definitions for this test. It also has a lower torque constant, which is not an explicit parameter in this sensorless algorithm. What it does mean, though, is that the flux magnitude should be lower:
And in fact it is, by a small amount. It's also interesting to see that the general form of the flux estimates is very similar for both motors. In particular, the gap between about 200º and 210º shows up in both cases. This eliminates the possibility that the gap is caused by some asymmetry in one motor's winding. It could be current sensor asymmetry, though.
Kitmotter also accidentally allowed me to test the fast overcurrent shutdown. The dangling mess of alligator clips seen above was not the best idea, and when two of the phase outputs shorted to each other, causing a large spark, the controller shut down properly and gave an overcurrent fault. No dead FET.
The next parameter I want to tweak is the low-pass filter time constant, which affects the low speed performance of the sensorless control. I mentioned that the low-pass filter causes the flux estimator to lead the true angle at low speeds, and showed the offset as a function of speed. Here it is again, but in pseudo-3D:

click me
Is this nauseating?

Below 200rpm, the flux estimator angle is greater than the Hall effect sensor angle (trusted to be the true electrical angle). This causes the data, which should lie on the x = y plane, to slope off to the left at low speeds. Making the low-pass filter time constant longer should flatten out the plane and improve the low-speed torque. A lower "valid data" speed for switching to sensorless control will make start-up ramping, when I get around to that, just a bit easier.
I'm also worried about high-speed operation, which was the problem that magically disappeared when I made several code improvements. But, I'm still working with relatively slow motors, having commutation frequencies of 200Hz or less. I'm interested to see how things change (probably for the worse) when I try to run a non-direct-drive (indirect-drive?) motor like that of tinyKart, which can hit 750Hz or more.
More testing to come... Posted by Shane Colton at 7:05 PM Labels: FOC, motor control, pneu, pneu scooter, sensorless